Proteomics in Deciphering the Auxin Commitment in the Arabidopsis

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Review

Proteomics in deciphering the auxin commitment in the Arabidopsis thaliana root growth Benedetta Mattei, Sabrina Sabatini, and M. Eugenia Schininà J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr400697s • Publication Date (Web): 14 Sep 2013 Downloaded from http://pubs.acs.org on September 19, 2013

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Journal of Proteome Research

Proteomics in deciphering the auxin commitment in the Arabidopsis thaliana root growth Benedetta Mattei*#, Sabrina Sabatini#, M. Eugenia Schininà§ #

Dept. Biology and Biotechnology, Sapienza University of Rome, Via dei Sardi 70, 00185 Rome,

Italy §

Dept. of Biochemical Sciences, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy

KEYWORDS: root proteome; A. thaliana; auxin; plant systems biology Corresponding Author: M. Benedetta Mattei Dept. Biology and Biotechnology, Sapienza University of Rome Via dei Sardi 70 00185 Rome Italy email address: [email protected] Telephone: 0039 06 49917796 Fax: 0039 06 49912446

ABBREVIATIONS 2DE, two-dimensional gel electrophoresis; 2DLC, two orthogonal liquid chromatographic peptide separation; DIGE, Difference In Gel Electrophoresis; FACS, Fluorescent Activated Cell Sorting methodology; GFP, green fluorescent protein; HILEP, Hydroponic Labeling of Entire Plants; IAA, indole-3-acetic acid; ICA, independent component analysis; ICAT, Isotope-Coded Affinity Tags; IMS, Imaging mass spectrometry; iTRAQ, isobaric Tags for Relative and Absolute Quantification; ACS Paragon Plus Environment

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MS, mass spectrometry; MudPIT, multidimensional protein identification technology; PCA, principal component analysis; SILAC, Stable Isotope Labeling by Amino Acid in Cell Culture; SILIP, Stable Isotope Labeling In Planta; SMIRP, Subtle Modification of Isotope Ratio Proteomics; TCA, trichloroacetic acid; vMALDI-LTQ, vacuum matrix-assisted laser desorption ionization-linear ion trap.

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ABSTRACT: The development of plant root systems is characterized by a high plasticity, made possible by the continual propagation of new meristems. Root architecture is fundamental for overall plant growth, abiotic stress resistance, nutrient uptake and response to environmental changes. Understanding the function of genes and proteins which control root architecture and/or stress resistance will contribute to develop more sustainable systems of intensified crop production. To meet these challenges, proteomics provides the genome-wide scale characterization of protein expression pattern, subcellular localization, post-translational modifications, activity regulation and molecular interactions. In this review, we describe a variety of proteomic strategies that have been applied to study the proteome of the whole organ and of specific cell-types during root development. Each has advantages and limitations, but collectively they are providing important insights into the mechanisms by which auxin structures and patterns the root system, and on the interplay between signaling networks, auxin transport and growth. The acquisition of proteomic, transcriptomic and metabolomic datasets of the root apex on the cell scale has revealed the high spatial complexity of regulatory networks, and fosters the use of new powerful proteomic tools for a full understanding of the control of root developmental processes and environmental responses.


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INTRODUCTION

Root architecture is a fundamental aspect of plant productivity, with implications for the overall plant growth rate and yield. As many soil resources are unevenly distributed, or are subject to localized depletion, the spatial order of the root system determines by a large extent the ability of a plant to exploit them. Furthermore, the root ability to optimise growth for a local environment provides an advantage to plants, as habitats are altered by the changing climate and environmental pollution. This high phenotypic plasticity is possible because root architecture is a complex system composed of primary, lateral and adventitious roots. The knowledge of how root systems develop in crops requires a deeper insight into the molecular networks that drive root growth and branching. With the advent of intensive fertilisation-based agricultural schemes over the past decades, the impact of the root system on plant performance has been largely neglected in crop breeding, and root architecture has not been specifically selected for by plant breeders of major crops. Instead, breeders have focused almost exclusively on aerial traits since the Green Revolution in the 1960s. The need to improve nutrient use efficiency in crops by manipulating root architecture is, however, becoming increasingly urgent. In the developed world, such improvement would ensure that agriculture becomes more sustainable through reduction of chemical inputs. In the developing world, farmers who have limited access to fertilisers could increase yields to keep pace with population growth. Thus current efforts strive to minimise chemical inputs, in order to improve the sustainability of agricultural production: gaining insight on how and to what degree root system architecture impacts the shoot performance is of paramount importance, as root architecture critically influences nutrient uptake efficiency. The potential impact on world agriculture is such that a “Second Green Revolution”, focused on root architecture, should be made a priority for plant biology. To this end, a systems-level characterisation of root development and its interaction with the environment, and consequently control of the overall architecture of the root system, are imperative for improving crop performance. The development of plant root systems depends on the activity of localized regions called ACS Paragon Plus Environment

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meristems. Within the meristem, stem cells self-renew and produce daughter cells that differentiate giving rise to different organ structures.1 Factors which affect the initiation and activity of meristems will clearly have a major effect on the root system architecture, and determine its ability to explore the distinct spatial domains in the soil, as well as to respond dynamically to the localized availability of soil resources.2 In the last ten years, large-scale genome-based projects have demonstrated that many aspects of root development (e.g., cell division and differentiation, cell fate, root elongation, gravitropism) depend on the local concentration, and redistribution of the plant hormone auxin.3, 4 Many aspects of root development, from the formation of an embryonic root to the control of root architecture, are coordinated by subtle spatial differences in the concentrations of auxin and by a complex crosstalking with other hormones exspecially cytokinin .5-12 The study of root development is challenging because roots are inaccessible and observation often requires invasive measures. Knowledge on root development has greatly advanced through the use of the model organism Arabidopsis thaliana. The simple structure of the Arabidopsis root makes it ideal for studying developmental events and investigating cellular functions. Moreover, Arabidopsis seedling can be easily grown in non-soil media, which facilitates phenotypical analyses and control of growth conditions. In Arabidopsis asymmetric auxin/IAA signaling can be seen by genetics and molecular biology as early as the two-cell embryo, where it is confined at the basal daughter cell. In the post-embryonic root, an auxin response maximum arises in the stem cell niche acting as a distal organizer of root development controlling cell division and cell polarity.5 Auxin activities in the root meristem is antagonized by cytokinin which promotes cell differentiation by repressing both auxin signalling and transport.13 This knowledge is mainly based on the paradigmatic application to A. thaliana of several advanced genetic and molecular methodologies. In particular, highly comprehensive RNA-chips are available today for hybridization-based gene expression studies, which have allowed the integration of large ACS Paragon Plus Environment

