Title: Complementary Proteome and Transcriptome Profiling in Phosphate-deficient Arabidopsis Roots Reveals Multiple Levels of Gene Regulation
Abstract: Phosphate (Pi) deficiency impairs plant growth and productivity in many agricultural ecosystems, causing severe reductions in crop yield. To uncover novel aspects in acclimation to Pi starvation, we investigated the correlation between Pi deficiency-induced changes in transcriptome and proteome profiles in Arabidopsis roots. Using exhaustive tandem mass spectrometry-based shotgun proteomics and whole-genome RNA sequencing to generate a nearly complete catalog of expressed mRNAs and proteins, we reliably identified 13,298 proteins and 24,591 transcripts, subsets of 356 proteins and 3106 mRNAs were differentially expressed during Pi deficiency. Most dramatic changes were noticed for genes involved in Pi acquisition and in processes that either liberate Pi or bypass Pi/ATP-consuming metabolic steps, for example during membrane lipid remodeling and glycolytic carbon flux. The concordance between the abundance of mRNA and its encoded protein was generally high for highly up-regulated genes, but the analysis also revealed numerous discordant changes in mRNA/protein pairs, indicative of divergent regulation of transcription and post-transcriptional processes. In particular, a decreased abundance of proteins upon Pi deficiency was not closely correlated with changes in the corresponding mRNAs. In several cases, up-regulation of gene activity was observed solely at the protein level, adding novel aspects to key processes in the adaptation to Pi deficiency. We conclude that integrated measurement and interpretation of changes in protein and transcript abundance are mandatory for generating a complete inventory of the components that are critical in the response to environmental stimuli. Phosphate (Pi) deficiency impairs plant growth and productivity in many agricultural ecosystems, causing severe reductions in crop yield. To uncover novel aspects in acclimation to Pi starvation, we investigated the correlation between Pi deficiency-induced changes in transcriptome and proteome profiles in Arabidopsis roots. Using exhaustive tandem mass spectrometry-based shotgun proteomics and whole-genome RNA sequencing to generate a nearly complete catalog of expressed mRNAs and proteins, we reliably identified 13,298 proteins and 24,591 transcripts, subsets of 356 proteins and 3106 mRNAs were differentially expressed during Pi deficiency. Most dramatic changes were noticed for genes involved in Pi acquisition and in processes that either liberate Pi or bypass Pi/ATP-consuming metabolic steps, for example during membrane lipid remodeling and glycolytic carbon flux. The concordance between the abundance of mRNA and its encoded protein was generally high for highly up-regulated genes, but the analysis also revealed numerous discordant changes in mRNA/protein pairs, indicative of divergent regulation of transcription and post-transcriptional processes. In particular, a decreased abundance of proteins upon Pi deficiency was not closely correlated with changes in the corresponding mRNAs. In several cases, up-regulation of gene activity was observed solely at the protein level, adding novel aspects to key processes in the adaptation to Pi deficiency. We conclude that integrated measurement and interpretation of changes in protein and transcript abundance are mandatory for generating a complete inventory of the components that are critical in the response to environmental stimuli. Bottom-up, high-throughput profiling of transcripts or proteins is a powerful method to analyze changes in biological processes, e.g. during development or upon environmental perturbations. Methods examining gene activity have improved dramatically during the past few years. Quantitative gel-free shotgun proteomics based on tandem mass spectrometry is now possible with high resolution, yielding detailed protein expression maps (1Ong S.E. Mann M. Mass spectrometry-based proteomics turns quantitative.Nat. Chem. Biol. 2005; 1: 252-262Crossref PubMed Scopus (1317) Google Scholar, 2Liberman L.M. Sozzani R. Benfey P.N. Integrative systems biology: an attempt to describe a simple