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• W2167542004 abstract "Leaf senescence represents the final stage of leaf development and is associated with fundamental changes on the level of the proteome. For the quantitative analysis of changes in protein abundance related to early leaf senescence, we designed an elaborate double and reverse labeling strategy simultaneously employing fluorescent two-dimensional DIGE as well as metabolic 15N labeling followed by MS. Reciprocal 14N/15N labeling of entire Arabidopsis thaliana plants showed that full incorporation of 15N into the proteins of the plant did not cause any adverse effects on development and protein expression. A direct comparison of DIGE and 15N labeling combined with MS showed that results obtained by both quantification methods correlated well for proteins showing low to moderate regulation factors. Nano HPLC/ESI-MS/MS analysis of 21 protein spots that consistently exhibited abundance differences in nine biological replicates based on both DIGE and MS resulted in the identification of 13 distinct proteins and protein subunits that showed significant regulation in Arabidopsis mutant plants displaying advanced leaf senescence. Ribulose 1,5-bisphosphate carboxylase/oxygenase large and three of its four small subunits were found to be down-regulated, which reflects the degradation of the photosynthetic machinery during leaf senescence. Among the proteins showing higher abundance in mutant plants were several members of the glutathione S-transferase family class phi and quinone reductase. Up-regulation of these proteins fits well into the context of leaf senescence since they are generally involved in the protection of plant cells against reactive oxygen species which are increasingly generated by lipid degradation during leaf senescence. With the exception of one glutathione S-transferase isoform, none of these proteins has been linked to leaf senescence before. Leaf senescence represents the final stage of leaf development and is associated with fundamental changes on the level of the proteome. For the quantitative analysis of changes in protein abundance related to early leaf senescence, we designed an elaborate double and reverse labeling strategy simultaneously employing fluorescent two-dimensional DIGE as well as metabolic 15N labeling followed by MS. Reciprocal 14N/15N labeling of entire Arabidopsis thaliana plants showed that full incorporation of 15N into the proteins of the plant did not cause any adverse effects on development and protein expression. A direct comparison of DIGE and 15N labeling combined with MS showed that results obtained by both quantification methods correlated well for proteins showing low to moderate regulation factors. Nano HPLC/ESI-MS/MS analysis of 21 protein spots that consistently exhibited abundance differences in nine biological replicates based on both DIGE and MS resulted in the identification of 13 distinct proteins and protein subunits that showed significant regulation in Arabidopsis mutant plants displaying advanced leaf senescence. Ribulose 1,5-bisphosphate carboxylase/oxygenase large and three of its four small subunits were found to be down-regulated, which reflects the degradation of the photosynthetic machinery during leaf senescence. Among the proteins showing higher abundance in mutant plants were several members of the glutathione S-transferase family class phi and quinone reductase. Up-regulation of these proteins fits well into the context of leaf senescence since they are generally involved in the protection of plant cells against reactive oxygen species which are increasingly generated by lipid degradation during leaf senescence. With the exception of one glutathione S-transferase isoform, none of these proteins has been linked to leaf senescence before. A major focus of proteome research is the simultaneous identification and quantification of proteins in cells, tissues, or organisms in dependence on the developmental stage, different physiological conditions, environmental influences, or genotypes. This quantitative, mass spectrometry (MS) 1The abbreviations used are: At, Arabidopsis thaliana; cpr5, constitutive expressor of pathogenesis-related genes 5; CyDyes, cyanine dyes; FI, fluorescent intensity; GST, glutathione S-transferase; MLP, major latex protein; RP, reversed-phase; RuBisCO, ribulose 1,5-bisphosphate carboxylase/oxygenase; wt, wild-type. 1The abbreviations used are: At, Arabidopsis thaliana; cpr5, constitutive expressor of pathogenesis-related genes 5; CyDyes, cyanine dyes; FI, fluorescent intensity; GST, glutathione S-transferase; MLP, major latex protein; RP, reversed-phase; RuBisCO, ribulose 1,5-bisphosphate carboxylase/oxygenase; wt, wild-type.