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sets of data to infer gene regulatory networks.14, 15 More recently, RNA-seq technology is providing genome-scale transcriptional maps of the genes involved in the responsiveness of the A. thaliana root to environmental stimuli.16-19 Moreover, well-characterized transgenic lines expressing green fluorescent protein (GFP) in specific cell populations have been made available. These lines allow cell-type specific molecular profiling via Fluorescent Activated Cell Sorting (FACS) methodology. For instance, application of mass spectrometry–based measurements of the intracellular concentration of indole-3-acetic acid (IAA) recently allowed quantification of the intracellular auxin concentrations for each FACS sorted cell type in the Arabidopsis root apex. These results provided direct evidence that local biosynthesis and polar transport combine to produce an auxin gradient having a maximum in the stem cell niche of the root tip.20 In the present -omics era, a critical boost to the knowledge of gene networks involved in the auxin control of the root shaping has been achieved with the transcriptional profiling of nearly all cell types in the root.21-23 To date a transcriptional expression map of the Arabidopsis root at specific cell type resolution, generated from 13 longitudinal sections of the organ, is available. In a recent study, Lan et al. used the RNA-seq technology for an exhaustive comparison of transcriptome and proteome data sets in root tissues.24, 25In general, poor correlation is observed for strongly downregulated proteins and their cognate transcripts, suggesting that biochemical conclusions based solely upon data from transcript profiling is inappropriate. The rapid expansion of mass spectrometry (MS) in biopolymer analyses, as well as the development of enrichment strategies and efficient protocols for isolation of subcellular protein fractions, have eventually paved the way to proteomics as a reliable complementary approach to transcriptomics in studying the A. thaliana root biology. Two-dimensional gel electrophoresis (2DE) protein maps of the whole root extract have been largely used to obtain “user friendly” pictures of the effect of phytohormones, biotic and abiotic stresses on protein expression. However, these “first generation” proteomic analyses often identified repeatedly the same abundant proteins, irrespective of the process studied. Systematic studies ACS Paragon Plus Environment

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indeed revealed the limited dynamic range of 2DE-based proteomics.26 The development of “second generation” methodologies, mainly based on isotopic labeling for multiplex comparison of protein relative abundances, made proteomics reliable in dissecting the signalling gradients in the root compartment and in defining the complex molecular networks that underlie the auxin driven root shaping (for a survey of recent works, see Table I). In the present third generation era, proteomics is taking advantage of very high resolution mass spectrometers, software assisted quantitative comparison of multiplex unlabelled samples, and analytical strategies targeted on gene products expressed in few copies per cell. The library of Arabidopsis organ-specific proteotypic peptides currently available allows to expand quantitative correlation analyses to high-resolution surveys of metabolic or regulatory pathways, or even individual enzymes, in a targeted approach.27 The advent of cell-type specific approaches has enabled the acquisition of -omic datasets that reveal the high spatial complexity of developmental processes and environmental responses in roots. In this review the results achieved so far by proteomics on the whole organ and on specific root cell-types will be surveyed. Finally, recent results and perspectives towards comprehensive proteome, transcriptome and metabolome signatures of all specific cell types in the root apex will also be addressed.

2. BIOMARKERS FOR ROOT DEVELOPMENTAL PROCESSES IN A. thaliana Complete annotation of genomes has advanced our knowledge on the molecular basis of life, providing information on the potential protein-coding capacity of biological systems. Since proteins are the main effectors of biological functions, systems biology projects must integrate the qualitative and quantitative profiling of their expression. Organ specific maps of protein profiles, possibly including post-translationally modified products, are the expected achievements in proteomics surveys in plant biology. The main analytical challenge is the sensitive detection and quantification of the minute amounts of proteins collected from a few root cells. Gel-based mapping ACS Paragon Plus Environment

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A map of the proteome expressed in the A. thaliana root has been achieved in 1995 through the “user friendly” 2DE approach. Protein spots observed in the published bidimensional map were deposited in the first proteomic database of this plant organ, indexed by their pI and MW values, while only twelve spots were identified by their partial N-terminal amino acid sequence.28 Although the success of this first proteomic analysis could be nowadays considered very limited with respect to its time- and sample-consuming procedures, the straightforwardness of this first map laid the foundation for the early applications of 2DE-based proteomics in Arabidopsis biology, particularly in a comparative manner. 29-33 In the last years, the software assisted spot volume comparison among the 2DE protein maps have been largely employed in deciphering the pleiotropic effects that a single stress agent or a single gene mutation can have on the developmental and environmental commitment in the Arabidopsis root.34-37 Since membrane proteins are expected to play key roles in modulating the signaling pathways which regulate cell-cell interactions and responses to differential inputs, the microsomal proteome of the A. thaliana root has been widely explored using the classic 2DE-based proteomics strategy, looking in protein maps for receptors, channels, and membrane-associated molecules.3842

2DE-based proteomics has been also employed in establishing the effects of auxin-dependent

developmental commitment in A. thaliana roots and in identifying valuable markers that might be used for the future identification of genotypes with better rooting capability. A first large-scale analysis of differences in protein expression among several mutants (in which overexpression of auxin was correlated with abnormalities in the adventitious rooting process that takes place at the hypocotyl level) has been accomplished in 2006 by the Bellini’s group,.43 In a reference map encompassing 1,147 proteome components from six different genotypes (wild-type (Col-0) and (Ws) seedlings, ago1-3, sur1-3 and sur1-2, and the sur2-2ago1-3 double mutant), qualitative and quantitative variations of 192 2DE spot volumes were displayed with the assistance of advanced image analysis software. Correlation of the spot changes with the number of adventitious roots and/or the endogenous auxin content resulted in the identification of only 11 gene products. These ACS Paragon Plus Environment

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genes were already predicted to be involved in regulatory pathways associated with adventitious root formation, including the regulation of auxin homeostasis via GH-like proteins.44 Since the activity of the PIN-FORMED (PIN) membrane transporters is considered a key element for auxin redistribution in the root meristem,45 in the search for biomarkers of the transmission of gravity signals a comparison between the proteomes of pin-2 mutant and wild type A. thaliana roots was performed by analysing the 2DE-maps of the root tip protein extracts from seedlings subjected to different gravitational conditions.46 About 1,300-1,500 protein spots were reproducibly detected and reliably quantified in silver-stained 2DE gels, and 25 gravity-responsive proteins were identified, with differential patterns of protein expression that correlated with the altered gravity conditions. More recently a 2DE comparison among different hormonal treatments on A. thaliana roots has been preferred in disclosing the molecular interplay between auxin and ethylene affecting root elongation at the meristem level.47 Even if several hundreds of proteins have been encompassed in the master image representing all gels from differentially treated Arabidopsis roots, only nine among the identified spots displaying a significant volume change (2-0.44 ratio), could be related to auxin. Nevertheless, from this highly specific signature, authors could infer - partially confirming previous transcriptomic data - direct pleiotropic effects of auxin on the glucose metabolism (glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase 1) and CO2 scavenger activity (large subunit of RuBisCO). Moreover a complex control of IAA on the root cell duplication could be verified by changes in abundance of protein components of the translational(eEF-1B gamma 2 transcriptional elongation factor and TCP-1 chaperonin), cytoscheleton- (tubulin complex TUA2/TUA4), cell wall synthesis- (rhamnose biosynthesis 1) apparata. Clues have been also provided on polysaccharide cross-talking (adenosine kinase 2 and enzymes of the cell wall synthesis). The auxin signatures proposed by 2DE approaches (surveyed in Table II) encompass several translational products of genes for which a role in the regulation of auxin homeostasis had been already established by genetic studies (e.g., proteins of the GH3 family). On the other hand, proteins ACS Paragon Plus Environment

