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Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis

2009; Springer Nature; Volume: 5; Issue: 1 Linguagem: Inglês

10.1038/msb.2009.68

1744-4292

Maria Piques, Waltraud X. Schulze, Melanie Höhne, Björn Usadel, Yves Gibon, Johann M. Rohwer, Mark Stitt,

Plant Gene Expression Analysis

Article13 October 2009Open Access Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis Maria Piques Maria Piques Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Waltraud X Schulze Waltraud X Schulze Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Melanie Höhne Melanie Höhne Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Björn Usadel Björn Usadel Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Yves Gibon Yves Gibon Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, GermanyPresent address: INRA Bordeaux, University of Bordeaux 1&2, UMR619 Fruit Biology, F-33883 Villenave d'Ornon, France Search for more papers by this author Johann Rohwer Johann Rohwer Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Permanent address: Triple-J Group for Molecular Cell Physiology, Department of Biochemistry, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa Search for more papers by this author Mark Stitt Corresponding Author Mark Stitt Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Maria Piques Maria Piques Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Waltraud X Schulze Waltraud X Schulze Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Melanie Höhne Melanie Höhne Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Björn Usadel Björn Usadel Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Yves Gibon Yves Gibon Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, GermanyPresent address: INRA Bordeaux, University of Bordeaux 1&2, UMR619 Fruit Biology, F-33883 Villenave d'Ornon, France Search for more papers by this author Johann Rohwer Johann Rohwer Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Permanent address: Triple-J Group for Molecular Cell Physiology, Department of Biochemistry, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa Search for more papers by this author Mark Stitt Corresponding Author Mark Stitt Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany Search for more papers by this author Author Information Maria Piques1, Waltraud X Schulze1, Melanie Höhne1, Björn Usadel1, Yves Gibon1, Johann Rohwer1 and Mark Stitt 1 1Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany *Corresponding author. Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm 14474, Germany. Tel.: +49 331 5678100; Fax: +49 331 5678101; E-mail: [email protected] Molecular Systems Biology (2009)5:314https://doi.org/10.1038/msb.2009.68 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Plants are exposed to continual changes in the environment. The daily alternation between light and darkness results in massive recurring changes in the carbon budget, and leads to widespread changes in transcript levels. These diurnal changes are superimposed on slower changes in the environment. Quantitative molecular information about the numbers of ribosomes, of transcripts for 35 enzymes in central metabolism and their loading into polysomes is used to estimate translation rates in Arabidopsis rosettes, and explore the consequences for important sub-processes in plant growth. Translation rates for individual enzyme are compared with their abundance in the rosette to predict which enzymes are subject to rapid turnover every day, and which are synthesized at rates that would allow only slow adjustments to sustained changes of the environment, or resemble those needed to support the observed rate of growth. Global translation rates are used to estimate the energy costs of protein synthesis and relate them to the plant carbon budget, in particular the rates of starch degradation and respiration at night. Synopsis In the following article, we quantify ribosome and transcript concentrations, and polysome composition in Arabidopsis rosettes. These data are used to predict translation rates, globally, and for individual enzymes in central metabolism. We then explore the consequences of these molecular events for growth. First, we compare the rates of synthesis with protein abundance to predict which enzymes are likely to be subject to rapid turnover and which are synthesized at rates similar to those required for growth. Second, we estimate the costs of protein synthesis and relate them to the whole plant carbon budget. Plants experience continual changes in the environment. One of the most striking is the daily alternation between light and darkness. This leads to a repeated alternation between two states; a large positive balance of energy and carbon in the light period, and a deficit in