-based description of proteomes was facilitated by the development of various stable isotope labeling techniques that have since been applied to proteomics studies in a multitude of organisms (1Beynon R.J. Pratt J.M. Metabolic labeling of proteins for proteomics.Mol. Cell. Proteomics. 2005; 4: 857-872Abstract Full Text Full Text PDF PubMed Scopus (172) Google Scholar, 2Julka S. Regnier F.E. Recent advancements in differential proteomics based on stable isotope coding.Brief. Funct. Genomic Proteomics. 2005; 4: 158-177Crossref PubMed Scopus (67) Google Scholar). In plant proteomics, however, the most frequently used method for comparative, quantitative studies so far has been two-dimensional PAGE (3Glinski M. Weckwerth W. The role of mass spectrometry in plant systems biology.Mass Spectrom. Rev. 2006; 25: 173-214Crossref PubMed Scopus (114) Google Scholar). In traditional two-dimensional PAGE approaches, quantitative differences in protein abundance between biological samples are revealed by comparing spot patterns in individual gels based on densitometric analysis following silver or Coomassie Blue staining. Limitations of this method regarding reproducibility, sensitivity, and dynamic range of protein quantification were improved significantly by introducing the DIGE technology (4Unlu M. Morgan M.E. Minden J.S. Difference gel electrophoresis: a single gel method for detecting changes in protein extracts.Electrophoresis. 1997; 18: 2071-2077Crossref PubMed Scopus (1823) Google Scholar). The DIGE technique employs spectrally resolvable fluorescent cyanine dyes (CyDyes) to label proteins prior to separation by two-dimensional PAGE (5Tonge R. Shaw J. Middleton B. Rowlinson R. Rayner S. Young J. Pognan F. Hawkins E. Currie I. Davison M. Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology.Proteomics. 2001; 1: 377-396Crossref PubMed Scopus (798) Google Scholar). Using the minimal labeling approach, two distinct protein samples are separately labeled with the fluorescent dyes Cy3 and Cy5, respectively, while an internal standard consisting of equal amounts of protein of all samples to be compared in a study is labeled with Cy2 (6Alban A. David S.O. Bjorkesten L. Andersson C. Sloge E. Lewis S. Currie I. A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard.Proteomics. 2003; 3: 36-44Crossref PubMed Scopus (767) Google Scholar). The internal standard is used for normalization of data and allows for both intra- and inter-gel comparison, thereby significantly reducing technical inconsistencies caused by gel-to-gel variations. This leads to improved accuracy and reproducibility of protein quantification and, provided that an appropriate number of independent experiments are performed, facilitates the statistically sound identification of differentially regulated proteins in biological samples. Despite the advantages resulting from the use of DIGE, however, this technique is subjected to the same fundamental restrictions as traditional two-dimensional PAGE. It exhibits a strong bias against hydrophobic proteins such as membrane proteins and proteins with an extreme isoelectric point and/or molecular weight, for example. In addition, accurate relative protein quantification is impaired when two or more protein species are present in the same spot (7Wu W.W. Wang G. Baek S.J. Shen R.F. Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D gel- or LC-MALDI TOF/TOF.J. Proteome Res. 2006; 5: 651-658Crossref PubMed Scopus (521) Google Scholar).Stable isotope labeling of proteins or peptides combined with MS analysis represents an alternative strategy for accurate, relative quantification of proteins on a global scale. In this approach, proteins or peptides of two different samples are differentially labeled with stable isotopes, combined in equal ratio, and then jointly processed for subsequent MS analysis. Relative quantification of proteins is based on the comparison of signal intensities or peak areas of isotope-coded peptide pairs extracted from the respective mass spectra. Stable isotopes can be introduced either chemically into proteins/peptides via derivatization of distinct functional groups of amino acids or metabolically during protein biosynthesis. Metabolic labeling strategies are based on the in vivo incorporation of stable isotopes during growth of organisms. Nutrients or amino acids in a defined medium are replaced by their isotopically labeled (15N, 13C, or 2H) counterparts eventually resulting in uniform labeling of proteins during the processes of cell growth and protein turnover (1Beynon R.J. Pratt J.M. Metabolic labeling of proteins for proteomics.Mol. Cell. Proteomics. 2005; 4: 857-872Abstract Full Text Full Text PDF PubMed Scopus (172) Google Scholar). As a consequence, differentially labeled cells or organisms can be combined directly after harvesting. This minimizes experimental variations due to separate sample handling and thus facilitates relative protein quantification of high accuracy. Since metabolic labeling is best applicable to biological systems that can be maintained under controlled conditions, it has been predominantly applied to unicellular organisms such as bacteria (8Conrads T.P. Alving K. Veenstra T.D. Belov M.E. Anderson G.A. Anderson D.J. Lipton M.S. Pasa-Tolic L. Udseth H.R. Chrisler W.B. Thrall B.D. Smith R.D. Quantitative analysis of bacterial and mammalian proteomes using a combination of cysteine affinity tags and 15N-metabolic labeling.Anal. Chem. 2001; 73: 2132-2139Crossref PubMed Scopus (264) Google Scholar) and yeast (9Oda Y. Huang K. Cross F.R. Cowburn D. Chait B.T. Accurate quantitation of protein expression and site-specific phosphorylation.Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 6591-6596Crossref PubMed Scopus (938) Google Scholar) as well as cell culture systems (10Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Abstract Full Text Full Text PDF PubMed Scopus (4514) Google Scholar). However, the feasibility to label multicellular organisms such as Caenorhabditis elegans, Drosophila melanogaster, and a rat metabolically was shown as well (11Krijgsveld J. Ketting R.F. Mahmoudi T. Johansen J. Artal-Sanz M. Verrijzer C.P. Plasterk R.H. Heck A.J. Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics.Nat. Biotechnol. 2003; 21: 927-931Crossref PubMed Scopus (340) Google Scholar, 12Wu C.C. MacCoss M.J. Howell K.E. Matthews D.E. Yates 3rd, J.R. Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis.Anal. Chem. 2004; 76: 4951-4959Crossref PubMed Scopus (337) Google Scholar). Recently, increasing efforts have been made to adopt metabolic labeling for plant proteomics. Suspension cultures of Arabidopsis thaliana cells or A. thaliana plants grown in liquid culture were successfully labeled with either stable isotope-coded amino acids (13Gruhler A. Schulze W.X. Matthiesen R. Mann M. Jensen O.N. Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry.Mol. Cell. Proteomics. 2005; 4: 1697-1709Abstract Full Text Full Text PDF PubMed Scopus (175) Google Scholar) or with 15N (14Engelsberger W.R. Erban A. Kopka J. Schulze W.X. Metabolic labeling of plant cell cultures with K15NO3 as a tool for quantitative analysis of proteins and metabolites.Plant Methods. 2006; 2: 1-11PubMed Google Scholar, 15Lanquar V. Kuhn L. Lelievre F. Khafif M. Espagne C. Bruley C. Barbier-Brygoo H. Garin J. Thomine S. 15N-metabolic labeling for comparative plasma membrane proteomics in Arabidopsis cells.Proteomics. 2007; 7: 750-754Crossref PubMed Scopus (60) Google Scholar, 16Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana.Mol. Cell. Proteomics. 2007; 6: 860-881Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar, 17Benschop J.J. Mohammed S. O'Flaherty M. Heck A.J. Slijper M. Menke F.L. Quantitative phospho-proteomics of early elicitor signalling in Arabidopsis.Mol. Cell. Proteomics. 