Molecular Systems Biology 5; Article number 314; doi:10.1038/msb.2009.68
`Citation: Molecular Systems Biology 5:314
`& 2009 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/09
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`
`Ribosome and transcript copy numbers, polysome
`occupancy and enzyme dynamics in Arabidopsis
`
`Maria Piques, Waltraud X Schulze, Melanie Ho¨ hne, Bjo¨ rn Usadel, Yves Gibon1, Johann Rohwer2 and Mark Stitt*
`
`Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, Germany
`1 Present address: INRA Bordeaux, University of Bordeaux 1&2, UMR619 Fruit Biology, F-33883 Villenave d’Ornon, France
`2 Permanent address: Triple-J Group for Molecular Cell Physiology, Department of Biochemistry, Stellenbosch University, Private Bag X1, 7602 Matieland, South Africa
`* Corresponding author. Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm 14474, Germany. Tel.: þ 49 331 5678100;
`Fax: þ 49 331 5678101; E-mail: mstitt@mpimp-golm.mpg.de
`
`Received 24.4.09; accepted 21.7.09
`
`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.
`Molecular Systems Biology 5: 314; published online 13 October 20009; doi:10.1038/msb.2009.68
`Subject Categories: functional genomics; plant biology
`Keywords: Arabidopsis; polysomes; quantitative RT–PCR; ribosome; translation
`
`This is an open-access article distributed under the terms of the Creative Commons Attribution Licence,
`which permits distribution and reproduction in any medium, provided the original author and source are
`credited. Creation of derivative works is permitted but the resulting work may be distributed only under the
`same or similar licence to this one. This licence does not permit commercial exploitation without specific
`permission.
`
`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 (Kcat¼3 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.
`their
`Thousands of genes undergo diurnal changes of
`transcript level (Bla¨sing 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
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`Quantitative polysome analysis in Arabidopsis
`M Piques et al
`
`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 480 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 ribo-
`somes 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 percen-
`tages 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 concentra-
`tions, 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.
`
`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
`
`Figure 1 Quantitative analysis of translation in Arabidopsis rosette leaves.
`
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`Table I Ribosome content in the cytosol, plastids and mitochondria
`1 FW)Ribosome content (mol g
`
`
`
`Cytosol
`Plastid
`Mitochondrion
`
`Dark period
`7.62E11±1.56E11
`2.64E11±6.66E12
`2.25E12±4.42E13
`
`Light period
`7.27E11±1.79E11
`2.57E11±7.42E12
`2.21E12±3.96E13
`
`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.
`
`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 X5 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
`1 fresh weight (FW) (see,
`ribosomes is about 0.10 nmol g
`Table I and Calculations and assumptions section). Cytosolic
`ribosomes were threefold more abundant than plastid ribo-
`somes, 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 mitochon-
`drial rRNA was not investigated.
`
`Quantitative polysome analysis in Arabidopsis
`M Piques et al
`
`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.
`
`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
`
`ribosomes and the
`the numbers of
`Information about
`proportion found in polysomes was used to estimate the
`overall rate of protein synthesis. The summed concentration of
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`Quantitative polysome analysis in Arabidopsis
`M Piques et al
`
`cytosolic, plastid and mitochondrial ribosomes is about
`1 FW (see Table I). Assuming that a ribosome
`0.1 nmol g
`adds 3 amino acids per s (see Calculations and assumptions
`section), these could catalyze the addition of 26 mmol amino
`1, equivalent to the synthesis of about 3 mg
`acids per g FW day
`1 FW day
`1. The actual rate of protein synthesis is
`protein g
`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 pro-
`
`1 FW day1. Under the conditions used in these experi-
`tein g
`1 FW
`ments, Arabidopsis contains about 15 mg protein g
`(Gibon et al, 2004a, 2009; Hannemann et al, 2009) and grows
`exponentially (see e.g. Tschoep et al, 2009), with a relative
`
`1 FW day1, which is equiva-
`growth rate of 0.15–0.20 g FW g
`1 FW day
`1. The
`lent to the synthesis of 2.2–3.0 mg protein g
`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 concentra-
`tion 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 normal-
`ization of qRT–PCR data between different organs or environ-
`ment 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 (un-
`fractionated 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
`
`Figure 3 Scatter plot comparing the ribosome occupancy of 98 transcripts in
`the night and in the light period. Ribosome occupancy was calculated as
`(SPSþ LPS)/(NPS þ SPSþ 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.
`
`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.