the dark period. It is buffered by storing some newly fixed C as starch, and remobilizing it during the night (Smith and Stitt, 2007). We do not know how energetically expensive processes like protein synthesis are coordinated with these dramatic diurnal changes in the energy budget. Plants also experience changes in the environment over a time range of days or weeks due to changing weather patterns and seasons, raising the question how they integrate their response over a wide range of time spans. Although there are large diurnal changes for thousands of transcripts (Usadel et al, 2008), most enzymes show only small diurnal changes in their maximum activity, and require days to adjust when plants are transferred to new conditions. Gibon et al (2004b) proposed that translation is so slow that several days are required to produce a major change in protein abundance. This would buffer enzymatic capacities against recurring changes caused by the light–dark cycle, while allowing them to adjust to sustained changes in the surroundings. To test this hypothesis, it was necessary to obtain information about rates of protein synthesis. Protein synthesis occurs by the recruitment of transcripts to ribosomes, to form polysomes. The rate of translation of a given transcript species depends upon transcript abundance, the proportion present in polysomes, the number of ribosomes present on the transcript, and their speed of progression along the transcript. Real time RT–PCR combined with spiked external RNA standards was used to quantify the abundance of cytosolic, plastidic and mitochondrial ribosomal RNA and 98 transcripts, including 84 for enzymes involved in central metabolism. This was done in whole rosettes, and in fractions from polysome density gradients. The estimated rates of synthesis of proteins varied by >1000-fold. The measurements were carried out in the dark and the light periods. Ribosomal and transcript loading into polysomes and the estimated rates of synthesis of most proteins decreased by up to twofold in the dark. Protein abundance was estimated in two ways; from maximum enzyme activities, corrected by literature values for specific activity, and from MS analyses using the emPAI index. Comparison of the resulting estimates for protein abundance revealed a highly significant agreement (Pearson's R2=0.590). The estimated rates of protein synthesis were compared with protein abundance to calculate how many days it would require to synthesize all the protein in an Arabidopsis rosette (TP, this term is used instead of T0.5, because part of the newly synthesized protein represents the flux to growth). For some enzymes like nitrate reductase and ADP glucose pyrophosphorylase, the estimated rate of synthesis was high compared with their abundance. These enzymes also show marked changes of their maximum activity during diurnal cycles. However, estimated rates of synthesis of most enzymes were low compared with the amount present in the leaf, resulting in a TP of four or more days. This resembles that needed for growth. An independent estimate of the global rate of protein synthesis was obtained using information about the ribosome concentration (about 0.1 nmol ribosomes g−1 FW). This would support a maximum rate of protein synthesis of approximately 3 mg protein g−1 FW day−1, allowing synthesis of all proteins in the rosette (15 mg g−1 FW) in about 5 days, resembling the rate required for the observed rate of growth. There are considerable energetic costs associated with protein synthesis; conversion of one molecule of nitrate to amino acids requires 5 ATP molecules, and addition of an amino acid to a growing peptide chain requires >5 ATP molecules. ATP is supplied by photophosphorylation in the light period, and by respiration in the dark period. The latter is thermodynamically less efficient, and requires transient storage of large amounts of starch. When Arabidopsis is grown in a 12-h light–dark cycle, the rosette contains about 40 μmol glucose g−1 FW as starch at the end of the light period, and has a rate of respiration of about 8 μmol CO2 g−1 FW h−1 in the dark period. If protein synthesis continued at maximal rates in the dark period, about 65 μmol hexose g−1 FW would be required each night to supply amino acids, and another 7 μmol hexose g−1 FW for respiration to deliver ATP. Thus, insufficient starch is stored to support high rates of protein synthesis during the night. A deficit is avoided in two ways. First, up to 30 μmol amino acids g−1 FW is accumulated in the light period for use at night (Gibon et al, 2009). Second, (see above) protein synthesis is about twofold slower in the dark period than in the light period. These diurnal adjustments abolish the nocturnal requirement for carbon for amino acid synthesis, and decrease