weed.Curr. Opin. Plant Biol. 2012; 15: 162-167Crossref PubMed Scopus (30) Google Scholar). Similarly, the technologies for whole transcriptome sequencing (RNA-seq) ameliorates some of the caveats of DNA microarrays, permitting deep coverage of transcriptomic landscapes and detection of expression changes with a high dynamic range. Computational algorithms to analyze and display the data in a biologically meaningful way have become available. The task of assigning functions to the annotated genes/proteins, however, remains a major challenge. As opposed to a reductionist approach (i.e. studying one gene/protein at a time), systems biology attempts to address the system as a whole in order to understand the interdependence and dynamics of its components and to predict cellular behavior. Deciphering functional gene networks that mediate biological processes in concert will reveal a more holistic view of the processes under study and will provide an approach to manipulate the fitness and performance of plants. Because of the relatively low cost and ease of application, transcriptional profiling has been adopted as the method of choice by many labs to interrogate the adaptation of plants to environmental signals. A large set of genome-wide data is available regarding transcriptional changes induced by Pi deficiency, derived from microarray studies using roots, shoots and seedlings of Arabidopsis (3Wu P. Ma L. Hou X. Wang M. Wu Y. Liu F. Deng X.W. Phosphate starvation triggers distinct alterations of genome expression in Arabidopsis roots and leaves.Plant Physiol. 2003; 132: 1260-1271Crossref PubMed Scopus (368) Google Scholar, 4Hammond J.P. Bennett M.J. Bowen H.C. Broadley M.R. Eastwood D.C. May S.T. Rahn C. Swarup R. Woolaway K.E. White P.J. Changes in gene expression in Arabidopsis shoots during phosphate starvation and the potential for developing smart plants.Plant Physiol. 2003; 132: 578-596Crossref PubMed Scopus (334) Google Scholar, 5Misson J. Raghothama K.G. Jain A. Jouhet J. Block M.A. Bligny R. Ortet P. Creff A. Somerville S. Rolland N. Doumas P. Nacry P. Herrerra-Estrella L. Nussaume L. Thibaud M.C. A genome-wide transcriptional analysis using Arabidopsis thaliana Affymetrix gene chips determined plant responses to phosphate deprivation.Proc. Natl. Acad. Sci. U.S.A. 2005; 102: 11934-11939Crossref PubMed Scopus (705) Google Scholar, 6Bari R. Datt Pant B. Stitt M. Scheible W.R. PHO2, microRNA399, and PHR1 define a phosphate-signaling pathway in plants.Plant Physiol. 2006; 141: 988-999Crossref PubMed Scopus (850) Google Scholar, 7Morcuende R. Bari R. Gibon Y. Zheng W. Pant B.D. Bläsing O. Usadel B. Czechowski T. Udvardi M.K. Stitt M. Scheible W.R.. Genome-wide reprogramming of metabolism and regulatory networks of Arabidopsis in response to phosphorus.Plant Cell Environ. 2007; 30: 85-112Crossref PubMed Scopus (427) Google Scholar, 8Müller R. Morant M. Jarmer H. Nilsson L. Nielsen T.H. Genome-wide analysis of the Arabidopsis leaf transcriptome reveals interaction of phosphate and sugar metabolism.Plant Physiol. 2007; 143: 156-171Crossref PubMed Scopus (252) Google Scholar, 9Marchive C. Yehudai-Resheff S. Germain A. Fei Z. Jiang X. Judkins J. Wu H. Fernie A.R. Fait A. Stern D.B. Abnormal physiological and molecular mutant phenotypes link chloroplast polynucleotide phosphorylase to the phosphorus deprivation response in Arabidopsis.Plant Physiol. 2009; 151: 905-924Crossref PubMed Scopus (38) Google Scholar, 10Lin W.D. Liao Y.Y. Yang T.J. Pan C.Y. Buckhout T.J. Schmidt W. Coexpression-based clustering of Arabidopsis root genes predicts functional modules in early phosphate deficiency signaling.Plant Physiol. 2011; 155: 1383-1402Crossref PubMed Scopus (102) Google Scholar) and also other species such as wild mustard (11Hammond J.P. Broadley M.R. Craigon D.J. Higgins J. Emmerson Z.F. Townsend H.J. White P.J. May S.T. Using genomic DNA-based probe-selection to improve the sensitivity of high-density oligonucleotide arrays when applied to heterologous species.Plant Methods. 2005; 1: 10Crossref PubMed Scopus (67) Google Scholar), rice (12Wasaki J. Yonetani R. Kuroda S. Shinano T. Yazaki J. Fujii F. Shimbo K. Yamamoto K. Sakata K. Sasaki T. Kishimoto N. Kikuchi S. Yamagishi M. Osaki M. Transcriptomic analysis of metabolic changes by phosphorus stress in rice plant roots.Plant Cell Environ. 