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involved in other biological processes have been also innovatively added by proteomics in the inventory of biomarkers of the diverse root developmental processes. Overall, these novel results confirm at the protein level that distinctive molecular frameworks in root morphogenesis link a proper auxin distribution with specific arrays of biochemical processes, including primary metabolism, reorganization of the cytoskeleton, vesicle trafficking, cell wall reassembly, production of reactive oxygen species, assistance in protein folding and in cellular protein machinery assembly. For instance, the presence of enzymes involved in metabolic pathways among the auxin-signatures related to adventitious root primordia formation, confirms at the proteome level a biochemical link in root development between the light responsiveness and the auxin signaling.43 Proteome signatures from roots treated with exogenous auxin application lead to an intriguing hypothesis of the involvement of cell wall degradation products in the auxin-signaling networks.47 Crosstalking in root growth regulation between auxin and abscisic acid could be biomarked by the expression at the protein level of the aspartic proteases ASPG1.48 In conclusion, the first generation of proteomics has proven the classical 2DE approach to be a friendly method also in assessment of the proteome status for the A. thaliana root. However, the main limits of a gel-based approach - e.g. the poor representation of highly acidic/basic proteins, limited dynamic range, difficulties in displaying and identifying low-abundance and proteins with extreme size, comigration of multiple proteins in a single spot - make it not suitable to achieve high density profiles and to detect differences in low abundance protein expression. Moreover, plant seedlings yield low amount of proteins per fresh weight, and are not particularly suitable for isoelectrofocusing due to the high abundance of non-proteinaceous contaminants, such as polyphenols, lipids and organic acids. As a consequence, 2DE root proteome profiles resulted to be highly protocol- and technology-dependent, and required desalting steps such as trichloroacetic acid (TCA) precipitation or acetone treatment as prerequisite to obtain a well-resolved 2DE gel pattern of the root proteomes.49 LC-MS/MS profiling ACS Paragon Plus Environment

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More recently, the development of high resolution mass spectrometers allowed to reach comprehensive A. thaliana proteome profiles by 2DE-free platforms such as the multidimensional protein identification technology (MudPIT50). In fact, shotgun proteomics provided very large data sets of detectable peptides that can unambiguously identify a protein and which can be used for targeted quantitative approaches (see next paragraph). In 2008 a high-density map of the Arabidopsis proteome was assembled and deposited in the PRIDE database (http://www.ebi.ac.uk/pride/), that represents the most comprehensive and detailed map of Arabidopsis organ-specific proteotypic peptides. Quantitative pairwise comparison of the differential protein accumulation highlighted functional specialization between different plant organs and determined “organ-specific biomarkers”.51 In particular, among the approximately 5,159 protein signatures identified in 10 day old roots, and 4,466 in 23 day old roots, intracellular protein transport, response to oxidative stress, and toxin catabolic process were included, even though the proteome profile was overwhelmed by proteins involved in basic metabolic processes and by high molecular weight components. In compliance with the current standards for proteome data deposition (MIAPE52), MS/MS spectra achieved through the LTQ (linear trap quadrupole) ion-trap mass spectrometry were made available and updatable (www.ebi.ac.uk/pride/prideMart.do, www.AtProteome.ethz.ch, pep2pro.etch.ez). The new era of high-throughput characterization of the A. thaliana root system had definitively started.53-57 Nowadays several highly comprehensive inventories of root proteins, encompassing also a significant number of signalling components are available (Table I). However, even if gel-free approaches finally overcame some limitations of the gel-based root proteomics, these high density profiles are not largely overlapping with each other. Moreover, the high-sequence identity shared by the large variety of known plant transporters, makes a proteolytic peptide-based proteomics unable to truly assess their involvement in root biology. Expression and activity of post-translational modification enzymatic machineries (e.g. acetyltransferases) could be often only inferred by the presence in the MS/MS spectra of the resulting modified peptides. In addition, shotgun proteomics could be a valuable tool in the ACS Paragon Plus Environment

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correction of genome data, revealing unexpected coded regions (e.g. splicing variants) from the MS-determined peptide sequence. In conclusion, even if shotgun has improved significantly in the past few years the list of A. thaliana root biomarkers, this inventory is still far from complete, and novel, uncharacterized proteins and protein isoforms remain to be identified. In this dataset, the expression of gene products known to be auxin-related has been largely underrepresented, until shotgun proteomics and very high resolution MS techniques, aimed to achieve a very high-density proteome profile at the cell-type level, was fielded by Benfey’s group, (Table III). One million sorted root cells from each of six GFP marker lines were used to profile their proteomes by 1DE/LC-MS/MS.58 By this tremendous effort, utilizing an efficient highthroughput platform the authors expanded the number of biomarkers needed for a robust A. thaliana root signature. Moreover, high-density and highly specific cell-type proteomes were deciphered, encompassing many auxin-related proteins. In this comprehensive shotgun data set by Benfey's group, the isoforms of PIN auxin transporters are differentially expressed in this zone. Moreover, some of the novel biomarkers of auxin-driven growth processes mapped by 2DE-based proteomics (e.g., RHM1 protein) are shown to be specifically enriched in the QC, columella and vasculature (Table II). A total of 129 gene products differentially expressed between root hairs and non-root hair tissue have been surveyed by Lan et al.25 These results will represent not only an indispensable protein inventory for the Arabidopsis community, but also a true support for integrated studies of the Arabidopsis root system biology.

3.

WEIGHTING DIFFERENCES IN A. thaliana ROOT CELLS.

The increase in accuracy and sensitivity of the “second generation” proteomics technologies allowed the comparative profiling of protein expression by adding a quantitative dimension to proteomic data. Several strategies have been developed to obtain a precise measurement of the protein copies actually expressed in cells (absolute quantification) or of the temporal changes in the proteome (relative abundances). Quantitative proteomic approaches have been initially based on ACS Paragon Plus Environment

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isotopic labeling of samples and comparison between signals in the same mixture with high sensitivity and accuracy.59 However, the peculiar features of plants genome and physiology are currently posing further challenges and bottlenecks in quantitative proteomics. Protein extraction from plant tissues often involves complex, multi-step procedures of cell wall disruption and removal of non-proteinaceous contaminants as polysaccharides, polyphenols and nucleic acids. Targeted fractionation is often required to challenge the large dynamic range of protein abundances in plant proteomes. Therefore, quantitative proteomics requires a careful experimental design further validated by several replicates.60 At present, quantitative analysis of plant proteomes has been less prominent compared to human, animal and microbial applications, even in the presence of completed and well-annotated genome sequences. So far, auxin-induced root apex elongation seems to be yet unattainable for labeling-based quantitative proteomics, particularly with respect to the transcriptomics datasets. Here we give an overview of the main challenges encountered in weighting the responsiveness of the A. thaliana root proteome. Labeling-based proteome comparisons The DIGE (Difference In Gel Electrophoresis) approach is the direct implementation of the DEbased proteomics at a more accurate and sensitive quantitative level. A very robust protein spot comparative quantification overcomes the protein ratio errors arising from a low gel-to-gel reproducibility of the traditional 2DE approach. Due to its robustness, DIGE has been applied in plant biology to infer precise functional information from difference in protein abundances61 or from differential protein modifications as in the case of phosphoisoforms.62 Moreover, since DIGE is based on orthogonal electrophoretic separations, the first resolution principle employed could be selectively chosen according to the sample targets, as in the combination of Blue native DIGE used to achieve stoichiometric determination of membrane complexes involved in respiratory, photosynthetic, transport and signalling functions.63 On the other hand, DIGE applications to the A.