`Ribosomal occupancy was weakly but significantly depen-
`dent on transcript concentration, with a Pearson’s R2 value of
`0.065 (P¼0.011) in the dark period, and 0.102 (P¼0.001) 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 480%) 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.
`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 relationship indicates that
`
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`Quantitative polysome analysis in Arabidopsis
`M Piques et al
`
`density of ribosomes per transcript, resulting in a decrease of
`the proportion found in the SPS compared with the LPS
`fraction. However, the amount of transcript in the NPS fraction
`is higher than would be expected from a simple binomial
`distribution (data not shown); indicating that the probability
`that ribosomes are recruited to a free transcript is lower than
`for a transcript that already has bound ribosomes.
`The five solid symbols in Figures 3–5 depict the response of
`RBCS-1A, RBCS-1B, RBCS-2B, RBCS-3B (the small nuclear-
`encoded subunit of RubisCO) and RBCL (the large plastid-
`encoded subunit of RubisCO). These five transcripts deviate
`strongly from the general relationship between NPS and SPS/
`LPS. These five transcripts show a high occupancy in the
`dark as well as the light period, and a large proportion of the
`transcript is present in the SPS fraction (18–39% and 9–25% in
`the dark period and light period, respectively) (Figure 5). This
`could indicate that RBCL and RBCS transcripts are subject to
`complex translational control (see Discussion section).
`
`Estimation of translation rates
`
`The rate of synthesis of the proteins encoded by these 98
`transcripts was estimated from the transcript abundance in the
`SPS and LPS fractions, multiplied by the ribosome density per
`translating transcript (see Calculations and assumptions
`section, and Supplementary Table II). The calculation assumes
`an elongation rate of 3 amino acids per ribosome per s, an
`average of 3 ribosomes per transcript in the SPS fraction, and a
`ribosome density of 6.6 ribosomes per kb coding sequence,
`(Brandt et al, 2009) in the LPS fraction.
`The estimated rates of protein synthesis (mol pro-
`1) ranged from 2.5E15 to 2.9E09 in the dark
`1 FW h
`tein g
`period, and 6.5E15 to 4.3E10 in the light period (Table II
`and Supplementary Table II). Among the enzymes involved in
`primary metabolism, RubisCO is the most rapidly synthesized
`enzyme, reflecting the high abundance of this enzyme in
`leaves (see Introduction section). Other rapidly synthesized
`enzymes include several Calvin cycle enzymes (e.g. aldolase,
`NADP–GAPDH, PGK and TPI), enzymes involved in nitrogen
`assimilation (e.g. AlaAT, NR and GS), and NAD–GAPDH and
`NAD–MDH. The relatively high rates of synthesis of PEP
`carboxylase, NADP–IDH, aconitase and PK compared with
`other glycolytic enzymes may reflect the fact that
`these
`enzymes are required to synthesize 2-oxoglutarate, which acts
`as the C acceptor during nitrate and ammonium assimilation.
`The relatively high rate of synthesis of glycerate kinase may be
`related to the fact that in leaves this enzyme is required for
`photorespiration. Fluxes through this pathway are roughly
`15–20% of those through photosynthesis (Zhu et al, 2007),
`and much higher than in respiratory metabolism. Most
`enzymes showed an estimated 50–100% increase in the rate
`of synthesis in the light period compared to the dark period. To
`further interpret the biological significance of these rates of
`synthesis, we compared them with the estimated amount of
`each enzyme in the rosette.
`
`Estimation of protein abundance
`
`Protein abundance of metabolic enzymes in rosette leaves was
`estimated by two independent methods. In one approach,
`
`Figure 4 Scatter plot for transcript abundance versus ribosome occupancy in
`the night and the light period. Ribosomal occupancy was calculated as
`(SPSþ LPS)/(NPS þ SPSþ LPS). Blue and orange symbols denote plant
`material collected in the dark and light periods, respectively. Filled symbols
`denote the RBCS gene family (K) and RBCL (’). Ribosomal occupancy is
`weakly, but significantly dependent on transcript concentration in the dark period
`(Pearson’s R2¼0.065, P-value¼0.011) and light period (Pearson’s R2¼0.102,
`P-value¼0.001). The plot
`is generated by using the data provided in
`Supplementary Table II.
`
`Figure 5 Relation between the fraction of transcript in the non-polysomal
`fraction (NPS) and the distribution of transcript between the small (SPS) and
`large polysomal (LPS) fractions. Blue and orange symbols denote plant material
`collected in the dark and light periods, respectively. Filled symbols denote the
`RBCS gene family (K) and RBCL (’). The plot is generated using data
`provided in Supplementary Table II.