2003; 26: 1515-1523Crossref Scopus (186) Google Scholar), maize (13Calderon-Vazquez C. Ibarra-Laclette E. Caballero-Perez J. Herrera-Estrella L. Transcript profiling of Zea mays roots reveals gene responses to phosphate deficiency at the plant- and species-specific levels.J. Exp. Bot. 2008; 59: 2479-2497Crossref PubMed Scopus (109) Google Scholar), tomato (14Wang Y.H. Garvin D.F. Kochian L.V. Rapid induction of regulatory and transporter genes in response to phosphorus, potassium, and iron deficiencies in tomato roots. Evidence for cross talk and root/rhizosphere-mediated signals.Plant Physiol. 2002; 130: 1361-1370Crossref PubMed Scopus (241) Google Scholar) and bean (15Hernández G. Ramirez M. Valdes-Lopez O. Tesfaye M. Graham M.A. Czechowski T. Schlereth A. Wandrey M. Erban A. Cheung F. Wu H.C. Lara M. Town C.D. Kopka J. Udvardi M.K. Vance C.P. Phosphorus stress in common bean: root transcript and metabolic responses.Plant Physiol. 2007; 144: 752-767Crossref PubMed Scopus (254) Google Scholar). However, biological function is chiefly carried out by proteins, and determination of protein abundance is mandatory to fully understand the function of a system. Generally, because of post-translational turnover and alternate translation efficiency, mRNA abundance is not a reliable proxy of protein abundance, and modest congruency of the two levels has been reported except for high abundance transcripts and “molecular machines” (16Ideker T. Thorsson V. Ranish J.A. Christmas R. Buhler J. Eng J.K. Bumgarner R. Goodlett D.R. Aebersold R. Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network.Science. 2001; 292: 929-934Crossref PubMed Scopus (1650) Google Scholar, 17Rogers S. Girolami M. Kolch W. Waters K.M. Liu T. Thrall B. Wiley H.S. Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models.Bioinformatics. 2008; 24: 2894-2900Crossref PubMed Scopus (112) Google Scholar, 18Griffin T.J. Gygi S.P. Ideker T. Rist B. Eng J. Hood L. Aebersold R. Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae.Mol. Cell. Proteomics. 2002; 1: 323-333Abstract Full Text Full Text PDF PubMed Scopus (563) Google Scholar, 19Chen G. Gharib T.G. Huang C.C. Taylor J.M. Misek D.E. Kardia S.L. Giordano T.J. Iannettoni M.D. Orringer M.B. Hanash S.M. Beer D.G. Discordant protein and mRNA expression in lung adenocarcinomas.Mol. Cell. Proteomics. 2002; 1: 304-313Abstract Full Text Full Text PDF PubMed Scopus (784) Google Scholar, 20Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.Nat. Biotechnol. 1999; 17: 994-999Crossref PubMed Scopus (4345) Google Scholar, 21Tian Q. Stepaniants S.B. Mao M. Weng L. Feetham M.C. Doyle M.J. Yi E.C. Dai H. Thorsson V. Eng J. Goodlett D. Berger J.P. Gunter B. Linseley P.S. Stoughton R.B. Aebersold R. Collins S.J. Hanlon W.A. Hood L.E. Integrated genomic and proteomic analyses of gene expression in mammalian cells.Mol. Cell. Proteomics. 2004; 3: 960-969Abstract Full Text Full Text PDF PubMed Scopus (644) Google Scholar, 22Baginsky S. Kleffmann T. von Zychlinski A. Gruissem W. Analysis of shotgun proteomics and RNA profiling data from Arabidopsis thaliana chloroplasts.J. Proteome Res. 2005; 4: 637-640Crossref PubMed Scopus (37) Google Scholar). Much has been learned regarding Pi deficiency responses from proteomic approaches (23Li K. Xu C. Zhang K. Yang A. Zhang J. Proteomic analysis of roots growth and metabolic changes under phosphorus deficit in maize (Zea mays L.) plants.Proteomics. 2007; 7: 1501-1512Crossref PubMed Scopus (89) Google Scholar, 24Torabi S. Wissuwa M. Heidari M. Naghavi M.R. Gilany K. Hajirezaei M.R. Omidi M. Yazdi-Samadi B. Ismail A.M. Salekdeh G.H. A comparative proteome approach to decipher the mechanism of rice adaptation to phosphorous deficiency.Proteomics. 2009; 9: 159-170Crossref PubMed Scopus (75) Google Scholar, 25Yao Y. Sun H. Xu F. Zhang X. Liu S. Comparative proteome analysis of metabolic changes by low phosphorus stress in two Brassica napus genotypes.Planta. 