the respiratory cost by about 75%. In conclusion, quantitative measurements of transcripts, ribosomes, polysomes and protein levels provide information that can be used to estimate protein synthesis and turnover rates. This allows proteins to be identified that are subject to rapid turnover, and provides an integrative framework in which the metabolic and energetic costs of protein synthesis and turnover can be related to central molecular components of cellular growth. Introduction Plant growth is driven by photosynthetic assimilation of carbon (C). Nutrients like nitrate are absorbed by the roots and converted to amino acids in the leaves using light energy, or imported C in the roots. Sucrose and amino acids are transported to the shoot and root apex to support growth of more leaves and roots. Plants are unavoidably exposed to changes in the environment. One of the most striking changes is the daily alternation of light and darkness, which leads to an extreme and repeated alternation between two states, occurs every day in the natural environment, and can be precisely simulated in laboratory experiments. It results in a large positive balance of energy and C in the light period, and a deficit in the dark period. This is buffered by storing some of the newly fixed C as starch, and remobilizing it to support metabolism and growth during night (Geiger et al, 2000; Smith and Stitt, 2007). However, we do not know how energetically expensive processes like protein synthesis are regulated during these marked diurnal changes in the plant's energy budget. Plants are also exposed to slower changes that occur in a time range of days or weeks, as a result of changing weather patterns and seasonal changes. A very large portion of the total leaf protein is invested in a single metabolic process. Owing to its very low rate of catalysis (Kcat=3 s−1), RubisCO represents over 30% of the total protein in a leaf (Farquhar et al, 2001; Zhu et al, 2007). Large amounts of protein are also invested in the synthesis of chlorophyll-binding proteins and other enzymes involved in the Calvin cycle and pathways for carbohydrate and amino acid synthesis. This raises the question about how plants integrate their response to the environment over a wide range of time spans to generate appropriate levels of proteins for photosynthesis and growth. Thousands of genes undergo diurnal changes of their transcript level (Bläsing et al, 2005) driven by the circadian clock, light and sugars (Usadel et al, 2008). This includes many transcripts that encode enzymes in central metabolism. Gibon et al (2004b) developed a robotized platform to profile the maximum activities of over 20 enzymes, most of which show rather small diurnal changes (Smith et al, 2004; Gibon et al, 2004b). Although transcripts respond within hours, changes of enzyme abundances require days to adjust when plants are transferred to continuous darkness (Gibon et al, 2004b, 2006). Gibon et al (2004b) hypothesized that the rate of translation is so slow that several days are required to produce a major change in protein abundance. As a result, the rapid transient changes of transcripts would be integrated over a longer period of time to set the levels of enzymes and other proteins. This would buffer the enzymatic capacities in central metabolism against recurring changes caused by the light–dark cycle, while allowing them to adjust to sustained changes in the surroundings. Protein synthesis occurs by the recruitment of transcripts to ribosomes, to form polysomes. Its synthesis represents a major portion of the total ATP consumption in animal and plant cells (Hachiya et al, 2007; Pace and Manahan, 2007; Proud, 2007). Energy is also required for the synthesis of amino acids. The conversion of nitrate to amino acids requires about five ATP molecules per amino acid (Penning de Vries, 1975; Hachiya et al, 2007). The synthesis of ribosomes requires energy, and diverts resources from other cellular components. Eukaryotic ribosomes typically contain one molecule of each of the four different ribosomal RNA (rRNA) species and one molecule of ca. 80 different ribosomal proteins (Perry, 2007). Ribosomal RNA and proteins represent >80 and 30–50% of the total RNA and protein, respectively, in a growing yeast cell (Warner, 1999; Perry, 2007). There is selective pressure to achieve a parsimonious use of the translational machinery (Beilharz and Preiss, 2007; Lackner et al, 2007). In budding yeast, up to 85% of the ribosomes are present in the polysomes. The ribosome density in polysomes is about one-fifth of the theoretical maximum (Arava et al, 2003; MacKay et al, 2004), which is consistent with the view that translation is generally regulated by the rate of initiation. A twofold decrease of transcript levels for some