`
`initiation and ribosome progression are determined in a
`similar manner for all these transcripts. It is consistent with
`initiation being the limiting step; an increased probability of
`initiation will result in a decreased fraction of the transcript in
`the NPS fraction, and will also result in an increased average
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`Quantitative polysome analysis in Arabidopsis
`M Piques et al
`
`Table II Estimated rates of protein synthesis of the different enzymes in
`Arabidopsis rosette in the dark and light periods
`
`Enzyme
`
`Ribulose-1,5-bisphosphate
`carboxylase (RubisCO)
`Fructose-bisphosphate aldolase
`(aldolase)
`NADP–glyceraldehyde 3-
`phosphate dehydrogenase
`(NADP–GAPDH)
`Alanine aminotransferase (AlaAT)
`NAD–glyceraldehyde 3-phosphate
`dehydrogenase (NAD–GAPDH)
`NAD–malate dehydrogenase
`(NAD–MDH)
`Nitrate reductase (NR)
`Glutamine synthetase (GS)
`Phosphoglycerokinase (PGK)
`Triose phosphateisomerase (TPI)
`ADP-glucose pyrophosphorylase
`(AGPase)
`Phosphoenolpyruvate carboxylase
`(PEP carboxylase)
`NADP–isocitrate dehydrogenase
`(NADP–IDH)
`Transketolase (TK)
`Aconitase
`Pyruvate kinase (PK)
`NADP–malate dehydrogenase
`(NADP–MDH)
`Acid invertase (INV)
`Glycerate kinase (GK)
`Glucose-6-phosphate isomerase
`(PGI)
`UDP-glucose pyrophosphorylase
`(UGPase)
`Ferredoxin–glutamate synthase
`(Fd–GOGAT)
`Phosphoglucomutase (PGM)
`PPi-phosphofructokinase (PFP)
`Fructose-1,6-bisphosphatase,
`cytosolic (cytFBPase)
`Sucrose phosphate synthase (SPS)
`Fructokinase (FK)
`Fumarase (FUM)
`Glucose-6-phosphate
`dehydrogenase (G6PDH)
`Aspartate aminotransferase
`(AspAT)
`NAD–isocitrate dehydrogenase
`(NAD–IDH)
`NAD–glutamate dehydrogenase
`(NAD–GDH)
`Glucokinase/hexokinase (HK)
`ATP-phosphofructokinase (PFK)
`Shikimate 5-dehydrogenase
`(Shikimate DH)
`
`Estimated translation rate
`
`1 g1 FW)
`(mol h
`
`Dark period
`
`Light period
`
`6.34E–10
`
`8.60E–10
`
`2.84E–11
`
`5.97E–11
`
`2.94E–11
`
`5.13E–11
`
`2.35E–11
`1.48E–11
`
`3.40E–11
`2.60E–11
`
`1.60E–11
`
`2.03E–11
`
`2.30E–11
`1.41E–11
`1.33E–11
`8.24E–12
`3.54E–12
`
`9.20E–12
`1.42E–11
`1.41E–11
`1.18E–11
`5.94E–12
`
`3.90E–12
`
`4.75E–12
`
`5.11E–12
`
`3.35E–12
`
`4.01E–12
`2.45E–12
`2.27E–12
`2.27E–12
`
`2.29E–12
`1.32E–12
`1.09E–12
`
`3.58E–12
`4.44E–12
`3.07E–12
`3.05E–12
`
`2.05E–12
`1.56E–12
`1.77E–12
`
`9.04E–13
`
`1.63E–12
`
`1.39E–12
`
`1.14E–12
`
`8.23E–13
`7.25E–13
`6.29E–13
`
`7.92E–13
`7.56E–13
`4.92E–13
`7.57E–13
`
`1.44E–12
`1.18E–12
`1.25E–12
`
`9.80E–13
`9.20E–13
`9.92E–13
`6.36E–13
`
`4.96E–13
`
`6.22E–13
`
`4.49E–13
`
`5.69E–13
`
`5.46E–13
`
`3.08E–13
`
`2.40E–13
`3.06E–13
`2.34E–13
`
`4.57E–13
`3.83E–13
`3.62E–13
`
`The raw data and calculations are provided in Supplementary Table II.