2011; 233: 523-537Crossref PubMed Scopus (59) Google Scholar). Despite these efforts, data sets on Pi deficiency-induced changes in the proteome remained somewhat patchy, because of a skew toward high-abundant proteins and work carried out on different species. The parallel analysis and interpretation of disparate “omics” data sets integrate different levels of the cellular response and allows for the uncovering of regulatory mechanisms beyond transcription. A recent study addressing the regulation of transcript and protein abundance in mammalian cells highlights the importance of translational control for the cellular abundance of protein (26Schwanhäusser B. Busse D. Li N. Dittmar G. Schuchhardt J. Wolf J. Chen W. Selbach M. Global quantification of mammalian gene expression control.Nature. 2011; 473: 337-342Crossref PubMed Scopus (4091) Google Scholar), underlining the importance of an integrated view on gene activity. Parallel analysis of transcriptome and proteome expression data is challenging because of biological (e.g. different mRNA and protein stability) and technical biases (e.g. differences in resolution of transcriptomic and proteomic profiles). Because proteomic and transcriptomic data are often collected separately, it remains often unclear whether discordant mRNA and protein expression merely reflects experimental noise or represents biological meaningful post-transcriptional regulation. Phosphorous, mainly taken up as phosphate (Pi) 1The abbreviations used are:PiphosphateSQDGsulfoquinovosyldiacylglycerol.1The abbreviations used are:PiphosphateSQDGsulfoquinovosyldiacylglycerol. by plants, is an essential macronutrient of crucial importance in signaling, metabolism and photosynthesis. Because of its tendency to form complexes with soil cations, the low bioavailability of Pi often limits plant growth. In natural ecosystem, the availability of and demand for Pi are major determinants for the composition of plant communities. Phosphate deficiency is a major cause of severe yield losses in crops and poor quality of edible plant parts. Low Pi availability is often corrected by the use of fertilizers but which are associated with environmental damages and substantial costs. To cope with low Pi availability, plants have evolved a plethora of adaptive processes, aimed at improving the Pi uptake and remobilization, and involve alterations in developmental programs and metabolic networks. Phosphate acquisition and use efficiency are traits that vary among species and cultivars. A prerequisite for developing Pi-efficient germplasm is a thorough understanding of the mechanisms that control cellular Pi homeostasis. Proteomic and transcriptomic profiling studies have uncovered several robustly changed processes in Pi-deficient plants, including the remodeling of lipid metabolism, changes in glycolytic carbon flux, alterations in root development, and signaling pathways (27Nakamura Y. Awai K. Masuda T. Yoshioka Y. Takamiya K. Ohta H. A novel phosphatidylcholine-hydrolyzing phospholipase C induced by phosphate starvation in Arabidopsis.J. Biol. Chem. 2005; 280: 7469-7476Abstract Full Text Full Text PDF PubMed Scopus (194) Google Scholar, 28Plaxton W.C. Tran H.T. Metabolic adaptations of phosphate-starved plants.Plant Physiol. 2011; 156: 1006-1015Crossref PubMed Scopus (372) Google Scholar, 29Chiou T.J. Aung K. Lin S.I. Wu C.C. Chiang S.F. Su C.L. Regulation of phosphate homeostasis by MicroRNA in Arabidopsis.Plant Cell. 2006; 18: 412-421Crossref PubMed Scopus (660) Google Scholar, 30Svistoonoff S. Creff A. Reymond M. Sigoillot-Claude C. Ricaud L. Blanchet A. Nussaume L. Desnos T. Root tip contact with low-phosphate media reprograms plant root architecture.Nat. Genet. 2007; 39: 792-796Crossref PubMed Scopus (433) Google Scholar). Based on microarray experiments, the expression of ∼1000 genes was estimated to be differentially expressed upon Pi deficiency. How tightly these changes correspond to changes of the proteomic profiles is not known. phosphate sulfoquinovosyldiacylglycerol. phosphate sulfoquinovosyldiacylglycerol. In an attempt to identify nodes that are important for the function of roots under conditions of Pi starvation, we cataloged differences in the abundance of mRNAs and proteins by integrated profiling of gene activity using RNA-seq and high-resolution quantitative iTRAQ proteomics. Transcriptomic and proteomic