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thaliana root proteome are still limited, probably being hampered by the high amount of plant material necessary to detect low abundance proteins.64 As MS signals are notoriously variable, and since consequently MS analyses are not inherently quantitative, accurate information on the relative abundance of identified protein components has been first approached by metabolic labeling of whole proteomes with stable isotopes.65, 66 In this scenario, plants show the evident advantage over animal models to be organisms that can be grown in simple and very controlled growth conditions and nutrient supplementation. These features in fact have offered evident opportunities for global isotope labeling procedures. Differently from animals, 14N/15N salts can be used to directly label plant proteomes in controlled growth conditions, as in Subtle Modification of Isotope Ratio Proteomics (SMIRP,67), in Stable Isotope Labeling In Planta (SILIP,68), and in Hydroponic Labeling of Entire Plants (HILEP,69) methods. A quantitative profiling of the Arabidopsis root proteome has been achieved in plants hydroponically grown and then subjected to heat stress. 707 proteins have been detected and identified by shotgun proteomics trough high-resolution MS analyses and several replicates, and approximately two hundreds of them showed a change in abundance when plants were submitted to heat stress. The use of high resolution mass spectrometry and advanced data mining software are mandatory to resolve the higher complexity arising from labeling with 15N the entire protein backbone in addition to all nitrogen-containing amino acid side chains.70 More recently, with the aim to dig into the low abundance components of the auxin-responsive A. thaliana root proteome, 15N based metabolic labeling was coupled with phosphopeptide enrichment.71 Among the 3,068 phosphopeptides identified as changing in abundance during the IAA-induced pericycle cell divisions and lateral root formation, accurate signatures of key proteins already known to be involved in the auxin responsiveness (e.g., MDR and PIN2 auxin carriers, AUXIN RESPONSE FACTOR 2, SUPPRESSOR OF AUXIN RESISTANCE 3 and SORTING NEXIN 1) have been resolved. The eight differentially regulated proteins considered, by the authors, relevant in root auxin-signaling have been surveyed in Table II. Moreover, since kinase activity is relevant in signaling, the twenty ACS Paragon Plus Environment

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proteins differentially phosphorylated have been also included. An alternative to the use of 14N/15N salts is represented by amino acid labeling during protein synthesis, as in the Stable Isotope Labeling by Amino Acid in Cell Culture (SILAC) approach. Auxotrophic cell cultures are grown in defined media complemented respectively with naturally occurring essential amino acids or their synthetic variants containing a specific number of stable isotope atoms. The peak intensities of the heavy and light peptides are then compared to determine the differential protein expression in one sample relative to the other sample. SILAC is actually a powerful approach in yeast and mammalian cell culture studies, both in gel-based or gel-free proteomic approaches. In any case, an accurate evaluation of the stoichiometric metabolic incorporation of heavy amino acid in proteins is strictly required.72 On the other hand, autotrophy in plant cells limits the applications of SILAC in plant proteomics to conditions of the cultured cells, in which amino acid biosynthesis pathways are feedback-regulated.73 As a consequence, SILAC has been so far employed in the differential analysis of the A. thaliana root proteome only in a case-study on the response to abiotic stress induced by salycilic acid.74 Besides metabolic labeling, several chemical labeling methods using light or heavy versions of an isotopic tag have been described in the last years, as in the original Isotope-Coded Affinity Tags (ICAT)75 or the popular iTRAQ (Isobaric Tags for Relative and Absolute Quantification) methods, allowing multiplex analyses.76 By the use of these approaches (reviewed in77), nowadays quantification of proteomes is expected to become a more attainable target, even if the detection of low abundance components such as signaling factors still poses challenges to these analyses. In the A. thaliana root proteome landscape, an iTRAQ labeling has been successfully applied to the quantitative comparison of the microsome proteome fraction by shotgun proteomics. The molecular network responsible for mutant phenotypes has been actually depicted at the protein level, with an increase of more than 500 proteins identified and quantified with respect to a parallel 2DE-based approach.53 Moreover, highly comprehensive inventories of differentially expressed proteomes upon hormonal or inorganic stimuli have been achieved by iTRAQ-based quantitative comparison ACS Paragon Plus Environment

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of whole root proteomes by two orthogonal liquid chromatographic peptide separation (2DLC) and peptide identification by ESI-Q-ToF MS/MS54, LTQ Orbitrap Velos25, 56, and LTQ-Orbitrap XL55 mass spectrometers. Beyond the increasing number of protein signatures picked out (720, 2,086, 2,447 and 4,454, respectively), relevant features in these profiles are i) the presence of low abundant signalling factors and of high molecular weight proteins, ii) a significant percentage of the root proteome components displayed a larger range in protein fold change. Even if these high-throughput data sets are not related to auxin-induced proteome changes, large and comprehensive data sets of unambiguous peptide spectra acquired by LC-MS/MS shotgun proteomics and quantification of the relative proteins are a mandatory prerequisite as source for deciphering physiologal and molecular mechanisms of the complex root developmental biology by the emerging “targeted quantitative proteomics” (see next paragraph). In fact, perturbated states could be selectively analysed in terms of signatures of the proteolytic peptides from targeted proteins through sensitive and accurate MS methodologies (e.g., SRM, signal recognition monitoring), allowing to infer differential expression information at the level of few copies of protein products.

Label-free proteome comparisons More recently, label-free LC/MS quantitative techniques have emerged as an attractive alternative to isotope coding, because they do not require special labeling chemistries or growth conditions, and are amenable to complex experimental designs with any number of treatment groups.60 Peak intensity-based and spectral counting-based approaches78, 79 are becoming eligible tools also in the A. thaliana root proteome studies (Table I). A shotgun approach allowed Samaj's group to achieve a relative quantification of differentially expressed root proteins without previous labeling procedures.34-35 Besides providing a complementary approach to the 2DE-based protein identification, normalization of 2DE/LC-MS/MS signals on total sampling in biological groups and replicates revealed proteins that were up- and down-regulated upon activating vesicle trafficking unbalances. A simultaneous qualitative and quantitative strategy based also on a powerful data-


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independent statistical analysis software (MSE) has been fielded by Petrika et al. to identify proteins enriched in single cell types and calculate the correlation between protein and mRNA expression profiles.58 Lan et al. catalogued differences in the abundance of mRNAs and proteins by integrating RNA-sequencing profiling of gene activity and high-resolution quantitative iTRAQ proteomics.24 In the new generation proteomics, improved sensitivity is being achieved by focusing MS analyses on specific m/z values of predefined peptides and fragments. This “targeted quantitative proteomics”80, 81 is strongly dependent on the availability of a large dataset of unambiguous peptide spectra acquired by LC-MS/MS shotgun proteomics.82, 83 High-throughput shotgun strategies and label-free quantitative approaches on root tissues will support a targeted proteomics of auxin-related molecular networks. Recently a targeted proteomics approach, aimed at identifying and quantifying within single experiments several key proteins of the A. thaliana root membrane functionality, achieved an accurate profile of several members of the aquaporin (PIPs) , autoinhibited H+ATPases, ammonium transporters, and nitrate transporter (NRTs) families. The same setup was used to investigate the effect of salt stress on the expression of the latter 20 transporters in roots.84 A targeted proteomics survey was also attempted on samples from Arabidopsis for uncovering changes in the global phosphorylation state of the entire proteome upon iron depletion85.

4.

MASS SPECTROMETRIC IMAGING OF PHYTOHORMONE GRADIENTS IN A.thaliana.