2007; 6: 1198-1214Abstract Full Text Full Text PDF PubMed Scopus (529) Google Scholar, 18Nelson C.J. Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Implications of 15N-metabolic labeling for automated peptide identification in Arabidopsis thaliana.Proteomics. 2007; 7: 1279-1292Crossref PubMed Scopus (93) Google Scholar).In the present study, we used A. thaliana as model organism to gain deeper insights into early leaf senescence on the protein level. Leaf senescence is a highly regulated process in which nutrients from the senescing leaf are relocated to other parts of the plant and which eventually leads to leaf death (19Lim P.O. Kim H.J. Nam H.G. Leaf senescence.Annu. Rev. Plant Biol. 2006; 58: 115-136Crossref Scopus (1447) Google Scholar). To identify changes in protein abundance related to early leaf senescence, we analyzed 16-day-old A. thaliana wild-type (wt) plants versus the onset of leaf death mutant old1–1. Compared with the wt, this mutant features a strong early leaf senescence phenotype (described in detail in (20Jing H.C. Sturre M.J.G. Hille J. Dijkwel P.P. Arabidopsis onset of leaf death mutants identify a regulatory pathway controlling leaf senescence.Plant J. 2002; 32: 51-63Crossref PubMed Scopus (99) Google Scholar, 21Jing H.C. Schippers J.H. Hille J. Dijkwel P.P. Ethylene-induced leaf senescence depends on age-related changes and OLD genes in Arabidopsis.J. Exp. Bot. 2005; 56: 2915-2923Crossref PubMed Scopus (144) Google Scholar, 22Jing, H. C., Anderson, L., Sturre, M. J. G., Hille, J., and Dijkwel, P. P. (2007) Arabidopsis CPR5 is a senescence-regulatory gene with pleiotropic function as predicted by the evolutionary theory of senescence. J. Exp. Bot. in pressGoogle Scholar)). After 16 days of growth, A. thaliana plants did not show visible signs of leaf senescence, i.e. yellowing of leaves. Expanding an approach reported by Kolkman et al. (23Kolkman A. Dirksen E.H. Slijper M. Heck A.J. Double standards in quantitative proteomics: direct comparative assessment of difference in gel electrophoresis and metabolic stable isotope labeling.Mol. Cell. Proteomics. 2005; 4: 255-266Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar), we designed a double and reverse labeling strategy employing both two-dimensional DIGE and metabolic 15N labeling for relative quantification of plant proteins. A. thaliana plants used in this work were grown on solid medium in a growth chamber simulating growth conditions as natural as possible. Reverse 14N/15N labeling experiments showed that quantitative incorporation of 15N-isotopes (95%) had no adverse effects on plant development. The concomitant use of DIGE as alternative quantification technique provided us with a control to assess the consistency of the results obtained by both methods. Overall, we performed nine independent experiments, which led to the reliable identification of 13 different proteins or protein subunits with significant changes in abundance in the old1–1 mutant plant. Ribulose 1,5-bisphosphate carboxylase/oxygenase large as well as three of its four small subunits showed decreased abundance in mutant plants. Proteins found to be up-regulated were, among others, several isoforms of the glutathione S-transferase (GST) family class phi and quinone reductase. These proteins are generally known to be involved in the detoxification of reactive oxygen species (24Marrs K.A. The functions and regulation of glutathione S-transferases in plants.Annu. Rev. Plant Physiol. Plant Mol. Biol. 1996; 47: 127-158Crossref PubMed Scopus (1073) Google Scholar, 25Sparla F. Tedeschi G. Pupillo P. Trost P. Cloning and heterologous expression of NAD(P)H:quinone reductase of Arabidopsis thaliana, a functional homologue of animal DT-diaphorase.FEBS Lett. 1999; 463: 382-386Crossref PubMed Scopus (40) Google Scholar).DISCUSSIONTo analyze changes in protein abundance occurring during early leaf senescence, we developed a comprehensive quantitative proteomics strategy comprising differential labeling of proteins from A. thaliana wt and old1–1 mutant plants using CyDyes and 14N/15N-isotopes followed by MS (Fig. 1). We used second-generation