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• W2896654920 abstract "•The proteome of a photosynthetic bacterium was probed under light and CO2 limitation•Protein abundance changed linearly with growth rate according to growth law•The response to light limitation exceeds the response to CO2•A resource allocation model suggests that proteins are not always utilized optimally Cyanobacteria must balance separate demands for energy generation, carbon assimilation, and biomass synthesis. We used shotgun proteomics to investigate proteome allocation strategies in the model cyanobacterium Synechocystis sp. PCC 6803 as it adapted to light and inorganic carbon (Ci) limitation. When partitioning the proteome into seven functional sectors, we find that sector sizes change linearly with growth rate. The sector encompassing ribosomes is significantly smaller than in E. coli, which may explain the lower maximum growth rate in Synechocystis. Limitation of light dramatically affects multiple proteome sectors, whereas the effect of Ci limitation is weak. Carbon assimilation proteins respond more strongly to changes in light intensity than to Ci. A coarse-grained cell economy model generally explains proteome trends. However, deviations from model predictions suggest that the large proteome sectors for carbon and light assimilation are not optimally utilized under some growth conditions and may constrain the proteome space available to ribosomes. Cyanobacteria must balance separate demands for energy generation, carbon assimilation, and biomass synthesis. We used shotgun proteomics to investigate proteome allocation strategies in the model cyanobacterium Synechocystis sp. PCC 6803 as it adapted to light and inorganic carbon (Ci) limitation. When partitioning the proteome into seven functional sectors, we find that sector sizes change linearly with growth rate. The sector encompassing ribosomes is significantly smaller than in E. coli, which may explain the lower maximum growth rate in Synechocystis. Limitation of light dramatically affects multiple proteome sectors, whereas the effect of Ci limitation is weak. Carbon assimilation proteins respond more strongly to changes in light intensity than to Ci. A coarse-grained cell economy model generally explains proteome trends. However, deviations from model predictions suggest that the large proteome sectors for carbon and light assimilation are not optimally utilized under some growth conditions and may constrain the proteome space available to ribosomes. In a given environment, maximization of fitness requires microbial cells to regulate their proteome to balance the cost and benefit of proteins (Neidhardt and Magasanik, 1960Neidhardt F.C. Magasanik B. Studies on the role of ribonucleic acid in the growth of bacteria.Biochim. Biophys. Acta. 1960; 42: 99-116Crossref PubMed Scopus (120) Google Scholar). Because there is a finite amount of cellular resources and cytoplasmic space, an increase in one protein fraction will come at the expense of others. For example, the proteome of several microorganisms, including Escherichia coli and Saccharomyces cerevisiae, exhibits a linear increase in the ribosomal protein fraction with growth rate and, conversely, a linear decrease in the carbon assimilation fraction (Scott et al., 2010Scott M. Gunderson C.W. Mateescu E.M. Zhang Z. Hwa T. Interdependence of cell growth and gene expression: origins and consequences.Science. 2010; 330: 1099-1102Crossref PubMed Scopus (796) Google Scholar, Hui et al., 2015Hui S. Silverman J.M. Chen S.S. Erickson D.W. Basan M. Wang J. Hwa T. Williamson J.R. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.Mol. Syst. Biol. 2015; 11: 784Crossref PubMed Scopus (183) Google Scholar, Metzl-Raz et al., 2017Metzl-Raz E. Kafri M. Yaakov G. Soifer I. Gurvich Y. Barkai N. Principles of cellular resource allocation revealed by condition-dependent proteome profiling.eLife. 2017; 6: e28034Crossref PubMed Scopus (85) Google Scholar). However, the linear dependencies of translational and substrate assimilation proteins with growth rate are not universal (Lactococcus lactis; Goel et al., 2015Goel A. Eckhardt T.H. Puri P. de Jong A. Branco Dos Santos F. Giera M. Fusetti F. de Vos W.M. Kok J. Poolman B. et al.Protein costs do not explain evolution of metabolic strategies and regulation of ribosomal content: does protein investment explain an anaerobic bacterial Crabtree effect?.Mol. Microbiol. 