In proteomics, profiles of enzyme expression and localization can be used to infer the actual biosynthesis of morphogenic signaling metabolites. Complementarily, high resolution maps of regulatory proteins (transcriptional factors and/or signalling protein components) are mandatory in the description of the sophisticated networks between signalling, transcription and metabolome, inside and among cells. These results, however, cannot be conclusive to assess the final location where post-synthetic accumulation of metabolites occurs. Mapping of specific metabolite gradients is therefore critical in studying the development of shaping in plant biology. ACS Paragon Plus Environment

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In the case of plant biomolecules provided with spectroscopic properties this analytical goal can be achieved by live cell imaging for extended periods of time without requirement of other fluorophores. For instance, fluorescent and antioxidant properties have been used as a powerful tool for allowing anthocyanin gradient determination in grape bunches.86 Alternatively, metabolite distribution profiles have been accomplished by immunohistochemical approaches, although antibodies for all metabolites have not yet been developed.87 Even though sensitivity and analytical accuracy are features that make mass spectrometry an eligible approach in this field, very often MSbased metabolomics profiling has been performed in the whole-tissue homogenate. This leads to the complete loss of information related to signalling molecules confined to a relatively small area of the plant tissue. Imaging mass spectrometry (IMS) is an emerging tool for determining and visualizing the simultaneous distribution in situ on intact tissue of a wide range of biomolecules, without requirement of antibodies, staining, or complicated pre-treatment steps. According to the classical approach, in IMS a thin section of the sample is moved in predefinited x-y coordinates and thousands of position-dependent mass spectra are achieved from a small spot by desorption with a pulsed laser energy (typically ~0.1 J/cm2 pulse) across the target. Maps of specific m/z value distributions of the detected ions are then obtained by extraction of a targeted data set and transformed in rast images.88, 89 These maps are then typically analyzed by statistical methods such as principal component analysis (PCA) or hierarchical clustering, in order to detect areas in which diverse concentrations can be recorded. This innovative technique has been shown to be an excellent approach in physio-pathological imaging of proteins, peptides, amino acids, glycolipids, lipids from animal tissues.90 Nowadays recent improvements applied in IMS hold promise for its application also in the study of plant metabolites at the tissue- or cell-scale (reviewed in91, 92). However, several drawbacks at the technical level (e.g., in sample mounting, matrix selection and deposition, laser pulse spatial resolution, and tissue stability) limit its usefulness as routine metodologies in plant developmental biology, and specifically in root shaping. Submicrometer MS ACS Paragon Plus Environment

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imaging of potassium and calcium has been performed on pine tree roots by Spengler and Hubert.93 More recently, a LTQ-Orbitrap with a MALDI ion source was adopted to achieve MS imaging of the C18 fatty alcohol caffeate ester in A. thaliana root at high mass resolution.94 In this latter work, a spectacular single cell resolution was achieved thanks to an optimised sample attachment method, an accurate and homogeneous matrix deposition, image acquisitions with a high resolution linear trap mass spectrometer equipped with a vacuum assisted MALDI source (vMALDI-LTQ), and particularly the use of a laser spot size of 12 µm. These technical advancements allowed also to use vMALDI-LTQ imaging of the root surface in a comparative manner, and relative amounts of the selected metabolites could be determined between wild-type and individuals carrying mutation in epicuticular lipid biosynthetic pathway. Integration of the well-established fluorescent labeling of single cell types and high accuracy in isotopic resolution by MS could be the key step towards an application of IMS for an in depth structural characterization of the auxin gradient in vivo.

5.

DATA INTEGRATION

A full understanding of metabolic networks requires the integration of quantitative data about transcript levels, protein levels or enzyme activities and metabolite levels. Interactions among these three functional levels will depend on the structure of the metabolic and signalling network, and on the dynamics of transcript, protein and metabolite turnover. Normalization, analysis and display of multilayered data sets is very challenging. While considerable progress has been achieved for transcript arrays,95, 96 there is no consensus on normalization strategies for metabolites and/or proteins. Combined network analysis with implemented causality has been used to generate putative gene-metabolite communication networks97 and protein-metabolite networks.98 Different methods have been applied for integrating data from multiple sources such as univariate correlations, PCA, independent component analysis (ICA)99 and multivariate regression, such as the O2PLS platform which was shown to be very suitable to study the relationship between metabolomic and transcriptomic datasets.100 ACS Paragon Plus Environment

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The first comprehensive proteomic study in Arabidopsis that integrated the proteome profiles based on proteotypic peptides with gene expression data to reveal potential biomarkers for roots, flowers, leaves, seeds, siliques and cell culture was by Baereenfaller et al.51 An exhaustive comparison of RNA-sequencing and proteomics data sets on the differential gene and protein expression in root tissues under phosphate deprivation has been achieved with the help of iTRAQ technique by Lan et al. (2012).24 The same group provided an integrated and exhaustive measurement and comparison of mRNA and protein abundances caused by environmental perturbations.19 In all these genomewide scale comparisons, discordance between transcriptomics and proteomics datasets is discussed. Post-transcriptional regulation could account for the moderate correlation between genes and protein products. Comparing gene expression levels and protein abundance may be challenging due to the different mRNA and protein stability. Moreover, the observed changes in the protein abundance may be the result of post-translational regulation of the protein stability. However a full understanding of gene function and regulatory processes requires to consider the

integration of gene expression profiling with metabolomics assays. Metabolomics studies on multicellular organisms have generally been performed on whole organisms, organs, or cell lines, losing information about individual cell types within a tissue. The recent advances in the cell sorting technique have made it possible for the first time to quantitatively assess how the different cell types coordinate gene expression responses. The work by Petersson et al.20 explored the possibility offered by these techniques to measure metabolite (auxin accumulation and synthesis) and mRNA level, to understand the possible connection between transcriptome and metabolome at a cell type level. In a later work, Moussaief et al. used FACS to dissect GFP-marked cells from Arabidopsis roots for metabolomics analysis.101 They performed nontargeted metabolomics analysis of five Arabidopsis GFP-tagged lines representing core cell types in the plant root, to provide a metabolic map of the root organ. Fifty metabolites were putatively identified, with the most prominent groups being glucosinolates, phenylpropanoids, and dipeptides. Metabolites were differentially abundant across ACS Paragon Plus Environment

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root cell types and in many cases, this abundance did not correlate with transcript expression of enzymes or regulators in the corresponding biosynthetic pathways, suggesting non–cell-autonomous mechanisms responsible for their targeted localization. A full understanding of the control of root development requires to consider the interplay and feedback between signalling networks, hormone transport, mechanical properties and growth on the cell scale. Recently many studies which combine experimental data with computational modelling have been published; a series of modelling studies have investigated how cell-scale fluxes produce auxin gradient in the root tissue.102-104 Similar studies suggest that morphogenetic patterns emerge from complex interplaying dynamical processes, acting at different levels of organization and spatiotemporal scales. Barrio et al.105 have modelled a distribution of the concentration of auxins (normalized with its maximum value) similar to the one observed in real roots by Petersson et al.20. However, modelling studies of auxins spatio-temporal distributions taking into account also the root plasticity, in which cells divide, grow, and change shape, are still at their onset.

6.