plants harvested after 16 days of growth. This time point was chosen because our focus was to identify alterations in protein abundance during early leaf senescence, i.e. the stage before visible symptoms of leaf senescence occur. In old1–1 mutants grown on solid medium, visible yellowing was not observed before day 17 (data not shown). We successfully demonstrated that full labeling with 15N, which has recently been reported for A. thaliana suspension cultures or plants grown in liquid culture (14Engelsberger W.R. Erban A. Kopka J. Schulze W.X. Metabolic labeling of plant cell cultures with K15NO3 as a tool for quantitative analysis of proteins and metabolites.Plant Methods. 2006; 2: 1-11PubMed Google Scholar, 15Lanquar V. Kuhn L. Lelievre F. Khafif M. Espagne C. Bruley C. Barbier-Brygoo H. Garin J. Thomine S. 15N-metabolic labeling for comparative plasma membrane proteomics in Arabidopsis cells.Proteomics. 2007; 7: 750-754Crossref PubMed Scopus (60) Google Scholar, 16Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana.Mol. Cell. Proteomics. 2007; 6: 860-881Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar, 17Benschop J.J. Mohammed S. O'Flaherty M. Heck A.J. Slijper M. Menke F.L. Quantitative phospho-proteomics of early elicitor signalling in Arabidopsis.Mol. Cell. Proteomics. 2007; 6: 1198-1214Abstract Full Text Full Text PDF PubMed Scopus (529) Google Scholar, 18Nelson C.J. Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Implications of 15N-metabolic labeling for automated peptide identification in Arabidopsis thaliana.Proteomics. 2007; 7: 1279-1292Crossref PubMed Scopus (93) Google Scholar), is equally feasible for entire A. thaliana plants grown on solid medium representing more natural growth conditions for this species. Following a labeling scheme spanning two generations, incorporation of 15N into the proteins of wt and old1–1 mutant plants amounted to 95%. Using distinct mixtures of protein extracts from 14N/15N-labeled wt plants, we evaluated the accuracy of MS-based relative protein quantification in the range of 1:5 to 5:1 and found a high correlation (R2 = 0.9965) between experimental and theoretical values (supplemental Fig. 1). This is in accordance with findings reported by Huttlin et al. showing that quantification is accurate for ratios between 1:12 and 12:1 (16Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana.Mol. Cell. Proteomics. 2007; 6: 860-881Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar). We observed relative errors between 7% for the 1:1 ratio and up to 21% for higher mixing ratios, which is in the range of those found in previous studies (15Lanquar V. Kuhn L. Lelievre F. Khafif M. Espagne C. Bruley C. Barbier-Brygoo H. Garin J. Thomine S. 15N-metabolic labeling for comparative plasma membrane proteomics in Arabidopsis cells.Proteomics. 2007; 7: 750-754Crossref PubMed Scopus (60) Google Scholar, 44Li X.J. Zhang H. Ranish J.A. Aebersold R. Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry.Anal. Chem. 2003; 75: 6648-6657Crossref PubMed Scopus (315) Google Scholar).The simultaneous use of DIGE and 14N/15N labeling combined with MS provided us with two independent approaches, which allowed us not only to assess the applicability of metabolic 15N labeling for quantitative plant proteomics but also to directly compare the capacity of both methods. Reversed 14N/15N labeling enabled us to demonstrate that the labeling did not have any side effects on protein expression and development of A. thaliana plants. For this, we analyzed 21 protein spots showing no regulation based on DIGE and performed at least four independent experiments per set to gain statistically sound data. Multiplication of abundance ratios based on 14N/15N-labeled peptides calculated for proteins in Set 1 and Set 2 yielded an average value of 0.90 (± 0.27) (Fig. 3A), indicating high consistency between both data sets. Overall, MS-based quantification results confirmed the DIGE findings, i.e. that the proteins underlying the spots do not exhibit differences in abundance between wt and mutant plants. However, we could identify one protein, AtGSTF8, which consistently exhibited a regulation factor