expression profiles were remarkably well correlated, but our analysis also revealed several exceptions indicative of different control mechanisms that dictate gene activity. We argue that this “molecular compendium” of gene and protein expression data provides an informative readout about the physiological state of Pi-deficient roots, uncovering several novel aspects in the metabolism of Pi-deficient plants and provides a comprehensive reference map of gene activity in Arabidopsis roots. Arabidopsis (Arabidopsis thaliana L.) plants were grown in a growth chamber on an agar-based medium as previously described (31Estelle M.A. Somerville C. Auxin-resistant mutants of Arabidopsis thaliana with an altered morphology.Mol. Gen. Genet. 1987; 206: 200-206Crossref Scopus (485) Google Scholar). Seeds of the Col-0 accession were obtained from the Arabidopsis Biological Resource Center (ABRC, Ohio State University, Columbus). Seeds were surface sterilized by immersing them in 5% (v/v) NaOCl for 5 min and 70% ethanol for 7 min, followed by four rinses in sterile water. Seeds were placed onto Petri dishes and kept for 1 d at 4 °C in the dark, after the plates were transferred to a growth chamber and grown at 21 °C under continuous illumination (50 μmol m−2 s−1, Philips TL lamps). The medium was composed of (mm): KNO3 (5), MgSO4 (2), Ca (NO3)2 (2), KH2PO4 (2.5), (μm): H3BO3 (70), MnCl2 (14), ZnSO4 (1), CuSO4 (0.5), NaCl (10), Na2MoO4 (0.2) and 40 μm FeEDTA, solidified with 0.3% Phytagel (Sigma-Aldrich). Sucrose (43 mm) and 4.7 mm Mes were included and the pH was adjusted to 5.5. After 10 days of precultivation, plants were transferred either to fresh agar media without phosphate or to fresh control media and grown for 3 days. The lower concentration of potassium because of the absence of KH2PO4 in the Pi-free media was corrected by the addition of KCl. Roots from control plants and Pi-deficient plants (13-day-old) were ground in liquid nitrogen and suspended in 10× volume of precooled acetone (−20 °C) containing 10% (w/v) trichloroacetic acid and 0.07% (v/v) 2-mercaptoethanol. Proteins were then precipitated for 2 h at −20 °C after thorough mixing. Proteins were collected by centrifuging at 35,000 × g (JA-20 108 rotor; Beckman J2-HS) at 4 °C for 30 min. The supernatant was carefully removed, and the protein pellets were washed twice with cold acetone containing 0.07% (v/v) 2-mercaptoethanol and 1 mm phenylmethanesulfonyl fluoride and a third time with cold acetone without 2-mercaptoethanol. Protein pellets were dried by lyophilization and stored at −80 °C or immediately extracted using protein extraction buffer composed of 6 m urea, 50 mm triethylammonium bicarbonate, pH 8.5, and 2% 3-[(3-cholamidopropyl)dimethylammonio]propanesulfonate for 1 h at 6 °C under constant shaking. Protein extracts were centrifuged at 19,000 × g for 20 min at 10 °C. The supernatant was then collected, and the protein concentration was determined using a protein assay kit (Pierce, Rockford, IL). Total protein (100 μg) was reduced by adding dithiothreitol to a final concentration of 10 mm and incubated for 1 h at room temperature. Subsequently, iodoacetamide was added to a final concentration of 40 mm, and the mixture was incubated for 1 h at room temperature in the dark. Then, dithiothreitol (10 mm) was added to the mixture to consume any free iodoacetamide by incubating the mixture for 1 h at room temperature in the dark. Proteins were then diluted by 50 mm triethylammonium bicarbonate and 1 mm CaCl2 to reduce the urea concentration to less than 0.6 m and digested with 40 μg of modified trypsin (Promega, Madison, WI) at 37 °C overnight. The resulting peptide solution was acidified with 10% trifluoroacetic acid and desalted on a C18 solid-phase extraction cartridge. Desalted peptides were then labeled with iTRAQ reagents (Applied Biosystems, Foster City, CA) according to the manufacturer's instructions. Control samples (proteins extracted from roots of control plants) were labeled with reagent 114; samples from Pi-deficient roots were labeled with reagent 117. Three independent biological experiments with two technical repeats each were performed. The reaction was allowed to proceed for 1 h at room