ribosomal proteins leads to a ribosome deficit and a minute growth reduction phenotype in Drosophila, indicating that the overall ribosomal number limits protein synthesis and growth (Perry, 2007). The rate of translation of a given transcript species depends on transcript abundance, the proportion present in polysomes (often termed ‘ribosome occupancy’), the number of ribosomes present on the transcript (often termed ‘ribosome density’), and their speed of progression along the transcript (Arava et al, 2003; Beilharz and Preiss, 2004; Beyer et al, 2004; Brockmann et al, 2007). On average, about 70% of a given transcript species is occupied by ribosomes. Similar percentages of ribosomes (60%) and transcripts (59–82%) are loaded into polysomes in plants (Kawaguchi et al, 2004; Kawaguchi and Bailey-Serres, 2005). In the following article, we describe the methods that allow quantitative analysis of ribosome and transcript concentrations, and polysome composition in Arabidopsis rosettes. These data allow us to predict translation rates, both globally, and for individual enzymes in central metabolism. We explore the consequences of these molecular events for important sub-processes in plant growth. First, we compare the rates of synthesis with the protein abundance to predict which enzymes are likely to be subject to rapid turnover. Second, we use these molecular data to estimate the costs of protein synthesis and relate to the C budget, in particular the rates of starch degradation and respiration at night. Results Experimental strategy Figure 1 outlines our experimental strategy. Ribosome copy numbers are measured using quantitative real time RT–PCR (qRT–PCR) for rRNA, and polysome fractionation was used to estimate the proportion actively involved in translation. This information is used to estimate the overall rate of protein synthesis. qRT–PCR is combined with polysome fractionation to estimate the copy number of transcripts in polysomes, including 84 that encode enzymes involved in central plant metabolism. This information is used to estimate the rates of translation of individual transcripts. In parallel, quantitative proteomics and robotized measurements of maximum enzyme activities are used to provide two independent estimates of the amounts of these enzymes in the leaves. By comparing the estimated rates of synthesis with the measured abundance of total and individual proteins, and the rate of growth, it is possible to predict whether the global rate of protein synthesis and the rates of synthesis of individual proteins are of the same order as that requited for growth, or whether proteins are subject to rapid turnover. Figure 1.Quantitative analysis of translation in Arabidopsis rosette leaves. Download figure Download PowerPoint Polysome fractionation In preliminary experiments, we collected whole rosettes from 5-week-old wild-type Arabidopsis growing in a 12-h light–dark cycle at 6 times during the diurnal cycle, fractionated the material by centrifugation, and collected three fractions: the non-polysomal fraction (NPS) at the top of the gradient, the small polysomal fraction (SPS) with an estimated 2–4 ribosomes per polysome, and the large polysomal fraction (LPS) with an estimated ⩾5 ribosomes per polysome. The distribution of total RNA in polysome gradients was monitored by measuring A254 (Supplementary Figure 1). The proportion in the LPS decreased by about twofold during the night, and recovered within 2 h in the next light period (Supplementary Figure 2). In subsequent experiments, plants were collected at the end of the night (‘dark’) period and after 2 h of illumination (‘light’), corresponding to the largest changes in polysome abundance. Quantification of ribosomes A254 does not distinguish between transcript RNA and rRNA, and does not distinguish between cytosolic, plastid and mitochondrial rRNA. Two complementary approaches were taken to quantify cytosolic and plastid ribosomes. The first approach used qRT–PCR to determine the concentrations of 18S, 16S and 18S-like rRNAs, corresponding to the rRNA in the small subunit of the cytosolic, plastid and mitochondrial ribosomes, respectively. To allow precise quantification, the qRT–PCR data were normalized on four artificial control RNAs, which were added at a known concentration before purification of the rRNA from the gradient fractions. The total estimated concentration of ribosomes is about 0.10 nmol g−1 fresh weight (FW) (see, Table I and Calculations and assumptions section). Cytosolic ribosomes were threefold more abundant than plastid ribosomes, and 30-fold more abundant than mitochondrial ribosomes. This is in agreement with earlier reports that 26–36% of the total ribosomes in leaves are present in plastids (Dyer et al, 1971). The distribution of different