of approximately +2 in both sets that could not be detected by DIGE.Our double and reverse labeling strategy resulted in the determination of 13 proteins and protein subunits that showed significant regulation in the old1–1 mutant compared with Arabidopsis wt plants. All proteins showed consistent regulation in both sets based on either DIGE (Fig. 4A) or 14N/15N labeling and MS (Fig. 4B). In addition, average values of 1.09 (± 0.26) for DIGE and 1.09 (± 0.30) for quantitative MS across all proteins indicate a very good correlation between Set 1 and Set 2 for both quantification strategies. Statistical analyses did not reveal any outliers in the data set based on 14N/15N labeling (Fig. 4D), whereas two outliers were detected in the data set derived from DIGE (Fig. 4C). These were spots number 58 and 65 with average values of 1.83 and 1.65. Both spots exhibited diffuse spot borders, which may have introduced small errors leading to a poor correlation between both sets of experiments.To assess the comparability of DIGE and 14N/15N labeling combined with MS for relative protein quantification, we divided the average abundance ratios of proteins determined by either method (note that only single protein-containing spots showing significant regulation were considered). The average values across all 20 protein spots were 0.99 (± 0.46) for Set 1 (Fig. 5A) and 1.40 (± 1.01) for Set 2 (Fig. 5B), indicating high consistency between both quantification methods. Statistical analyses revealed the presence of one outlier in Set 1, the protein quinone reductase in spot number 55, and two outliers in Set 2, namely AtGSTF7 in spot number 60 and AtGSTF2 in spot number 61. Both GST isoforms were consistently found to be highly regulated in all experiments (Table I). The average values determined for the individual proteins based on 14N/15N labeling, however, were 3- (AtGSTF2 in Set 2) to 8-fold (AtGSTF7 in Set 1) higher than those obtained by DIGE. This drift is more pronounced in Set 1, which is a reasonable explanation why these GSTs were classified as outliers in this data set. Interestingly, the tendency toward the determination of considerably higher regulation factors following MS-based quantification is true for all proteins in our study exhibiting regulation beyond the factor of ± 3 (Table I). Consequently, the correlation between DIGE and MS-based quantification is lower for proteins with regulation factors exceeding −3 and +3. Box plots visualize this observation by showing a skew to the lower quartile for Set 1 (Fig. 5C) and a skew to the upper quartile for Set 2 (Fig. 5D). Our results correspond to observations made by Kolkman et al. who evaluated the comparability of DIGE and 15N labeling combined with MS in a quantitative proteomic study of different yeast strains (23Kolkman A. Dirksen E.H. Slijper M. Heck A.J. Double standards in quantitative proteomics: direct comparative assessment of difference in gel electrophoresis and metabolic stable isotope labeling.Mol. Cell. Proteomics. 2005; 4: 255-266Abstract Full Text Full Text PDF PubMed Scopus (86) Google Scholar). They found a good correlation between both quantification techniques as long as the protein concentration ratios were within the range of −3 to +3, too. Beyond these margins, however, they also observed that MS provided higher values for protein concentration ratios. According to Huttlin et al. (16Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana.Mol. Cell. Proteomics. 2007; 6: 860-881Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar), MS-based quantification based on full metabolic labeling results in accurate values for regulation factors of ±12. To our knowledge, analogous data for DIGE experiments are not available. At present, it is therefore not possible to assess which quantification technique is more reliable and better suited for the determination of abundance ratios of proteins exhibiting rather extreme changes in abundance. Further studies are necessary to clarify this question. A promising approach to address this issue is the use of partial metabolic labeling. In a comparative study of full (> 98% 15N) versus partial metabolic labeling (5–6% 15N) of A. thaliana plants, Huttlin et al. demonstrated that the latter is superior for the analysis of proteins exhibiting large differences in protein abundance (16Huttlin E.L. Hegeman A.D. Harms A.C. Sussman M.R. Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana.Mol. Cell. Proteomics. 2007; 6: 860-881Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar).Fig. 6 demonstrates basic features of DIGE (Fig. 6A) and MS for relative protein quantification (Figs. 6B and C). While differences in protein abundance are typically immediately apparent following fluorescence image analysis and usually only regulated protein spots are subsequently identified by MS, stable isotope labeling combined with MS enables the global identification and quantification of proteins in the same experiment. Moreover, a general advantage of metabolic labeling and MS is the capability to quantify accurately different proteins comigrating during electrophoresis as illustrated for the proteins AtGSTF9 and AtGSTF2 both present in spot number 58 (Figs. 6B and C). MS-based quantification revealed that the two GST isoforms exhibited striking differences in abundance ratios; AtGSTF9 was ∼2-fold up-regulated in old1–1 mutant plants while AtGSTF2 exhibited a regulation factor of roughly +20. Based on DIGE, however, only one value was obtained for both proteins, i.e. +1.7 in Set 1 and +3.1 in Set 2 (Table I).We reliably identified five distinct proteins or protein subunits that were significantly down-regulated in old1–1 mutant plants, RuBisCO large subunit, RuBisCO small subunits 1A, 1B, and 2B as well as a protein related to major latex protein. RuBisCO is the key enzyme of the photosynthetic CO2 fixation and consists of eight large and eight small subunits. It catalyzes the carboxylation of d-ribulose 1,5-bisphosphate, the primary event in carbon dioxide fixation, as well as the oxidative fragmentation of the pentose substrate. Reduction in RuBisCO levels has been associated with senescence and its degradation may be triggered by reactive oxygen species (reviewed in (45Hortensteiner S. Feller U. Nitrogen metabolism and remobilization during senescence.J. Exp. Bot. 2002; 53: 927-937Crossref PubMed Scopus (522) Google Scholar)), such as ozone. In agreement with this idea, our results consistently showed an ∼2-fold decrease of all RuBisCO subunits at a developmental stage (16 days) before symptoms of leaf senescence are visible. Against the background of extensive changes in the metabolism of senescing leaves for the purposes of nutrient retrieval, reduction in RuBisCO levels is a necessary consequence. Indeed, it has been reported that the earliest structural hallmark of leaf senescence is the disintegration of chloroplasts, which is accompanied by, among others, the progressive loss of RuBisCO on the biochemical level (19Lim P.O. Kim H.J. Nam H.G. Leaf senescence.Annu. Rev. Plant Biol. 2006; 58: 115-136Crossref Scopus (1447) Google Scholar). A further protein with a 2- to 3-fold lower abundance in the old1–1 mutant was a protein related to major latex protein (MLP). MLPs were first isolated from the latex of opium poppy and have since been found to be present in a number of other plants and tissues, including Arabidopsis (46Cannon S.B. Young N.D. OrthoParaMap: distinguishing orthologs from paralogs by integrating comparative genome data and gene phylogenies.BMC Bioinformatics. 2003; 35: 1-15Google Scholar, 47Stromvik M.V. Sundararaman V.P. Vodkin L.O. A novel promoter from soybean that is active in a complex developmental pattern with and without its proximal 650 base pairs.Plant Mol. Biol. 1999; 41: 217-231Crossref PubMed Scopus (34) Google Scholar). The general function of this protein family is still unknown; follow-up studies" @default.
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• W2167542004 title "Study of Early Leaf Senescence in Arabidopsis thaliana by Quantitative Proteomics Using Reciprocal 14N/15N Labeling and Difference Gel Electrophoresis" @default.
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• W2167542004 doi "https://doi.org/10.1074/mcp.m700340-mcp200" @default.
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