Arabidopsis Proteome Research Articles

Genome-Scale Characterization of Predicted Plastid-Targeted Proteomes in Higher Plants

Plastids are morphologically and functionally diverse organelles that are dependent on nuclear-encoded, plastid-targeted proteins for all biochemical and regulatory functions. However, how plastid proteomes vary temporally, spatially, and taxonomically has been historically difficult to analyze at a genome-wide scale using experimental methods. A bioinformatics workflow was developed and evaluated using a combination of fast and user-friendly subcellular prediction programs to maximize performance and accuracy for chloroplast transit peptides and demonstrate this technique on the predicted proteomes of 15 sequenced plant genomes. Gene family grouping was then performed in parallel using modified approaches of reciprocal best BLAST hits (RBH) and UCLUST. A total of 628 protein families were found to have conserved plastid targeting across angiosperm species using RBH, and 828 using UCLUST. However, thousands of clusters were also detected where only one species had predicted plastid targeting, most notably in Panicum virgatum which had 1,458 proteins with species-unique targeting. An average of 45% overlap was found in plastid-targeted protein-coding gene families compared with Arabidopsis, but an additional 20% of proteins matched against the full Arabidopsis proteome, indicating a unique evolution of plastid targeting. Neofunctionalization through subcellular relocalization is known to impart novel biological functions but has not been described before on a genome-wide scale for the plastid proteome. Further work to correlate these predicted novel plastid-targeted proteins to transcript abundance and high-throughput proteomics will uncover unique aspects of plastid biology and shed light on how the plastid proteome has evolved to influence plastid morphology and biochemistry.

Mass-spectrometry-based draft of the Arabidopsis proteome.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions.

Proximity labeling of protein complexes and cell-type-specific organellar proteomes in Arabidopsis enabled by TurboID.

Defining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurbo), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurbo in Arabidopsis and Nicotiana benthamiana and versatile vectors enable customization by plant researchers.

Comprehensive nuclear proteome of Arabidopsis obtained by sequential extraction

ABSTRACTIn eukaryotes, the nucleus plays key roles in fundamental cellular processes, including DNA replication, chromatin maintenance, transcription, and translation. To better understand the functional diversity of nuclei, we developed a method for the comprehensive extraction of the nuclear proteome from Arabidopsis. We used a buffer with a high sucrose concentration to purify nuclei and then conducted solubility-based fractionation to increase proteome coverage. We identified 1539 proteins and two novel nuclear envelope (NE) proteins in the nuclear fraction of Arabidopsis cultured cells. The localization of 25 proteins was determined by GFP fusion analyses; 23 of these proteins were localized either in the nucleus or the NE-associated endoplasmic reticulum. This result was indicative of the high quality of the proteome. These findings will be useful for clarifying novel nuclear functions in plants.

Identification of viral domains integrated into Arabidopsis proteome

Horizontal gene transfer (HGT) contributes to the genome evolution of living organisms. In particular, several recent studies provide convincing data on the integration of viral sequences into diverse organisms. Here, we identified 101 viral domains integrated into the model plant Arabidopsis proteome. Functional analysis based on gene ontology (GO) terms indicates that viral domains in the Arabidopsis proteome were involved in various stress responses with binding functions. Protein interaction networks support the strong protein interactions of viral domains with other Arabidopsis proteins. A proteome-wide analysis gave a comprehensive evolutionary view of viral domains integrated into 41 plant proteomes, revealing the specific and conserved integration of viral domains into plant proteomes. Phylogenetic analyses revealed the possible HGT between viral domains and plant proteomes. Our results provide an overview of the integration of viral domains into plant proteomes and their possible functional roles associated with plant defense mechanisms.

ITRAQ-based analysis of the Arabidopsis proteome reveals insights into the potential mechanisms of anthocyanin accumulation regulation in response to phosphate deficiency