2015; 97: 77-92Crossref PubMed Scopus (41) Google Scholar). The growth rate-dependent reallocation of protein resources has been termed a microbial “growth law.” To date, such microbial growth laws have been exclusively tested with chemoheterotrophic microbes growing on reduced carbon sources. Photoautotrophic organisms such as cyanobacteria are an interesting model to test the universality of microbial growth laws. Compared with heterotrophs, their separate carbon and energy uptake machineries presumably require more protein resources that must be subject to sophisticated balancing. Excess light uptake also poses the additional risk of an over-reduced metabolism (lack of re-generated nicotinamide adenine dinucleotide (phosphate) [NAD(P)+]) that eventually reduces growth (Holland et al., 2015Holland S.C. Kappell A.D. Burnap R.L. Redox changes accompanying inorganic carbon limitation in Synechocystis sp. PCC 6803.Biochim. Biophys. Acta. 2015; 1847: 355-363Crossref PubMed Scopus (24) Google Scholar). Furthermore, a fluctuating energy source (sunlight) gives rise to temporal gene regulation, such as expression of enzymes for glycogen synthesis and storage, which may constrain their ability to maximize the growth rate under any given condition (Piechura et al., 2017Piechura J.R. Amarnath K. O’Shea E.K. Natural changes in light interact with circadian regulation at promoters to control gene expression in cyanobacteria.eLife. 2017; 6: 1-33Crossref Scopus (15) Google Scholar, Reimers et al., 2017Reimers A.-M. Knoop H. Bockmayr A. Steuer R. Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth.Proc. Natl. Acad. Sci. USA. 2017; 114: 6457-6465Crossref Scopus (63) Google Scholar). It is unknown to date how cyanobacteria re-allocate proteome resources to optimize growth under substrate-limiting conditions. Numerous studies have probed the response of cells toward inorganic carbon (Ci) or light limitation (reviewed by Battchikova et al., 2015Battchikova N. Angeleri M. Aro E.M. Proteomic approaches in research of cyanobacterial photosynthesis.Photosynth. Res. 2015; 126: 47-70Crossref PubMed Scopus (12) Google Scholar, and Gao et al., 2015Gao L. Wang J. Ge H. Fang L. Zhang Y. Huang X. Wang Y. Toward the complete proteome of Synechocystis sp. PCC 6803.Photosynth. Res. 2015; 126: 203-219Crossref PubMed Scopus (25) Google Scholar). However, most of these tested the time-dependent response after a step change without aiming to reach a “steady state,” and none of these studies employed proteomics in the context of a “cellular economy.” We hypothesize that lower maximum growth rates achieved in phototrophic bacteria may be a consequence of higher resource investment in costly enzymes for light harvesting, Ci reduction, and alleviation of photo-induced stress. The maximum growth rate (μmax) of the fastest-growing cyanobacterium, Synechococcus UTEX2973, is approximately 0.40 hr−1 (Ungerer et al., 2018Ungerer J. Lin P.-C. Chen H.-Y. Pakrasi H.B. Adjustments to Photosystem Stoichiometry and Electron Transfer Proteins Are Key to the Remarkably Fast Growth of the Cyanobacterium Synechococcus elongatus UTEX 2973.MBio. 2018; 9 (e02327.e17)Crossref PubMed Scopus (68) Google Scholar), considerably less than E. coli. In this study, the cyanobacterium Synechocystis sp. PCC6803 was chosen to elucidate the mode and extent to which a photosynthetic cell allocates protein resources to meet the needs of different substrate availabilities. We cultivated cells under steady-state conditions and applied gradual changes of light intensity or CO2 concentration as a means to control the growth rate. Quantitative mass spectrometry (MS)-based shotgun proteomics was used to generate a detailed view of the proteome and estimate protein investment for major functional groups (sectors). We found that protein allocation between sectors followed a linear relationship with growth rate, even though not all proteins within sectors were correlated. Many, but not all, of the observed trends were explained by a protein economy model that predicted adjustment of enzyme abundance as a strategy to maximize biomass formation. To obtain