CONCLUDING REMARKS:

Our understanding of the root biology has advanced dramatically in the last years, and genetic, proteomic and transcriptomic studies in Arabidopsis will continue to play a fundamental role in the molecular dissection of root developmental processes. Global profiling of genes and proteins have shown that complex intracellular gene regulatory networks at the supracellular scale, are fundamental for proper root growth and development. They also confirm the crucial role of auxin signaling and distribution during root development advancing our knowledge on the role of this hormone during basic developmental program such as cell division, cell differentiation and cell polarity.106 The advent of high-throughput methodologies for RNA sequencing and for identifying and quantifying expressed proteins is nowadays making possible comprehensive comparisons between trascriptome and proteome data also in the root


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landscape.25, 51, 58 Some recent comparisons have shown that the datasets on gene and protein expression partially correlate in plant roots. Nonetheless, their integration with interactomics25 and metabolomics101 data can provide opportunities for future studies aimed at understanding the mechanisms that guide developmental processes and tissue-specificity in environmental responses, which will eventually lead to new strategies for the design of improved, stress resistant plants. The field is moving beyond studying the individual gene products, aiming to determine the functional relationships between multiple components of regulatory pathways; mathematical modelling is going to become increasingly crucial as networks become more complex. As systems biology approaches will be increasingly utilized to collect high-resolution, spatio-temporal expression data for all the genes and gene products in root systems, it will be possible to model the dynamics of early events that prompt the auxin-signal to a diverse expression planning in close cells towards diverse structures and metabolisms to coordinate growth in response to a changing environment. The physiology of the entire plant organism rests on the root architecture as this provides the optimal anchoring and the pathway for incoming nutrients. Moreover, the environment is sensed by plants through their complex root systems. Since auxin is considered one of the major phytohormone in manipulating root development, its synthetic forms are commonly used in rooting powders by the horticultural industry. Therefore, a deeper knowledge on the complex regulatory networks that control auxin distribution and biological activities in different plant organs will be crucial in achieving better performance in the rooting of agronomically important plants. In this scenario, defining the proteome landscape of the auxin-dependent root biology is expected to provide detailed blueprints for genomics-assisted crop improvement.


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TABLE I Hallmarks in the A. thaliana root proteome profiling Sample Source Year (reference#)

Organ/Cell Type

2013 (56)

Cellular Fractiona

Constitutive Expression Identification Methodb Proteins (#)

UR

2,086

MudPIT MudPIT MudPIT MudPIT

Treatment abscisic acid;

2013 (57) 2013 (36) 2013 (71)

root hairs epidermis primary roots primary roots

UR UR UR PP

2,45 1,493

2012 (47)

primary roots

UR

-

-

2012 (24)

root hairs

UR

13,298

MudPIT

2012 (85)

primary roots root hairs non hair-epidermis cortex endodermis-quiescent vasculature columella

PP

425

MudPIT

cytokinins auxin auxin ethylene phosphate depletion iron depletion

UR

1,363 1,342 714 686 1,268 784

1DE-LC-MS/MS

2013 (25)

2012 (58)

-

3,266

Comparative Proteome Analyses Identification Methodb Genotypes Proteins (#) WT 161 MudPIT gtg1gtg2 64 129 MudPIT WT WT

DA SIL

2DE-ToF/ToF

DA

WT

356

MudPIT

iTRAQ

WT

45

MudPIT

iTRAQ

-

cpc1try82 wer1myb23-1

149 123

1DE-LC-MS/MS

DDA, MSE

16 75

WT

UR

857

MudPIT

wortmannin

WT

2012 (55)

primary roots

UR

4,454

MudPIT

iron

WT WT gpa1-4 WT det3-1 -

UR

2011 (53)

primary roots

MEM

2011 (64) 2009 (33)

primary roots primary roots 10 days roots 23 days roots primary roots

MYT RF

2008 (51) 2007 (32) 2006 (43)

adventitious roots

UR UR UR

720 521 519 5,159 4,466 -

DDA

2DE-LC-MS/MS MudPIT

primary roots

primary roots

iTRAQ

52 19 8 5

2012 (35)

2011 (54)

Comparative Methodc

MudPIT

abscisic acid

MudPIT

zinc

-

methyl jasmonate

1DE-LC-MS/MS

-

-

NaCl

-

ago1-3 sur1sur2.1

101

2DE-MALDITof/Tof MudPIT MudPIT

iTRAQ

MudPIT

iTRAQ

DA DDA

98 74 284 326 44 6

MudPIT

iTRAQ

2DE-LC-MS/MS 2DE-LC-MS/MS

DIGE DA

-

-

-

81

2DE-LC-MS/MS

DA

50

2DE-LC-MS/MS

DA

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sur2-1 ago1-3

a

2006 (31)

primary roots

MF

35

2004 (41)

primary roots

MEM

-

2DE-MALDI/ToF 2DE-MALDI/Q-ToF -

-

-

-

-

-

NaCl

WT

23

2DE-MALDI/ToF

DA

UR, unfractionated root cells; PP, phosphopeptide fraction isolated from unfractionated root cells; RF, redox proteome revealed by thiol-labeling; MF, copper-binding proteins fraction isolated from unfractionated root cells; MEM, microsomal fraction; MYT, mytochondrial fraction.

b

1DE-LC-MS/MS, proteins are resolved by SDS-PAGE and peptide mass fingerprints of selected slice determined by tandem mass spectrometry coupled to liquid chromatography; 2DE-MALDI/ToF, proteins are resolved by 2DE and peptide mass fingerprints of selected spots determined by MALDI-ToF mass spectrometer; 2DEMALDI/Q-ToF, proteins are resolved by 2DE and peptide mass fingerprints of selected spots determined by hybrid mass spectrometry coupled to a MALDI source; 2DETof/Tof, proteins are resolved by 2DE and peptide mass fingerprints of selected spots determined by MALDI-ToF/ToF mass spectrometer; MudPIT, multidimensional protein identification technology; iTRAQ, Isobaric tags for relative and absolute quantification ; DA, densitometric analysis of protein spot volumes; SIL, 15N,15N based metabolic labeling.; DDA, label-free comparison among several replicates coupled with a data dependent analysis of proteotypic peptides; MSE, label-free comparison among several replicates coupled with data independent LC-MS/MS analyses.


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Table II Proteomic signatures of auxin-signaling following distinct perturbation of A. thaliana root developmental processes Biomarkers of putative auxin-regolated networks gene locus

Proteomics Techniquec

Perturbated root developmental processreference #

Cell populationd

Microtubule-associated protein TORTIFOLIA1 (phosphorylated variant)

At4g27060

SG71

lateral root formation71

ND

Q9SZT9

GH3.2 IAA-amido synthetasee

At4g37390

SG71


ND

Q9SYM5

RHM1 rhamnose biosynthesis 1

At1g78570

2DE47, SG58

apical meristemal elongation47

cortex, vasculature (cluster 14)

Q9SII8

DSK2b Ubiquitin domain-containing protein

At2g17200

2DE46

root gravitropism46

ND

Q9S7C0

HSP70-14 Heat shock 70 kDa protein 14

At1g79930

2DE43, SG58

adventitious root development43 lateral root formation71

epidermis/QC, columell (cluster 38)

Q9MAH0

PPC1 Phosphoenolpyruvate carboxylase 1 (phosphorylated variant)

At1g53310

SG58, SG71


hairs, epidermis/QC, vasculature (Cluster 28)

Q9M0H8f

Predicted proline-rich protein (phosphorylated variant)

At4g28300

SG71


ND

Q9LZG0

ADK2 adenosine kinase 2

At5g03300

2DE47


ND

Q9LSQ4

GH3.6 IAA-amido synthetase

At5g54510

2DE43

adventitious root development43

ND

Q9LS40

ASPG1 Aspartic protease in guard cell 1

At3g18490

2DE43, SG58


ND

Q9LKR3

MED37A component of the Mediator complex

At5g28540

2DE43


Ubiquitous (Cluster 5)