Phosphate (Pi) deficiency significantly limits plant growth in natural and agricultural systems. Accumulation of anthocyanins in shoots is a common response of Arabidopsis thaliana to Pi deficiency. To elucidate the mechanisms underlying Pi deficiency-induced anthocyanin accumulation, we employed a proteomic approach based on isobaric tags for relative and absolute quantification (iTRAQ) to investigate protein expression profiles of Arabidopsis thaliana seedlings subjected to Pi deficiency for 7 days. In total, 5,106 proteins were identified, of which 156 displayed significant changes in abundance upon Pi deficiency. Bioinformatics analysis indicated that flavonoid biosynthesis was the most significantly elevated metabolic process under Pi deficiency. We further examined the potential role of the flavonoid biosynthetic pathway using a dihydroflavonol 4-reductase (DFR) mutant (tt3) and quantitative RT-PCR (qRT-PCR) analysis, and found that the tt3 mutant was deprived of transcriptional up-regulation of three genes related to anthocyanin biosynthesis, modification and transport under Pi deficiency. These results showed that Pi deficiency probably enhances the anthocyanin accumulation by promoting the flavonoid biosynthesis. The exact functions of these proteins remain to be examined. Nevertheless, our study increases the understanding of the mechanisms implicated in the anthocyanin accumulation induced by Pi deficiency and adaptive responses of plants to Pi starvation.

MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants.

Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.

Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism

Plants respond to seasonal cues such as the photoperiod, to adapt to current conditions and to prepare for environmental changes in the season to come. To assess photoperiodic responses at the protein level, we quantified the proteome of the model plant Arabidopsis thaliana by mass spectrometry across four photoperiods. This revealed coordinated changes of abundance in proteins of photosynthesis, primary and secondary metabolism, including pigment biosynthesis, consistent with higher metabolic activity in long photoperiods. Higher translation rates in the day than the night likely contribute to these changes, via an interaction with rhythmic changes in RNA abundance. Photoperiodic control of protein levels might be greatest only if high translation rates coincide with high transcript levels in some photoperiods. We term this proposed mechanism “translational coincidence”, mathematically model its components, and demonstrate its effect on the Arabidopsis proteome. Datasets from a green alga and a cyanobacterium suggest that translational coincidence contributes to seasonal control of the proteome in many phototrophic organisms. This may explain why many transcripts but not their cognate proteins exhibit diurnal rhythms.

Targeted Profiling of Arabidopsis thaliana Subproteomes Illuminates Co- and Posttranslationally N-Terminal Myristoylated Proteins.

N-terminal myristoylation, a major eukaryotic protein lipid modification, is difficult to detect in vivo and challenging to predict in silico. We developed a proteomics strategy involving subfractionation of cellular membranes, combined with separation of hydrophobic peptides by mass spectrometry-coupled liquid chromatography to identify the Arabidopsis thaliana myristoylated proteome. This approach identified a starting pool of 8837 proteins in all analyzed cellular fractions, comprising 32% of the Arabidopsis proteome. Of these, 906 proteins contain an N-terminal Gly at position 2, a prerequisite for myristoylation, and 214 belong to the predicted myristoylome (comprising 51% of the predicted myristoylome of 421 proteins). We further show direct evidence of myristoylation in 72 proteins; 18 of these myristoylated proteins were not previously predicted. We found one myristoylation site downstream of a predicted initiation codon, indicating that posttranslational myristoylation occurs in plants. Over half of the identified proteins could be quantified and assigned to a subcellular compartment. Hierarchical clustering of protein accumulation combined with myristoylation and S-acylation data revealed that N-terminal double acylation influences redirection to the plasma membrane. In a few cases, MYR function extended beyond simple membrane association. This study identified hundreds of N-acylated proteins for which lipid modifications could control protein localization and expand protein function.

Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.

Global proteomic analysis of advanced glycation end products in the Arabidopsis proteome provides evidence for age-related glycation hot spots

Glycation is a post-translational modification resulting from the interaction of protein amino and guanidino groups with carbonyl compounds. Initially, amino groups react with reducing carbohydrates, yielding Amadori and Heyns compounds. Their further degradation results in formation of advanced glycation end products (AGEs), also originating from α-dicarbonyl products of monosaccharide autoxidation and primary metabolism. In mammals, AGEs are continuously formed during the life of the organism, accumulate in tissues, are well-known markers of aging, and impact age-related tissue stiffening and atherosclerotic changes. However, the role of AGEs in age-related molecular alterations in plants is still unknown. To fill this gap, we present here a comprehensive study of the age-related changes in the Arabidopsis thaliana glycated proteome, including the proteins affected and specific glycation sites therein. We also consider the qualitative and quantitative changes in glycation patterns in terms of the general metabolic background, pathways of AGE formation, and the status of plant anti-oxidative/anti-glycative defense. Although the patterns of glycated proteins were only minimally influenced by plant age, the abundance of 96 AGE sites in 71 proteins was significantly affected in an age-dependent manner and clearly indicated the existence of age-related glycation hot spots in the plant proteome. Homology modeling revealed glutamyl and aspartyl residues in close proximity (less than 5 Å) to these sites in three aging-specific and eight differentially glycated proteins, four of which were modified in catalytic domains. Thus, the sites of glycation hot spots might be defined by protein structure that indicates, at least partly, site-specific character of glycation.