rRNA species in the polysome gradient suggests that about twofold more ribosomes were present in the polysomes in the light period than in the dark period (Figure 2A and C). The proportion of cytosolic rRNA in the LPS fraction increased from 26 to 50%, whereas the proportion in SPS decreased slightly (from 27 to 20%), as expected in the case if the polysome population is shifting towards a higher number of ribosomes per transcript, and free cytosolic ribosomes decreased from 47 to 30%. A qualitatively similar picture was found for plastid rRNA, except that a smaller proportion was found in polysomes (Figure 2B). This conclusion is supported by the distribution of ribosomal proteins (Figure 2D). The distribution of mitochondrial rRNA was not investigated. Figure 2.Distribution of ribosomes in different polysomal fractions in the dark and the light periods. (A, B) Ribosome number in each fraction was calculated by determining the amount of the SSU rRNA for cytosolic and plastid ribosomes by qRT–PCR, assuming each rRNA copy corresponds to one ribosome. (C, D) Ribosomal protein abundance in each fraction, calculated by normalizing the summed emPAI values for ribosomal proteins on total protein in the fraction. Download figure Download PowerPoint Table 1. Ribosome content in the cytosol, plastids and mitochondria Ribosome content (mol g−1 FW) Dark period Light period Cytosol 7.62E−11±1.56E−11 7.27E−11±1.79E−11 Plastid 2.64E−11±6.66E−12 2.57E−11±7.42E−12 Mitochondrion 2.25E−12±4.42E−13 2.21E−12±3.96E−13 The results are represented as mean±s.d. of three biological samples. Estimation was done by qRT–PCR of SSU rRNA subunits of the cytosolic, plastidic and mitochondrial ribosomes. The second approach used relative quantitative proteomics based on emPAI values (Ishihama et al, 2005) to estimate the abundance of ribosomal proteins in the polysome gradient fractions (Figure 2C and D). Briefly, the number of identified peptides per protein are corrected for the number of possible peptides from that protein, and taken as quantitative measure of the protein abundance (see Calculations and assumptions section). This approach confirmed that about twofold more ribosomes are present in polysomes in the light period than in the dark period, and that a larger proportion of cytosolic ribosomes than plastid ribosomes are present in polysomes. The measurements of protein abundance indicate a higher proportion of ribosomes in polysomes than the measurements of rRNA for cytosolic ribosomes. This might be for technical reasons. The NPS fraction is a complex matrix with a high proportion of proteins with other biological functions and emPAI may underestimate ribosomal proteins, especially in the light period, when they are strongly depleted in NPS. Taken together, both approaches reveal marked changes in ribosomal loading into polysomes between the dark period and the light period, with 53–58% of the cytosolic ribosomes being loaded in the dark period, and 70–90% in the light period. The values in the light are similar to those in rapidly growing yeast (see Introduction). Global rates of protein synthesis compared with growth requirements Information about the numbers of ribosomes and the proportion found in polysomes was used to estimate the overall rate of protein synthesis. The summed concentration of cytosolic, plastid and mitochondrial ribosomes is about 0.1 nmol g−1 FW (see Table I). Assuming that a ribosome adds 3 amino acids per s (see Calculations and assumptions section), these could catalyze the addition of 26 μmol amino acids per g FW day−1, equivalent to the synthesis of about 3 mg protein g−1 FW day−1. The actual rate of protein synthesis is lower, because only 70% of the ribosomes are in polysomes in the light period, and 40% in the dark period (Figure 2), resulting in an estimated synthesis rate of 1.8 mg protein g−1 FW day−1. Under the conditions used in these experiments, Arabidopsis contains about 15 mg protein g−1 FW (Gibon et al, 2004a, 2009; Hannemann et al, 2009) and grows exponentially (see e.g. Tschoep et al, 2009), with a relative growth rate of 0.15–0.20 g FW g−1 FW day−1, which is equivalent to the synthesis of 2.2–3.0 mg protein g−1 FW day−1. The rate of protein synthesis estimated from ribosome copy number therefore resembles that required for the observed rate of growth. Ribosomal occupancy of transcripts encoding enzymes in central metabolism We next investigated ribosomal occupancy of transcripts for 98 genes (Supplementary Table I) including the major members of the gene families for 35 enzymes of central metabolism (84 transcripts). We also included AtCAB1 and AtCAB2 as representatives of photosynthetic and circadian-regulated genes whose expressions peak at the beginning of the day (Ernst et al, 1990), three circadian-regulated