`
`protein abundance was estimated from the maximum enzyme
`activity (original data provided in Supplementary Table III),
`corrected by literature values for the specific activity (Supple-
`mentary Table IV). In case of disagreement, the highest specific
`activity was chosen because an underestimate of the specific
`activity can easily occur due to loss of activity or incomplete
`purification. Enzyme activities were measured using a robot-
`ized enzyme determination platform, in which products are
`quantitatively determined in highly sensitive cycling assays
`
`Figure 6 Relation between protein concentrations of metabolic enzymes
`calculated from specific enzyme activities or from the emPAI protein abundance
`index determined by mass spectrometric analysis. Filled symbol denotes
`RubisCO, which is not included in the Pearson’s regression analysis. Protein
`abundances estimated using the two methods are highly significantly correlated
`(R2¼0.592, P-value¼0.681E06). The plot is generated by using the data
`provided in Supplementary Tables V and VII.
`
`that allow high dilution ratios and minimize the interference
`form other components in the extracts (Gibon et al, 2004b). For
`all assays, it was checked that the substrate concentration was
`saturating, and that the activity was linear with the amount of
`extract added. The results and calculations are summarized in
`Supplementary Table V.
`In the second approach, relative protein amounts were
`estimated using the emPAI index (Supplementary Table VI).
`The emPAI index is calculated from the fraction of the number
`of experimentally identified tryptic peptides out of all
`detectable tryptic peptides within the mass range of the mass
`spectrometer (Ishihama et al, 2005). This method yields
`abundances that are linear over three orders of magnitude in
`1–10 mmol l
`1), although it
`protein concentration (10 nmol l
`does underestimate very highly abundant single proteins
`(Ishihama et al, 2005). In our data set, RubisCO subunits
`summed to 410% of all proteins identified, and are probably
`underestimated (Supplementary Table VI). The experimen-
`1 FW) was
`tally determined total protein content (15 mg g
`used to convert the mol% values to concentrations (Supple-
`mentary Table VI). On a molar basis, proteins with photo-
`synthetic functions contributed 30% of all identified proteins.
`Protein synthesis made up the second largest functional
`protein group (11%). Proteins with functions in amino acid
`metabolism, redox regulation and TCA cycle/organic acid
`transformation contributed with 5–6% each (Supplementary
`Table VI).
`Both approaches are subject to experimental error and
`involve assumptions. Nevertheless, comparison of the result-
`ing estimates for protein abundance (Figure 6 and Table III)
`revealed a highly significant agreement (Pearson’s R2 ¼0.590,
`P¼0.727E06). The slope of the regression (0.73) deviated
`from 1, with a smaller dynamic range for the proteomics-based
`
`6 Molecular Systems Biology 2009
`
`& 2009 EMBO and Macmillan Publishers Limited
`
`Regeneron Exhibit 2017
`Page 06 of 17
`
`

`

`Quantitative polysome analysis in Arabidopsis
`M Piques et al
`
`Table III Comparison of the estimated enzyme abundance in the rosette and the corresponding estimated time to synthesize the entire enzyme in the rosette in days
`(TP)
`
`Enzyme
`
`1 FW)Protein abundance (mol g
`
`
`
`Days to synthesize all the protein in
`the rosette (TP)
`
`Nitrate reductase (NR)
`ATP-phosphofructokinase (PFK)
`Acid Invertase (INV)
`Glucose-6-phosphate dehydrogenase (G6PDH)
`Glucokinase/hexokinase (HK)
`Alanine aminotransferase (AlaAT)
`ADP-glucose pyrophosphorylase (AGPase)
`Pyrophosphate-phosphofructokinase (PFP)
`NADP–glyceraldehyde 3-phosphate dehydrogenase
`(NADP–GAPDH)
`Ribulose-1,5-bisphosphate carboxylase (RuBisCO)
`Glucose-6-phosphate isomerase (PGI)**
`Phosphoenolpyruvate carboxylase (PEP carboxylase)**
`Triose phosphate isomerase (TPI)*
`Glutamine synthetase (GS)
`Pyruvate kinase (PK)*
`Fructose-1,6-bisphosphatase, cytosolic (cytFBPase)*
`Sucrose phosphate synthase (SPS)
`NAD–isocitrate dehydrogenase (NAD–IDH)
`NAD–glyceraldehyde 3-phosphate dehydrogenase
`(NAD–GAPDH)
`NADP–isocitrate dehydrogenase (NADP–IDH)
`Shikimate 5-dehydrogenase (Shik