Persulfidation proteome reveals the regulation of protein function by hydrogen sulfide in diverse biological processes in Arabidopsis.

Hydrogen sulfide-mediated signaling pathways regulate many physiological and pathophysiological processes in mammalian and plant systems. The molecular mechanism by which hydrogen sulfide exerts its action involves the post-translational modification of cysteine residues to form a persulfidated thiol motif, a process called protein persulfidation. We have developed a comparative and quantitative proteomic analysis approach for the detection of endogenous persulfidated proteins in wild-type Arabidopsis and L-CYSTEINE DESULFHYDRASE 1 mutant leaves using the tag-switch method. The 2015 identified persulfidated proteins were isolated from plants grown under controlled conditions, and therefore, at least 5% of the entire Arabidopsis proteome may undergo persulfidation under baseline conditions. Bioinformatic analysis revealed that persulfidated cysteines participate in a wide range of biological functions, regulating important processes such as carbon metabolism, plant responses to abiotic and biotic stresses, plant growth and development, and RNA translation. Quantitative analysis in both genetic backgrounds reveals that protein persulfidation is mainly involved in primary metabolic pathways such as the tricarboxylic acid cycle, glycolysis, and the Calvin cycle, suggesting that this protein modification is a new regulatory component in these pathways.

Proteomic Analysis of Lipid Droplets from Arabidopsis Aging Leaves Brings New Insight into Their Biogenesis and Functions.

Lipid droplets (LDs) are cell compartments specialized for oil storage. Although their role and biogenesis are relatively well documented in seeds, little is known about their composition, structure and function in senescing leaves where they also accumulate. Here, we used a label free quantitative mass spectrometry approach to define the LD proteome of aging Arabidopsis leaves. We found that its composition is highly different from that of seed/cotyledon and identified 28 proteins including 9 enzymes of the secondary metabolism pathways involved in plant defense response. With the exception of the TRIGALACTOSYLDIACYLGLYCEROL2 protein, we did not identify enzymes implicated in lipid metabolism, suggesting that growth of leaf LDs does not occur by local lipid synthesis but rather through contact sites with the endoplasmic reticulum (ER) or other membranes. The two most abundant proteins of the leaf LDs are the CALEOSIN3 and the SMALL RUBBER PARTICLE1 (AtSRP1); both proteins have structural functions and participate in plant response to stress. CALEOSIN3 and AtSRP1 are part of larger protein families, yet no other members were enriched in the LD proteome suggesting a specific role of both proteins in aging leaves. We thus examined the function of AtSRP1 at this developmental stage and found that AtSRP1 modulates the expression of CALEOSIN3 in aging leaves. Furthermore, AtSRP1 overexpression induces the accumulation of triacylglycerol with an unusual composition compared to wild-type. We demonstrate that, although AtSRP1 expression is naturally increased in wild type senescing leaves, its overexpression in senescent transgenic lines induces an over-accumulation of LDs organized in clusters at restricted sites of the ER. Conversely, atsrp1 knock-down mutants displayed fewer but larger LDs. Together our results reveal that the abundancy of AtSRP1 regulates the neo-formation of LDs during senescence. Using electron tomography, we further provide evidence that LDs in leaves share tenuous physical continuity as well as numerous contact sites with the ER membrane. Thus, our data suggest that leaf LDs are functionally distinct from seed LDs and that their biogenesis is strictly controlled by AtSRP1 at restricted sites of the ER.

Mass Spectrometric Analyses Reveal a Central Role for Ubiquitylation in Remodeling the Arabidopsis Proteome during Photomorphogenesis