Q9LFX8f

Uncharacterized glycine-rich protein (phosphorylated variant)

At1g27090

SG58, SG71


non-hair epidermis

Q9LD57

PGK1

At3g12780

2DE47, SG58


Ubiquitous

protein IDa

protein nameb

Q9T041



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phosphoglycerate kinase

(Cluster 5)

Q9FVT2

eEF-1B gamma 2 transcription elongation factor

At1g57720

2DE47, SG58



Q9FT97f

AGAL1 Alpha-galactosidase-like protein

At5g08380

2DE46

root gravitropism46

ND

Q9FND9

RFS5 Probable galactinol-sucrose galactosyltransferase 5 (phosphorylated variant)

At5g40390

SG71


ND

Q9FMM3f

GYF domain-containing protein (phosphorylated variant)

At5g42950

SG71


ND

Q9FG81f

Putative aluminum-induced protein (phosphorylated variant)

At5g43830

SG58, SG71


Hairs (Cluster 33)

Q9FG38

SNX1 Sorting nexin 1 (phosphorylated variant)

At5g06140

SG71


ND

Q9C9D0

SOT16 Cytosolic sulfotransferase 16

At1g74100

2DE43


ND

Q9C787f

cAMP-regulated phosphoprotein 19-related protein (phosphorylated variant)

At1g69510

SG71


ND

Q94K05f

TCP-1 Chaperonin T-complex protein 1, theta subunit

At3G03960

2DE47, SG58


cortex, vasculature, QC, hairs (cluster 29)

Q944G9

FBA2 Probable fructose-bisphosphate aldolase 2

At4g38970

2DE43


ND

Q8RWD5f

Putative uncharacterized protein (phosphorylated variant)

At3g48860

SG71


ND

Q8L7U5

BSL1 Serine/threonine-protein phosphatase (phosphorylated variant)

At4g03080

SG71


ND

Q76E23

Eukaryotic translation initiation factor 4G (phosphorylated variant)

At3g60240

SG71


ND

Q56WE7f

ANNAT2 Annexin-2

At5g65020

2DE46, SG58

root gravitropism46



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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


Q39026

MPK6 MAP kinase 6 (phosphorylated variant)

At2g43790

SG71


ND

Q0WV25

TUA2/TUBA2 tubulin alpha-2 chain

At1g04820

2DE47

apical meristemal elongation47 lateral root formation71

ND

P94072

GER3 Germin-like protein

At5g20630

2DE43


ND

P93017f

Dormancy/auxin associated proteine (phosphorylated variant)

At2g33830

SG71


ND

P57691

RPP0C 60S acidic ribosomal protein P0-3

At3g11250

SG71


ND

P25858

GAPC glyceraldehyde-3-phosphate dehydrogenase

At3g04120

2DE47, SG58


epidermis/QC (Cluster 56)

P25697

PRK Phosphoribulokinase

At1g32060

2DE43


ND

P25071

CML12 Calmodulin-like protein 12e

At2g41100

SG71


ND

P10795

RBCS-1A Ribulose bisphosphate carboxylase small chain 1A

At1g67090

SG71


ND

O81829

GH3.5 IAA-amido synthetase

At4g27260

2DE43


ND

O65655f

Putative uncharacterized protein (phosphorylated variant)

At4g39680

SG71


ND

O64768f

Octicosapeptide/Phox/Be.1 domain-containing protein kinase (phosphorylated variant)

At2g35050

SG71


ND

O24496

GLX2-2 Hydroxyacylglutathione hydrolase

At3g10850

2DE46

root gravitropism46

ND

O23207f

Quinone reductase family protein (phosphorylated variant)

At4g36750

SG71


ND

O22190

GH3-3 IAA-amido synthetasee

At2g23170

2DE43, SG71

adventitious root development43 lateral root formation71

ND

O03042

RBCL

AtCg00490

2DE47,SG58, SG71


Cortex



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

RuBisCO, large subunit

Page 28 of 46


(Cluster 25)

F4JWJ7f

CCR4-NOT transcription complex subunit 3 (phosphorylated variant)

At5g18230

SG71


ND

C0Z2J1f

Adenine nucleotide alpha hydrolases-like protein (phosphorylated variant)

At1g11360

SG71


ND

B9DGT7

TUA4/TUBA4 tubulin alpha-4 chain

At1g050010

2DE47


ND

a

UniProtKB/Swiss-Prot Accession Number.

b

Main protein name, according to protein sequence databases. Proteomics techniques used to compare at the proteomic level “normal” and “perturbated” states of distinct root developmental processes.

c

Numbering refers to the Reference list. d

Localization of the biomarker is according to Petrika et al.58 ND, not determined.

e

Biomarkers highlighted by Zhang et al.71 according to the ratio of their differential expression between control and auxin treated samples.

f

UniProtKB/TrEMBL protein entry, unreviewed in SwissProt data base.


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TABLE III Proteomic features of auxin-related root proteins in A. thaliana Arabidopsis thaliana protein gene products involved in auxin-driven biological processes were depicted on the basis of the Gene Ontology (GO) annotation via AmiGO site (http://amigo.geneontology.org) PaxDBh locus nameb protein namec subcellular locationd tissue specificitye GO annotationf PRIDEg entry #a (ppm) AFB2 nucleus (IEA) ubiquitous Q9LW29 At3g26810 auxin signaling pathway (IEA) + 7.04 Protein AUXIN SIGNALING F-BOX 2 vacuole membrane (IEA) higher levels in seedlings AFB3 ubiquitous auxin signaling pathway (ISS) Q9LPW7 At1g12820 nucleus (IEA) + 1.68 Protein AUXIN SIGNALING F-BOX 3 higher levels in flowers primary root development (IMP) ARF1 auxin signaling pathway (IEA) Q8L7G0 At1g59750 nucleus (IEA) ubiquitous NA NA Auxin response factor 1 response to auxin stimulus (IEP) ARF2 Q94JM3 At5g62000 nucleus (IDA) ubiquitous auxin signaling pathway (IEA) + 0.18 Auxin response factor 2 ARF3 O23661 At2g33860 nucleus (IEA) ubiquitous auxin signaling pathway (IEA) NA NA Auxin response factor 3 ARF4 Q9ZTX9 At5g60450 nucleus (IEA) ubiquitous auxin signaling pathway (IEA) NA NA Auxin response factor 4 auxin signaling pathway (IEA) ARF5 ubiquitous meristem development (IGI) P93024 At1g19850 nucleus (IEA) + 0.44 Auxin response factor 5 lower expression in leaves response to auxin stimulus (IEP) root development (IGI) ARF6 auxin signaling pathway (IEA) Q9ZTX8 At1g30330 nucleus (IDA) ubiquitous + 0.54 Auxin response factor 6 response to auxin stimulus (IMP) auxin signaling pathway (IEA) ARF7 P93022 At5g20730 nucleus (IDA) ubiquitous response to auxin stimulus (IMP) NA Auxin response factor 7 lateral root formation (IGI) ARF8 auxin signaling pathway (IEA) Q9FGV1 At5g37020 nucleus (IEA) ubiquitous + 0.18 Auxin response factor 8 response to auxin stimulus (IMP) ARF9 Q9XED8 At4g23980 nucleus (IEA) ubiquitous auxin signaling pathway (IEA) + NA Auxin response factor 9 ARF10 auxin signaling pathway (IMP) Q9SKN5 At2g28350 nucleus (IEA) ubiquitous NA NA Auxin response factor 10 root cap developmental (IMP) ARF11 nucleus (IEA) Q9ZPY6 At2g46530 NA auxin signaling pathway (IEA) NA NA Auxin response factor 11 plasmodesma (IDA) ARF12 Q9XID4 At1g34310 nucleus (IEA) NA auxin signaling pathway (IEA) NA NA Auxin response factor 12 ARF13 Q9FX25 At1g34170 nucleus (IEA) NA auxin signaling pathway (IEA) + 0.33 Auxin response factor 13 ARF14 Q9LQE8 At1g35540 nucleus (IEA) NA auxin signaling pathway (IEA) + 0.20 Putative auxin response factor 14 ARF15 Q9LQE3 At1g35520 nucleus (IEA) NA auxin signaling pathway (IEA) NA NA Putative auxin response factor 15 auxin signaling pathway (IEA) ARF16 Q93YR9 At4g30080 nucleus (IDA) NA response to auxin stimulus (IEP) NA NA Auxin response factor 16 root cap developmental (IMP) ARF17 Q84WU6 At1g77850 nucleus (IEA) NA adventitious root development (IMP) NA Auxin response factor 17 ARF18 Q9C5W9 At3g61830 nucleus (IEA) NA auxin signaling pathway (IEA) NA NA Auxin response factor 18 Q8RYC8 At1g19220 ARF19 nucleus (IEA) NA auxin signaling pathway (IEA) NA NA