temperature. Subsequently, treated and control peptides were combined and further fractionated offline using high-resolution cation-exchange chromatography (PolySulfoethyl A, 5 μm, 200-Å bead). In total, 35 fractions were collected and combined into 16 final fractions. Each final fraction was lyophilized in a centrifugal speed vacuum concentrator. Samples were stored at −80 °C. Liquid chromatography was performed on a nanoACQUITY UPLC System (Waters, Milford, MA) coupled to an LTQ Orbitrap Velos hybrid mass spectrometer (Thermo Scientific, Waltham, MA) equipped with a PicoView nanospray interface (New Objective, Woburn, MA). Peptide mixtures were loaded onto a 75 μm × 250 mm nanoACQUITY UPLC BEH130 column packed with C18 resin (Waters, Milford, MA) and separated using a segmented gradient in 120 min from 5 to 40% solvent B (95% acetonitrile with 0.1% formic acid) at a flow rate of 300 nl/min. Solvent A was 0.1% formic acid in water. The samples were maintained at 4 °C in the autosampler. The LTQ Orbitrap was operated in the positive ion mode with the following acquisition cycle: a full scan (m/z 350∼1600) recorded in the Orbitrap analyzer at resolution R 60,000 was followed by MS/MS of the 10 most intense peptide ions with collision-induced dissociation-high energy collision-induced dissociation acquisition of the same precursor ion. Collision-induced dissociation was done with collision energy of 35%. high energy collision-induced dissociation-generated ions were detected in the Orbitrap with collision energy of 45%. Two search algorithms, Mascot (version 2.2.06, Matrix Science) and SEQUEST, which is integrated in Proteome Discoverer software (version 1.3.0.339, Thermo Scientific), were used to simultaneously identify and quantify proteins (supplemental Fig. S2). Searches were made against the Arabidopsis protein database (TAIR10 20110103, 27416 sequences; ftp://ftp.arabidopsis.org/home/tair/Sequences/blast_datasets/TAIR10_blastsets/TAIR10 pep 20110103 representative gene model) concatenated with a decoy database containing the randomized sequences of the original database. Peak list data (MGF) files used for database searches were generated from Xcalibur raw files using a program in the MassMatrix conversion tools (v. 1.4, http://www.massmatrix.net). The protein sequences in the database were searched with trypsin digestion at both ends and two missed cleavages allowed, fixed modifications of carbamidomethylation at Cys, variable modifications of oxidation at Met and iTRAQ 4plex at Tyr; peptide tolerance was set at 10 ppm, and MS/MS tolerance was set at 0.8 D. iTRAQ 4plex was chosen for quantification during the search simultaneously. The search results were passed through additional filters, peptide confidence more than 95% (p < 0.05), before exporting the data. For protein quantitation, only unique peptides were used to quantify proteins. These filters resulted in a false discovery rate of less than 1% after decoy database searches were performed. For biological repeats, spectra from the two technical repeats were combined into one file and searched. MS/MS spectra of single peptide-based identifications are deposited in the public proteome database Tranche (ProteomeCommons.org) under the entry name TfZmsyU6n. Proteins identified and quantified by at least in two biological repeats were considered to further analyze the abundance change in response to Pi deficiency using a method described by (32Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9214) Google Scholar). In brief, the log2 ratios of the 7026 quantified proteins overlapping in at least two biological repeats were calculated and analyzed for normal distribution. For a given protein in one biological repeat, the ratio was calculated as the reverse log2 of the median of the log2 value of all peptide ratios and averaged across the biological replicates. Next, mean and S.D. were calculated and 95% confidence (Z score = 1.96) was used to select those proteins whose distribution was far from the main distribution. For the down-regulated proteins, the confidence interval was –0.263053843 (0.052103724, mean ratio of the 7026 proteins) –1.96 × 0.160794677 (S.D.), corresponding to a protein ratio of 