reliable growth conditions and proteome profiles under different light and CO2 regimes, we used a multiplex turbidostat continuous cultivation system (details in STAR Methods). The turbidostat is ideal for cultivation of cyanobacteria because the incident light intensity per biomass is kept constant over time to prevent culture self-shading (Zavřel et al., 2015Zavřel T. Sinetova M.A. Búzová D. Literáková P. Červený J. Characterization of a model cyanobacterium Synechocystis sp: PCC 6803 autotrophic growth in a flat-panel photobioreactor.Eng. Life Sci. 2015; 15: 122-132Crossref Scopus (46) Google Scholar, Du et al., 2016Du W. Jongbloets J.A. Pineda Hernandez H. Bruggeman F.J. Hellingwerf K.J. Branco dos Santos F. Photonfluxostat: A method for light-limited batch cultivation of cyanobacteria at different, yet constant, growth rates.Algal Res. 2016; 20: 118-125Crossref Scopus (28) Google Scholar). To ensure a steady state, cells were cultivated under each condition for a minimum of five reactor volume retention times (RT = 1/μ) after the growth rate (μ) and the relative chlorophyll content (optical density 680 (OD680)/OD720 had stabilized) (see Figure S1 for example cultivations and Table S1 for a complete list of all performed cultivations). This corresponds to at least 7.2 doublings and took between 70 and 250 hr of cultivation depending on μ. Light-limited and CO2-limited regimes yielded specific growth rates from μ = 0.016 to μmax = 0.106 h−1, and growth rate generally increased in a Monod-like manner with each substrate (Figures 1A and 1B ; Table S1). We chose five light conditions (60, 100, 200, 300, and 1,000 μmol photons × m−2 × s−1) and five CO2 conditions (0.15%, 0.2%, 0.3%, 0.5%, and 1% v/v CO2) and harvested steady-state cells for proteomics analysis. Protein abundances under each of the 10 conditions were determined using a label-free quantification strategy and yielded a dataset with 1,979 proteins quantified in every replicate, 53.9% of the 3,672 genes annotated in the Cyanobase database (http://genome.microbedb.jp/cyanobase/Synechocystis). The coverage was at least 65% for 12 of 17 functional categories according to the Cyanobase pathway annotation (broadest category; Figure 1C). Proteome coverage was in the higher range compared with previous reports (average of 1,500 proteins based on 15 MS studies; Gao et al., 2015Gao L. Wang J. Ge H. Fang L. Zhang Y. Huang X. Wang Y. Toward the complete proteome of Synechocystis sp. PCC 6803.Photosynth. Res. 2015; 126: 203-219Crossref PubMed Scopus (25) Google Scholar). To estimate the energetic and spatial resources allocated to a particular protein under different environmental conditions, we determined the protein mass fraction α. For each protein in the proteome, α was calculated by dividing the MS1 ion intensity by the sum of all intensities of a sample (total proteome approach) (Hui et al., 2015Hui S. Silverman J.M. Chen S.S. Erickson D.W. Basan M. Wang J. Hwa T. Williamson J.R. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.Mol. Syst. Biol. 2015; 11: 784Crossref PubMed Scopus (183) Google Scholar, Basan et al., 2015Basan M. Hui S. Okano H. Zhang Z. Shen Y. Williamson J.R. Hwa T. Overflow metabolism in Escherichia coli results from efficient proteome allocation.Nature. 2015; 528: 99-104Crossref PubMed Scopus (350) Google Scholar, Peebo et al., 2015Peebo K. Valgepea K. Maser A. Nahku R. Adamberg K. Vilu R. Proteome reallocation in Escherichia coli with increasing specific growth rate.Mol. Biosyst. 2015; 11: 1184-1193Crossref PubMed Google Scholar). Ion intensity generally correlates with absolute protein abundance (Schmidt et al., 2016Schmidt A. Kochanowski K. Vedelaar S. Ahrné E. Volkmer B. Callipo L. Knoops K. Bauer M. Aebersold R. Heinemann M. The quantitative and condition-dependent Escherichia coli proteome.Nat. Biotechnol. 2016; 34: 104-110Crossref PubMed Scopus (409) Google Scholar), but determination of a particular protein abundance can be biased by the efficiency of purification, proteolytic digestion, the physico-chemical properties of the detected peptides, the MS instrumentation, and other factors. To increase the robustness of protein quantification, we included only the peptides of a proteolytic protein whose abundances correlated with each other according to the Diffacto algorithm (STAR Methods; Zhang et al., 2017Zhang B. Pirmoradian M. Zubarev R. Käll L. Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences.Mol. Cell. Proteomics. 2017; 16: 936-948Crossref PubMed Scopus (37) Google Scholar). When interpreting changes in proteome composition across different culture conditions, it is implied that the size of the proteome is constant. Several recent reports showed that the total cellular protein concentration in cyanobacteria is invariant across growth conditions (Du et al., 2016Du W. Jongbloets J.A. Pineda Hernandez H. Bruggeman F.J. Hellingwerf K.J. Branco dos Santos F. Photonfluxostat: A method for light-limited batch cultivation of cyanobacteria at different, yet constant, growth rates.Algal Res. 2016; 20: 118-125Crossref Scopus (28) Google Scholar, Touloupakis et al., 2015Touloupakis E. Cicchi B. Torzillo G. A bioenergetic assessment of photosynthetic growth of Synechocystis sp. PCC 6803 in continuous cultures.Biotechnol. Biofuels. 2015; 8: 133Crossref PubMed Scopus (39) Google Scholar, Zheng and O’Shea, 2017Zheng X.Y. O’Shea E.K. Cyanobacteria Maintain Constant Protein Concentration despite Genome Copy-Number Variation.Cell Rep. 2017; 19: 497-504Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar). To assess global trends in proteome mass fractions, we gathered proteins into seven functional sectors broadly describing cyanobacteria metabolism: LHC (light-harvesting complex), RIB (ribosome and protein production), PSET (photosystems and photosynthetic electron transport), CBM (Ci uptake, fixation, and metabolism), LPB (lipid biosynthesis and membrane components), GLM (glucose uptake and metabolism), and MAI (maintenance and regulation, including hypothetical proteins). The functional sectors were manually populated with genes based on annotations in Cyanobase (second-broadest category; Table S2). The mass fraction of each sector α was calculated as the sum of mass fraction of its component proteins. The proteome was relatively balanced among LHC (average 15% and 19.6% of proteome mass fraction for Ci and light limitation, respectively), RIB (16.9% and 16.5%), CBM (15% and 14.2%), and PSET (13.5% and 12.9%), although the protein number in each sector varied widely (Figure 2A). For example, the LHC sector contained only 57 proteins but constituted as much as 25% of proteome mass, whereas the MAI sector contained 1,272 proteins, including all hypothetical (472) and unknown proteins (266), but did not exceed an average proteome mass fraction of 33%. With the exception of MAI proteins, each sector was dominated by a few proteins with a high proteome mass fraction (Figure 2B). For example, the five most abundant proteins (by mass) for the LHC sector were ApcA, ApcB, CpcA, CpcB, and CpcC1, which are part of the allophycocyanin (core rod) and phycocyanin (radial rod) structures of the phycobilisome, respectively. For the translation sector (RIB), the dominant proteins were the chaperones GroEL-1, GroEL-2, and GroES, whereas actual ribosomal proteins were less abundant. Overall, we found that the Synechocystis proteome composition differed markedly under light and Ci limitation, even when the growth rate was similar. This is exemplified by “proteomaps” for one light and one Ci limitation condition (Figures 2C and 2D). Proteins that show large differences in mass fraction between these two conditions include the Rubisco subunits RbcS and RbcL, the ATP synthase subunits AtpA and AtpB, and the already mentioned chaperone and phycobilisome proteins. We next examined the behavior of proteome sectors with growth rate as controlled by light and CO2 (Figure 2A). A significant linear correlation between μ and proteome mass fraction α was found for several sectors (see p values in Figure 2A). For light-controlled growth, the LHC sector decreased linearly with μ, from 25% of proteome mass to 12%. Contrary to expectation, the size of the PSET sector, containing the photosystems, did not change, although the composition shifted toward a higher photosystem I to photosystem II (PSI/PSII) ratio. The carbon metabolism sector CBM also increased linearly with light, indicating that Ci fixation is co-regulated with the photon uptake rate to dissipate excess reducing power. The glucose uptake sector GLM