The switch from skotomorphogenesis to photomorphogenesis is a key developmental transition in the life of seed plants. While much of the underpinning proteome remodeling is driven by light-induced changes in gene expression, the proteolytic removal of specific proteins by the ubiquitin-26S proteasome system is also likely paramount. Through mass spectrometric analysis of ubiquitylated proteins affinity-purified from etiolated Arabidopsis seedlings before and after red-light irradiation, we identified a number of influential proteins whose ubiquitylation status is modified during this switch. We observed a substantial enrichment for proteins involved in auxin, abscisic acid, ethylene, and brassinosteroid signaling, peroxisome function, disease resistance, protein phosphorylation and light perception, including the phytochrome (Phy) A and phototropin photoreceptors. Soon after red-light treatment, PhyA becomes the dominant ubiquitylated species, with ubiquitin attachment sites mapped to six lysines. A PhyA mutant protected from ubiquitin addition at these sites is substantially more stable in planta upon photoconversion to Pfr and is hyperactive in driving photomorphogenesis. However, light still stimulates ubiquitylation and degradation of this mutant, implying that other attachment sites and/or proteolytic pathways exist. Collectively, we expand the catalog of ubiquitylation targets in Arabidopsis and show that this post-translational modification is central to the rewiring of plants for photoautotrophic growth.

PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction

Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting.

Bioinformatic Identification and Analysis of Hydroxyproline-Rich Glycoproteins in Populus trichocarpa.

BackgroundHydroxyproline-rich glycoproteins (HRGPs) constitute a plant cell wall protein superfamily that functions in diverse aspects of growth and development. This superfamily contains three members: the highly glycosylated arabinogalactan-proteins (AGPs), the moderately glycosylated extensins (EXTs), and the lightly glycosylated proline-rich proteins (PRPs). Chimeric and hybrid HRGPs, however, also exist. A bioinformatics approach is employed here to identify and classify AGPs, EXTs, PRPs, chimeric HRGPs, and hybrid HRGPs from the proteins predicted by the completed genome sequence of poplar (Populus trichocarpa). This bioinformatics approach is based on searching for biased amino acid compositions and for particular protein motifs associated with known HRGPs with a newly revised and improved BIO OHIO 2.0 program. Proteins detected by the program are subsequently analyzed to identify the following: 1) repeating amino acid sequences, 2) signal peptide sequences, 3) glycosylphosphatidylinositol lipid anchor addition sequences, and 4) similar HRGPs using the Basic Local Alignment Search Tool (BLAST).ResultsThe program was used to identify and classify 271 HRGPs from poplar including 162 AGPs, 60 EXTs, and 49 PRPs, which are each divided into various classes. This is in contrast to a previous analysis of the Arabidopsis proteome which identified 162 HRGPs consisting of 85 AGPs, 59 EXTs, and 18 PRPs. Poplar was observed to have fewer classical EXTs, to have more fasciclin-like AGPs, plastocyanin AGPs and AG peptides, and to contain a novel class of PRPs referred to as the proline-rich peptides.ConclusionsThe newly revised and improved BIO OHIO 2.0 bioinformatics program was used to identify and classify the inventory of HRGPs in poplar in order to facilitate and guide basic and applied research on plant cell walls. The newly identified poplar HRGPs can now be examined to determine their respective structural and functional roles, including their possible applications in the areas plant biofuel and natural products for medicinal or industrial uses. Additionally, other plants whose genomes are sequenced can now be examined in a similar way using this bioinformatics program which will provide insight to the evolution of the HRGP family in the plant kingdom.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0912-3) contains supplementary material, which is available to authorized users.

HfAIM: A reliable bioinformatics approach for in silico genome-wide identification of autophagy-associated Atg8-interacting motifs in various organisms

ABSTRACTMost of the proteins that are specifically turned over by selective autophagy are recognized by the presence of short Atg8 interacting motifs (AIMs) that facilitate their association with the autophagy apparatus. Such AIMs can be identified by bioinformatics methods based on their defined degenerate consensus F/W/Y-X-X-L/I/V sequences in which X represents any amino acid. Achieving reliability and/or fidelity of the prediction of such AIMs on a genome-wide scale represents a major challenge. Here, we present a bioinformatics approach, high fidelity AIM (hfAIM), which uses additional sequence requirements—the presence of acidic amino acids and the absence of positively charged amino acids in certain positions—to reliably identify AIMs in proteins. We demonstrate that the use of the hfAIM method allows for in silico high fidelity prediction of AIMs in AIM-containing proteins (ACPs) on a genome-wide scale in various organisms. Furthermore, by using hfAIM to identify putative AIMs in the Arabidopsis proteome, we illustrate a potential contribution of selective autophagy to various biological processes. More specifically, we identified 9 peroxisomal PEX proteins that contain hfAIM motifs, among which AtPEX1, AtPEX6 and AtPEX10 possess evolutionary-conserved AIMs. Bimolecular fluorescence complementation (BiFC) results verified that AtPEX6 and AtPEX10 indeed interact with Atg8 in planta. In addition, we show that mutations occurring within or nearby hfAIMs in PEX1, PEX6 and PEX10 caused defects in the growth and development of various organisms. Taken together, the above results suggest that the hfAIM tool can be used to effectively perform genome-wide in silico screens of proteins that are potentially regulated by selective autophagy. The hfAIM system is a web tool that can be accessed at link: http://bioinformatics.psb.ugent.be/hfAIM/.