0.83. Similarly, for the up-regulated proteins, the confidence interval was calculated (mean ratio +1.96× S.D.), corresponding to a protein ratio of 1.29. Protein ratios outside this range were defined as being significantly different at p = 0.05. Total RNA was extracted from roots grown under control and Pi-deficient conditions using RNeasy Plant Mini kit (Qiagen). Equal amounts of RNA collected from three independent experiments and used for sequencing. cDNA libraries for sequencing were prepared from 5 μg of total RNA following protocols provided by the instrument manufacturer (Illumina). The cDNA libraries were enriched by 15 cycles of PCR amplification. The resulting cDNA libraries were sequenced on a single lane per sample of an Illumina Genome Analyzer II. The RNAseq and data collection followed published protocols (33Mortazavi A. Williams B.A. McCue K. Schaeffer L. Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq.Nat. Methods. 2008; 5: 621-628Crossref PubMed Scopus (9884) Google Scholar). The length of the cDNA library ranged from 250 to 300 bp with a 5′-adapter of 58 bp and a 3′-adapter of 63 bp at both ends. The fragment length of the cDNA ranged from 129 to 179 bp. Adapters were trimmed from reads and ∼20.0 M 80-mers reads for each sample from treated and untreated roots were obtained in three replicates. Reads were aligned to the TAIR10 genome using the BLAT program (34Kent W.J. BLAT–the BLAST-like alignment tool.Genome Res. 2002; 12: 656-664Crossref PubMed Scopus (6179) Google Scholar) with minimum 95% identity, but only the highest identity for each read was considered for mapping. Multireads were distributed in proportion to the number of unique and splice reads recorded at similar loci using Enhanced Read Analysis of Gene Expression (ERANGE) strategy (33Mortazavi A. Williams B.A. McCue K. Schaeffer L. Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq.Nat. Methods. 2008; 5: 621-628Crossref PubMed Scopus (9884) Google Scholar). RPKM values (reads per 1Kbps of exon model per million mapped reads) were computed as described in (33Mortazavi A. Williams B.A. McCue K. Schaeffer L. Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq.Nat. Methods. 2008; 5: 621-628Crossref PubMed Scopus (9884) Google Scholar). A transcript was defined as present when it was detected with at least five reads in at least two experiments within one growth type (i.e. either treated or untreated). A gene was defined as differentially expressed if the p value for the comparison of the RPKM values between samples from treated and untreated plants was < 0.05 based on Student’s t test. Phosphate deficiency-induced changes in the proteome of Arabidopsis roots were quantitatively cataloged using the iTRAQ technology. Proteins were extracted, digested in solution, and iTRAQ-labeled peptides were analyzed by liquid chromatography combined with tandem mass spectroscopy on an LTQ Orbitrap with higher-energy collisional dissociation (HCD) and collision-induced dissociation capabilities. This combination allows for an accurate identification of the peptides and precise quantification of the iTRAQ label with a wide scan range. Two search algorithms, Mascot and SEQUEST, were used to identify proteins. Using this strategy, we identified 57,153 unique peptides from 1,534,861 spectra, corresponding to 17,007 proteins (10,794 proteins/protein groups) in the three experiments (Fig. 1A, supplemental Data Sets S1, S2, S3). A subset of 13,298 proteins was identified by at least two peptides or in two experiments, 10,256 proteins were identified in all three experiments (Fig. 1A). Robustness of the analysis is supported by the nearly identical SCX chromatograms of the three samples (supplemental Fig. S1). Pi deficiency-induced changes in protein abundance values showed a normal distribution (Fig. 1B), from which cutoff values for proteins with significantly different expression at p < 0.05 were calculated (Pi-deficient/control plants ratio of ≥ 1.29 for induced proteins and ≤ 0.83 for repressed proteins). Using this criterion, 356 proteins from a total subset of 7,026 quantified proteins was defined as differentially expressed upon Pi deficiency (Fig. 1C; supplemental Data Set S4). The highest increa