and lipid metabolism sector LPB had small proteome fractions and did not change significantly with the growth rate. With CO2-controlled growth, the proteome changes were less severe. Contrary to expectation, the CBM sector was not dramatically affected but decreased slightly. The LHC sector expanded with the growth rate, presumably because of higher demand for photon uptake at high μ. Surprisingly, the PSET sector did not increase with CO2. Regardless of whether growth was controlled by light or CO2 supply, the protein synthesis sector RIB, composed of elongation factors, chaperones, and ribosomes, increased linearly with the growth rate. Ribosomal proteins ranged from 5% to 8% of the proteome (see Figure S2 for a detailed view of protein abundance within sectors). Each functional sector is composed of many proteins whose abundances may change by different magnitudes or even in opposite directions in response to light or CO2 (Figure 2B). We also grouped proteins with unsupervised, statistical clustering based on relative fold change between conditions, similar to Hui et al., 2015Hui S. Silverman J.M. Chen S.S. Erickson D.W. Basan M. Wang J. Hwa T. Williamson J.R. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.Mol. Syst. Biol. 2015; 11: 784Crossref PubMed Scopus (183) Google Scholar (Figures S3A and S3B). We found six clusters representing groups of genes that were either upregulated, downregulated, or did not change under the respective condition. However, a gene enrichment analysis of these clusters showed that no cluster contained a single, unambiguous biological function (Figure S3C). Therefore, unsupervised clustering identified proteins that show a similar pattern of regulation, but these clusters were not suitable for interpreting cellular expenses for a functional sector. The discrepancy between the ad hoc and statistically clustered sectors indicates that light and CO2 activate multiple metabolic processes that have significant overlap. The overlap could be due to different roles and, thus, regulation of enzymes within a metabolic pathway or incomplete gene annotation in cyanobacteria. Although clear functional annotation was not possible, we could use the clusters’ response to limitation to determine the constant and variable proteome fraction (Figures S3D and S3E). We found the constant proteome fraction (αconst) in Synechocystis to be considerably lower than in E. coli (41% versus 65%; Hui et al., 2015Hui S. Silverman J.M. Chen S.S. Erickson D.W. Basan M. Wang J. Hwa T. Williamson J.R. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.Mol. Syst. Biol. 2015; 11: 784Crossref PubMed Scopus (183) Google Scholar). The linear relationships between proteome sectors and light-dependent growth rate are reminiscent of trends predicted by the coarse-grain steady-state cellular economy model for microbial growth (Molenaar et al., 2009Molenaar D. van Berlo R. de Ridder D. Teusink B. Shifts in growth strategies reflect tradeoffs in cellular economics.Mol. Syst. Biol. 2009; 5: 323Crossref PubMed Scopus (381) Google Scholar). We thus adapted a variant of this model to interpret Synechocystis proteomics data (Burnap, 2015Burnap R.L. Systems and photosystems: cellular limits of autotrophic productivity in cyanobacteria.Front. Bioeng. Biotechnol. 2015; 3: 1Crossref PubMed Scopus (38) Google Scholar; Figure 3A; STAR Methods). The cellular economy model seeks the optimal proteome that will maximize the growth rate under a given substrate availability. The proteome is divided into functional sectors (“super-enzymes”), and the optimal size of these sectors reflects a balance between their cost of synthesis and their catalytic benefit to growth. The model constrains the total proteome (sum of all proteome sectors) to be 1 under all growth conditions. Similar models have been used to interpret the Crabtree effect in yeast and E. coli (Nilsson and Nielsen, 2016Nilsson A. Nielsen J. Metabolic Trade-offs in Yeast are Caused by F1F0-ATP synthase.Sci. Rep. 2016; 6: 22264Crossref PubMed Scopus (84) Google Scholar, Basan et al., 2015Basan M. Hui S. Okano H. Zhang Z. Shen Y. Williamson J.R. Hwa T. Overflow metabolism in Escherichia coli results from efficient proteome allocation.Nature. 