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Auxin response factor 19 Q9C7I9

At1g35240

Q9C8N9

At1g34410

Q9C8N7

At1g34390

Q9LP07

At1g43950

ARF20 Auxin response factor 20 ARF21 Putative auxin response factor 21 ARF22 Auxin response factor 22 ARF23 Putative auxin response factor 23

lateral root formation (IGI) response to auxin stimulus (IMP) nucleus (IEA)

NA

auxin signaling pathway (IEA)

+

1.84

nucleus (IEA)

NA


NA

NA

nucleus (IEA)

NA


NA

NA

nucleus (IEA)

NA


NA

NA

plasma membranei Golgi apparatus (IDA)

root and shoot apical tissuesj

auxin signaling pathway (IEA) lateral root formation (IMP) positive gravitropism (IMP) root cap development (IMP) root hair cell differentiation (IMP)

+

1.39

auxin polar transport (IMP) response to auxin stimulus (IMP)

+

80.0

+

35.1

+

6.25

At2g38120

AUX1 Auxin transporter protein 1

Q9FZ33

At1g54990

AXR4 Protein AUXIN RESPONSE 4

ER membrane

most abundant in root tissue lesser amounts in rosette leaves, stems and flowers very little in mature siliques

Q9SRU2

At3g02260

BIG Auxin transport protein BIG

plasma membrane (IDA) cytosol (IDA)

roots, rosette leaves, inflorescence stems, and flowers

Q9LFB2

At5g01240

plasma membrane

NA

Q9S836

At2g21050

plasma membrane

NA

Q9CA25

At1g77690

LAX3 Auxin transporter-like protein 3

plasma membrane

NA

Q9C6B8

At1g73590

PIN1 Auxin efflux carrier component 1

plasma membrane

basal side of elongated parenchymatous xylem cells.

Q9LU77

At5g57090


membrane

root-specifick

Q9S7Z8

At1g70940


membrane

predominantly expressed at the lateral side of shoot endodermis cells as well as root pericycle and columella cells.

Q8RWZ6

At2g01420


membrane

expressed in the quiescent center precursors and surrounding cells

Q9LFP6

At5g15100

membrane (IEA) ER (IDA)

NA

Q9SQH6

At1g77110

membrane (IDA)

NA

Q96247

LAX1 Auxin transporter-like protein 1 LAX2 Auxin transporter-like protein 2

PIN5 Putative auxin efflux carrier component 5 PIN6 Probable auxin efflux carrier component 6

Page 30 of 46

auxin signaling pathway (IEA) auxin polar transport (IDA) lateral root formation (IMP) auxin signaling pathway (IEA) root cap development (IGI) auxin signaling pathway (IEA) root cap development (IGI) auxin polar transport (IDA) lateral root formation (IGI) response to auxin stimulus (IDA) root cap development (IGI) adventitious root development (IEA) auxin signaling pathway (IEA) auxin polar transport (IMP) gravitropism (IMP) root development (IMP) xylem and phloem pattern formation (IMP) auxin polar transport (IMP) positive gravitropism (IMP) auxin efflux (IMP) auxin polar transport (IMP) positive gravitropism (IMP) regulation of root meristem growth (IGI) root hair elongation (IMP) root hair initiation (IMP) auxin signaling pathway (IEA) auxin polar transport (IMP) root development (IMP) auxin efflux (IMP) auxin homeostasis (IMP) auxin signaling pathway (IEA) auxin polar transport (IMP)

NA

+

2.19

+

4.02

+

4.33

+

0.48

+

0.19

+

0.77

+

0.34


Page 31 of 46

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


Q940Y5

At1g23080


membrane (IDA)

NA

Q9FFD0

At5g16530

PIN8 Putative auxin efflux carrier component 8

ER (IDA) integral to membrane (IEA)

NA

a) b) c) d) e) f)

g) h) i) j) k)

auxin polar transport (IMP) longitudinal axis specification (IMP) root development (IGI) auxin homeostasis (IMP) auxin signaling pathway (IEA) intracellular auxin transport (IDA)

+

9.37

NA

NA

Protein entry number, according to the UniProtKB/Swiss-Prot non redundant protein sequence database. Code of the gene locus, according to the TAIR database. Main protein name, according to protein sequence databases. According to electronic annotation in protein sequence databases; IEA, inferred from electronic annotation; IDA, inferred from a direct assay; ER, endoplasmic reticulum. According to electronic annotation in protein sequence databases; NA, not available. Gene Onthology (GO) annotation of auxin-driven biological processes in which protein are related; beside the 29 process encompassing the “auxin” word, further GO entry have been arbitrarily considered, according to their involvement in the root growth. IDA, Inferred from Direct Assay; IPI, Inferred from Physical Interaction; IGI, Inferred from Genetic Interaction; IEP, Inferred from Expression Pattern; IEA, Inferred from Electronic Annotation; IMP:,Inferred from Mutant Phenotype; ISS, Inferred from Sequence or Structural Similarity PRIDE MS/MS signature from proteomics studies; +, available from root tissue; -, not available from root tissues; NA, not available at all. Integrated dataset from several quantitative proteomics experiments, limited to the root tissues. In S2 columella cells, a dynamic cytoplasmic to membrane localization seems to occur during early stage of gravity signal transduction. In roots protophloem cells, asymmetric repartition in the upper plasma membrane. In root apex, confined to stele initials, protophloem poles, statolith-containing S2 columella cells, lateral root cap cells (LRC), and in epidermal cells from the distal elongation zone (DEZ) up to central elongation zone (CEZ) Localized to the cortex, epidermis and lateral root cap, predominantly at the upper side of cells



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

7.

Page 32 of 46

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8.

ACKNOWLEDGMENTS

This work was supported by an European Research Council grant (to S.S), an Italian grant from MIUR (to M.E.S; grant number PRIN 2010HEBBB8_002) and by a Sapienza University grant (to M-E.S.; grant number C26A124WCR).


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Table of contents/Abstract Graphic 66x50mm (300 x 300 DPI)


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Proteomics in Deciphering the Auxin Commitment in the Arabidopsis

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