Role of the proteome in phytohormonal signaling

Phytohormones are orchestrators of plant growth and development. A lot of time and effort has been invested in attempting to comprehend their complex signaling pathways but despite success in elucidating some key components, molecular mechanisms in the transduction pathways are far from being resolved. The last decade has seen a boom in the analysis of phytohormone-responsive proteins. Abscisic acid, auxin, brassinosteroids, cytokinin, ethylene, gibberellins, nitric oxide, oxylipins, strigolactones, salicylic acid — all have been analyzed to various degrees. For this review, we collected data from proteome-wide analyses resulting in a list of over 2000 annotated proteins from Arabidopsis proteomics and nearly 500 manually filtered protein families merged from all the data available from different species. We present the currently accepted model of phytohormone signaling, highlight the contributions made by proteomic-based research and describe the key nodes in phytohormone signaling networks, as revealed by proteome analysis. These include ubiquitination and proteasome mediated degradation, calcium ion signaling, redox homeostasis, and phosphoproteome dynamics. Finally, we discuss potential pitfalls and future perspectives in the field. This article is part of a Special Issue entitled: Plant Proteomics — a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.

Generation and characterization of expressed sequence tags in young roots of tea (Camellia assamica)

Assam tea (Camellia assamica) is perennial crop susceptible to moisture stress. We used its tender roots to construct a cDNA library for the identification, functional annotation, and analysis of transcripts. A total of 811 full-length expressed sequence tags were generated. After processing and assembly, 207 unigenes comprising 58 contigs and 149 singletons were registered. Finally, 35.75 % of the unigenes could be assigned to functional categories based on the Arabidopsis proteome. There was 43 % of a coding GC content and 1 272 coding DNA sequences found in the unigenes. Codon usage analysis shows leucine as the highest (9.92 %) and tryptophan (2.0 %) as the lowest coded amino acids. Further, a comparative study with drought-induced genes of young roots (reported earlier) reveals that 4.83 % of genes required for normal growth of roots were also induced by a drought stress. Expressions of 10 unigenes under different abiotic stresses, such as drought, cold, and salinity, were further confirmed by RT-qPCR. The sequence tags generated in this study will be valuable resources for functional genomics study of tea and other woody crop plants in future.

Identification of potential cargo proteins of transportin protein AtTRN1 in Arabidopsis thaliana.

We identified 23 novel proteins that can interact with At TRN1. These proteins are potential candidates of At TRN1 cargo proteins, which will facilitate our comprehending of At TRN1 functions in Arabidopsis. Tranportin 1 (TRN1) carries out the nucleo-cytoplasmic transport of many proteins, thereby ensuring that each of them is delivered to the right compartment for its proper function. These cargo proteins involved in lots of important processes, such as alternative pre-mRNA splicing, transcriptional regulation, and protein translation. Current understanding of cargo proteins transported by Arabidopsis thaliana transportin 1 (AtTRN1) is limited. Here, first we employed the yeast two-hybrid (Y2H) screening to identify proteins that can interact with AtTRN1 in Arabidopsis, and 12 novel proteins were found. Searching for PY-NLS motif in these 12 proteins suggested that no typical PY-NLS motif was present. We next investigated the specific motifs that will mediate the interactions in these sequences, and found that thirteen truncated fragments interacted with AtTRN1, containing 8 acidic and 5 basic fragments, respectively. We also searched the Arabidopsis proteome for homologs of cargo proteins of yeast Kapl04p and mammalian Kapβ2, and PY-NLS motif-containing proteins. Among these proteins, 11 were identified to interact with AtTRN1. The interactions between all the 23 proteins and AtTRN1 were confirmed by both Y2H and bimolecular fluorescence complementation (BiFC) assays. Our results show that AtTRN1 recognizes a broad spectrum of proteins having diverse functions, which will potentially be the cargoes of AtTRN1. Taken together, these results demonstrate the feasibility and potential power of these methods to identify cargo proteins of AtTRN1, and represent a primary and significant step in interpretation of AtTRN1 functionalities.