2015; 528: 99-104Crossref PubMed Scopus (350) Google Scholar) as being a result of high protein costs for respiration enzymes. The model was first trained using proteomics data from light-limited cultivations. We performed iterative random sampling of the super-enzyme kinetic parameters until the predictions for μ and αpro matched the experimentally determined linear relationships (Figures 3B and 3D). The constrained model reproduced the near-linear correlation between proteome sector size and growth rate over a wide range of light intensities. Extremely low light provoked a parabolic increase in light harvesting sector size (LHC), but our experimental conditions did not cover this range to corroborate the finding. The predictive power of the model was then tested by simulating CO2 limitation. The simulated reduction in growth rate (Figure 3C) and reallocation of proteome sectors generally aligned with experimental data (Figure 3D). The strong coupling of translation sector size (RIB) with growth rate and the stagnation of LHC was accurately represented. However, model predictions for the carbon assimilation sector CBM were more extreme than what was measured; the CBM sector was relatively invariant to Ci. One reason is the unexpected expansion of the MAI sector with Ci limitation opposite to the trend under light limitation, which was mainly caused by upregulation of regulatory proteins (Figure S2D). The constrained model was then used to chart unsampled space of the protein landscape. By simulating all combinations of carbon and light availability, we obtained predictions for protein allocation that were not covered experimentally (Figure 4). Interestingly, the CBM and PSET sectors showed a response to Ci limitation that was dependent on light status. The CBM sector thus illustrates how optimized growth is a tradeoff between cost and benefit of an enzyme for a given condition. When light is moderate or high, the CBM sector shrinks slightly as Ci increases, but when light is low, it shrinks dramatically with increased Ci as the LHC sector is preferentially expanded to provide scarce reductant. Next we tested whether the protein economy model could accurately reproduce cyanobacterial proteome reallocation during environmental “perturbations.” Three conditions were chosen that were expected to substantially alter growth rate and protein allocation: mixotrophic growth with glucose, CO2, and light; partial inhibition of photosystems; and expression of a non-catalytic protein. Glucose consumption was simulated in the model by implementing a glucose metabolic enzyme (GLM) that consumes glucose and produces ATP and nicotinamide adenine dinucleotide phosphate (NADPH) (Figure 3A; Yoshikawa et al., 2013Yoshikawa K. Hirasawa T. Ogawa K. Hidaka Y. Nakajima T. Furusawa C. Shimizu H. Integrated transcriptomic and metabolomic analysis of the central metabolism of Synechocystis sp. PCC 6803 under different trophic conditions.Biotechnol. J. 2013; 8: 571-580Crossref PubMed Scopus (51) Google Scholar, Nakajima et al., 2014Nakajima T. Kajihata S. Yoshikawa K. Matsuda F. Furusawa C. Hirasawa T. Shimizu H. Integrated metabolic flux and omics analysis of Synechocystis sp. PCC 6803 under mixotrophic and photoheterotrophic conditions.Plant Cell Physiol. 2014; 55: 1605-1612Crossref PubMed Scopus (69) Google Scholar). Photosystem inhibition was modeled by lowering kcat of PSET to a fraction of its original value (10%). The protein burden was implemented in the model by limiting the sum of protein mass fractions α (the protein “pool”) to a fraction of the original value (0.9 instead of 1). Experimentally, mixotrophic growth was achieved by addition of 1 g/L glucose, in large excess relative to biomass (0.08 g dry cell weight/L [gDCW/L]) (Nakajima et al., 2014Nakajima T. Kajihata S. Yoshikawa K. Matsuda F. Furusawa C. Hirasawa T. Shimizu H. Integrated metabolic flux and omics analysis of Synechocystis sp. PCC 6803 under mixotrophic and photoheterotrophic conditions.Plant Cell Physiol. 2014; 55: 1605-1612Crossref PubMed Scopus (69) Google Scholar, Fang et al., 2017Fang L. Ge H. Huang X. Liu Y. Lu M. Wang J. Chen W. Xu W. Wang Y. Trophic Mode-Dependent Proteomic Analysis Reveals Functional Significance of Light-Independent Chlorophyll Synthesis in Synechocystis sp. PCC 6803.Mol. Plant. 