genes whose expressions peak at the end of the day (GER3, CAT3, GRP7) and 9 transcripts for ‘house-keeping’ genes that are frequently used for normalization of transcriptional analyses (Czechowski et al, 2005). They are all nuclear encoded, except for the plastid-encoded RubisCO large subunit (RBCL). We used qRT–PCR to investigate the distribution of these 98 transcripts in the polysome gradients. To allow precise quantification, the qRT–PCR data were normalized on four artificial control RNAs that were added at a known concentration before purification of RNA in the gradient fractions. This allowed copy numbers of each transcript species to be determined per fraction. In addition to being necessary for subsequent calculations (see below), this bypassed the problems associated with normalization of transcript levels between fractions in polysome gradients. Transcript levels are usually normalized to total RNA. However, the relative levels of transcript and rRNA probably change across a polysome gradient. ‘House-keeping’ genes are often used for normalization of qRT–PCR data between different organs or environment treatments, but cannot be used for this purpose in polysome fractions, because it is not known whether they are subject to translational regulation. The proportion of transcript in each fraction of the polysome gradient can be easily calculated using qRT–PCR data in combination with internal standards, by simply comparing the numbers of transcripts against internal standard as a control across the whole gradient. The transcript concentrations in the rosette (unfractionated RNA) and polysome fractions, and the ribosomal occupancy (i.e. the proportion of a transcript in the SPS and LPS fractions) are provided in Supplementary Table II, along with the information about the length of the coding sequence, and length and molecular weight of the encoded peptide. Ribosomal occupancy of the 98 transcripts analyzed, varied between 40–95% in the dark period, and 50–90% in the light period (Figure 3). Most transcripts show a marked increase in occupancy between the dark and the light periods, with an increase of between 5 and 55% in absolute terms, and between 10 and 100% relative to the value in the dark period. ‘House-keeping’ genes showed a similar response to other transcripts. RBCL, RBCS-1B and RBCS-2B did not show any increase in ribosomal occupancy in the light (see below for further discussion). The increase in ribosomal occupancy in the light is smaller than the twofold increase of ribosome in polysomes (Figure 2), indicating that the average number of ribosomes per transcript probably increases in the light period. Figure 3.Scatter plot comparing the ribosome occupancy of 98 transcripts in the night and in the light period. Ribosome occupancy was calculated as (SPS+LPS)/(NPS+SPS+LPS). Green circles represent photosynthetic proteins, green filled circles indicate RBCS gene family and RBCL, and red circles indicate genes that are classified as ‘house-keeping’ in expression studies. The plot is generated using data provided in Supplementary Table II. Download figure Download PowerPoint Ribosomal occupancy was weakly but significantly dependent on transcript concentration, with a Pearson's R2 value of 0.065 (P=0.011) in the dark period, and 0.102 (P=0.001) in the light period (Figure 4). Transcript concentrations varied by three orders of magnitude. There was a large range of ribosomal occupancy (from 40 to >80%) for transcripts with the same concentration. Some of this was due to the effect of light, but there was still a large range when transcripts are considered in one condition. This indicates that ribosomal occupancy depends more on individual features of transcripts than their concentrations. Figure 4.Scatter plot for transcript abundance versus ribosome occupancy in the night and the light period. Ribosomal occupancy was calculated as (SPS+LPS)/(NPS+SPS+LPS). Blue and orange symbols denote plant material collected in the dark and light periods, respectively. Filled symbols denote the RBCS gene family (•) and RBCL (▪). Ribosomal occupancy is weakly, but significantly dependent on transcript concentration in the dark period (Pearson's R2=0.065, P-value=0.011) and light period (Pearson's R2=0.102, P-value=0.001). The plot is generated by using the data provided in Supplementary Table II. Download figure Download PowerPoint For almost all transcripts except those encoding RubisCO subunits, there are 10–100 times lesser transcripts in the SPS fraction than in the LPS fraction. This is shown by the low log10 ratio of SPS/LPS in Figure 5. The proportion of transcript in SPS compared with LPS decreases as the proportion in the NPS fraction decreases, resulting in a near-linear relation in this semi-log plot. This empirical