2017; 10: 73-85Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar). Partial inhibition of photosystem II was achieved by addition of 0.2 μM 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) (Chiu et al., 2009Chiu Y.F. Lin W.C. Wu C.M. Chen Y.H. Hung C.H. Ke S.C. Chu H.A. Identification and characterization of a cytochrome b559 Synechocystis 6803 mutant spontaneously generated from DCMU-inhibited photoheterotrophical growth conditions.Biochim. Biophys. Acta. 2009; 1787: 1179-1188Crossref PubMed Scopus (15) Google Scholar). The protein burden was imposed by expressing recombinant enhanced yellow fluorescent protein (eYFP) from a plasmid. Turbidostat cultivations and proteomics sampling were carried out for each perturbation at low and high light (60 and 300 μmol photons × m−2 × s−1, respectively). In all cases, perturbations had an effect on growth rate that coincided with model simulations (Figure 5A). With glucose addition, μ was predicted to improve primarily under low light, where ATP and NADPH generated from glucose were significant compared with those supplied by the light reactions. Experimentally, glucose addition significantly increased average μ at low light (+35%) and only slightly at high light (+9%; Table S1). DCMU addition reduced the growth rate under both light conditions (−59% and −51%), as predicted. A simulated protein burden of 10% reduced the growth rate by 9% under both light conditions. Experimentally, expression of eYFP (and four other plasmid-encoded proteins) accounted for approximately 8.8% of the proteome (for details, see STAR Methods and Figures S4A–S4C), which reduced the growth rate by 20% under both light conditions. The model also reproduced the overall changes in proteome mass fraction α for the different functional sectors (917 proteins) in response to the various perturbations (Figure 5B). With glucose addition, the size of the translation sector RIB increased and that of the photosystem sector PSET decreased, but the effect was weak at high light. From the perspective of a protein economy, the total cost for PSET and LHC is higher and the growth benefit is lower at low light so that glucose consumption becomes more favorable than at high light. Reduction of the photosynthesis machinery at low light and glucose (also visible by pigment absorption; Figures S4D–S4F) allows expansion of the translation sector (RIB) and, consequently, a stronger increase in μ compared with glucose addition under high light (Figure 5A). The addition of DCMU provoked a compensatory increase in the mass fraction of photosynthesis-related sectors (LHC and PSET) and, consequently, a reduction in RIB. The protein burden from expression of eYFP led to a slight reduction in all protein sectors compared with the control. However, some experimental trends were not accurately reproduced by model simulations. For example, the carbon assimilation sector CBM was nearly invariant for all perturbations, despite their effect on growth rate and other proteome sectors. This suggests underutilization of CBM proteins and, therefore, suboptimal protein allocation. Proteins constitute the largest mass fraction of all cellular components (Touloupakis et al., 2015Touloupakis E. Cicchi B. Torzillo G. A bioenergetic assessment of photosynthetic growth of Synechocystis sp. PCC 6803 in continuous cultures.Biotechnol. Biofuels. 2015; 8: 133Crossref PubMed Scopus (39) Google Scholar), whereas the intracellular protein concentration is constant and the protein pool limited (Zheng and O’Shea, 2017Zheng X.Y. O’Shea E.K. Cyanobacteria Maintain Constant Protein Concentration despite Genome Copy-Number Variation.Cell Rep. 2017; 19: 497-504Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar). In this work, we investigated how the model photoautotrophic organism Synechocystis sp. PCC6803, with its separate routes of carbon and energy assimilation, balances the cost and catalytic benefits of" @default.
• W2896654920 created "2018-10-26" @default.
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• W2896654920 date "2018-10-01" @default.
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• W2896654920 title "Growth of Cyanobacteria Is Constrained by the Abundance of Light and Carbon Assimilation Proteins" @default.
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