AtPIN: Arabidopsis thaliana protein interaction network

Protein interaction networks as metric spaces: a novel perspective on distribution of hubs

Background

In the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.

Results

By analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance.

Conclusions

We conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping’ functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence and that simply adopting theories from the social sciences may be misleading.

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

Reconstruction and analysis of nutrient-induced phosphorylation networks in Arabidopsis thaliana

Elucidating the dynamics of molecular processes in living organisms in response to external perturbations is a central goal in modern systems biology. We investigated the dynamics of protein phosphorylation events in Arabidopsis thaliana exposed to changing nutrient conditions. Phosphopeptide expression levels were detected at five consecutive time points over a time interval of 30 min after nutrient resupply following prior starvation. The three tested inorganic, ionic nutrients NH(+) 4, NO(-) 3, PO(3-) 4 elicited similar phosphosignaling responses that were distinguishable from those invoked by the sugars mannitol, sucrose. When embedded in the protein-protein interaction network of Arabidopsis thaliana, phosphoproteins were found to exhibit a higher degree compared to average proteins. Based on the time-series data, we reconstructed a network of regulatory interactions mediated by phosphorylation. The performance of different network inference methods was evaluated by the observed likelihood of physical interactions within and across different subcellular compartments and based on gene ontology semantic similarity. The dynamic phosphorylation network was then reconstructed using a Pearson correlation method with added directionality based on partial variance differences. The topology of the inferred integrated network corresponds to an information dissemination architecture, in which the phosphorylation signal is passed on to an increasing number of phosphoproteins stratified into an initiation, processing, and effector layer. Specific phosphorylation peptide motifs associated with the distinct layers were identified indicating the action of layer-specific kinases. Despite the limited temporal resolution, combined with information on subcellular location, the available time-series data proved useful for reconstructing the dynamics of the molecular signaling cascade in response to nutrient stress conditions in the plant Arabidopsis thaliana.

Fuzzy clustering of CPP family in plants with evolution and interaction analyses

Background

Transcription factors have been studied intensively because they play an important role in gene expression regulation. However, the transcription factors in the CPP family (cystein-rich polycomb-like protein), compared with other transcription factor families, have not received sufficient attention, despite their wide prevalence in a broad spectrum of species, from plants to animals. The total number of known CPP transcription factors in plants is 111 from 16 plants, but only 2 of them have been studied so far, namely TSO1 and CPP1 in Arabidopsis thaliana and soybean, respectively.

Methods

In this work, to study their functions, we applied the fuzzy clustering method to all plant CPP transcription factors. The feature vector of each protein sequence for the fuzzy clustering method is encoded by the short length peptides and the combination of functional domain models.

Results and conclusions

With the fuzzy clustering method, all plant CPP transcription factors are grouped into two subfamilies. A systems approach, including Expressed Sequence Tag analysis, evolutionary analysis, protein-protein interaction network analysis and co-expression analysis, is employed to validate the clustering results, the results of which also indicates that the transcription factors from different subfamilies show uncorrelated responses.

Evolutionary analyses of non-family genes in plants

There are a large number of 'non-family' (NF) genes that do not cluster into families with three or more members per genome. While gene families have been extensively studied, a systematic analysis of NF genes has not been reported. We performed comparative studies on NF genes in 14 plant species. Based on the clustering of protein sequences, we identified ~94,000 NF genes across these species that were divided into five evolutionary groups: Viridiplantae wide, angiosperm specific, monocot specific, dicot specific, and those that were species specific. Our analysis revealed that the NF genes resulted largely from less frequent gene duplications and/or a higher rate of gene loss after segmental duplication relative to genes in both low-copy-number families (LF; 3-10 copies per genome) and high-copy-number families (HF; >10 copies). Furthermore, we identified functions enriched in the NF gene set as compared with the HF genes. We found that NF genes were involved in essential biological processes shared by all plant lineages (e.g. photosynthesis and translation), as well as gene regulation and stress responses associated with phylogenetic diversification. In particular, our analysis of an Arabidopsis protein-protein interaction network revealed that hub proteins with the top 10% most connections were over-represented in the NF set relative to the HF set. This research highlights the roles that NF genes may play in evolutionary and functional genomics research.

OrchidBase 2.0: comprehensive collection of Orchidaceae floral transcriptomes

Both floral development and evolutionary trends of orchid flowers have long attracted the interest of biologists. However, expressed sequences derived from the flowers of other orchid subfamilies are still scarce except for a few species in Epidendroideae. In order to broadly increase our scope of Orchidaceae genetic information, we updated the OrchidBase to version 2.0 which has 1,562,071 newly added floral non-redundant transcribed sequences (unigenes) collected comprehensively from 10 orchid species across five subfamilies of Orchidaceae. A total of 662,671,362 reads were obtained by using next-generation sequencing (NGS) Solexa Illumina sequencers. After assembly, on average 156,207 unigenes were generated for each species. The average length of a unigene is 347 bp. We made a detailed annotation including general information, relative expression level, gene ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway mapping and gene network prediction. The online resources for putative annotation can be searched either by text or by using BLAST, and the results can be explored on the website and downloaded. We have re-designed the user interface in the new version. Users can enter the Phalaenopsis transcriptome or Orchidaceae floral transcriptome to browse or search the unigenes. OrchidBase 2.0 is freely available at http://orchidbase.itps.ncku.edu.tw/.

Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana

MOTIVATION

Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset.

RESULTS

We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein-protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome.

A computational prediction of structure and function of novel homologue of Arabidopsis thaliana Vps51/Vps67 subunit in Corchorus olitorius

Vps mediated vesicular transport is important for transferring macromolecules trapped inside a vesicle. Although highly abundant, Vps shows tremendous sequence variation among diverse array of species. However, this difference in sequence, which seems to also translate into substantial functional variation, is hardly characterized in Corchorus spp. Here, our computational study investigates structural and functional features of one of the Vps subunit namely Vps51/Vps67 in C. olitorius. Broad scale structural characterization revealed novel information about the overall Vps structure and binding sites. Moreover, functional analyses indicate interaction partners which were unexplored to date. Since membrane trafficking is essentially associated with nutrient uptake and chemical de-toxification, characterization of the Vps subunit can well provide us with better insight into important agronomic traits such as stress response, immune response and phytoremediation capacity.

Plant protein interactomes

Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.

Genetic adaptation associated with genome-doubling in autotetraploid Arabidopsis arenosa

Genome duplication, which results in polyploidy, is disruptive to fundamental biological processes. Genome duplications occur spontaneously in a range of taxa and problems such as sterility, aneuploidy, and gene expression aberrations are common in newly formed polyploids. In mammals, genome duplication is associated with cancer and spontaneous abortion of embryos. Nevertheless, stable polyploid species occur in both plants and animals. Understanding how natural selection enabled these species to overcome early challenges can provide important insights into the mechanisms by which core cellular functions can adapt to perturbations of the genomic environment. Arabidopsis arenosa includes stable tetraploid populations and is related to well-characterized diploids A. lyrata and A. thaliana. It thus provides a rare opportunity to leverage genomic tools to investigate the genetic basis of polyploid stabilization. We sequenced the genomes of twelve A. arenosa individuals and found signatures suggestive of recent and ongoing selective sweeps throughout the genome. Many of these are at genes implicated in genome maintenance functions, including chromosome cohesion and segregation, DNA repair, homologous recombination, transcriptional regulation, and chromatin structure. Numerous encoded proteins are predicted to interact with one another. For a critical meiosis gene, ASYNAPSIS1, we identified a non-synonymous mutation that is highly differentiated by cytotype, but present as a rare variant in diploid A. arenosa, indicating selection may have acted on standing variation already present in the diploid. Several genes we identified that are implicated in sister chromatid cohesion and segregation are homologous to genes identified in a yeast mutant screen as necessary for survival of polyploid cells, and also implicated in genome instability in human diseases including cancer. This points to commonalities across kingdoms and supports the hypothesis that selection has acted on genes controlling genome integrity in A. arenosa as an adaptive response to genome doubling.

Visual analysis of transcriptome data in the context of anatomical structures and biological networks

The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks

Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.

Tackling drought stress: receptor-like kinases present new approaches

Global climate change and a growing population require tackling the reduction in arable land and improving biomass production and seed yield per area under varying conditions. One of these conditions is suboptimal water availability. Here, we review some of the classical approaches to dealing with plant response to drought stress and we evaluate how research on RECEPTOR-LIKE KINASES (RLKs) can contribute to improving plant performance under drought stress. RLKs are considered as key regulators of plant architecture and growth behavior, but they also function in defense and stress responses. The available literature and analyses of available transcript profiling data indeed suggest that RLKs can play an important role in optimizing plant responses to drought stress. In addition, RLK pathways are ideal targets for nontransgenic approaches, such as synthetic molecules, providing a novel strategy to manipulate their activity and supporting translational studies from model species, such as Arabidopsis thaliana, to economically useful crops.

ANAP: an integrated knowledge base for Arabidopsis protein interaction network analysis

Protein interactions are fundamental to the molecular processes occurring within an organism and can be utilized in network biology to help organize, simplify, and understand biological complexity. Currently, there are more than 10 publicly available Arabidopsis (Arabidopsis thaliana) protein interaction databases. However, there are limitations with these databases, including different types of interaction evidence, a lack of defined standards for protein identifiers, differing levels of information, and, critically, a lack of integration between them. In this paper, we present an interactive bioinformatics Web tool, ANAP (Arabidopsis Network Analysis Pipeline), which serves to effectively integrate the different data sets and maximize access to available data. ANAP has been developed for Arabidopsis protein interaction integration and network-based study to facilitate functional protein network analysis. ANAP integrates 11 Arabidopsis protein interaction databases, comprising 201,699 unique protein interaction pairs, 15,208 identifiers (including 11,931 The Arabidopsis Information Resource Arabidopsis Genome Initiative codes), 89 interaction detection methods, 73 species that interact with Arabidopsis, and 6,161 references. ANAP can be used as a knowledge base for constructing protein interaction networks based on user input and supports both direct and indirect interaction analysis. It has an intuitive graphical interface allowing easy network visualization and provides extensive detailed evidence for each interaction. In addition, ANAP displays the gene and protein annotation in the generated interactive network with links to The Arabidopsis Information Resource, the AtGenExpress Visualization Tool, the Arabidopsis 1,001 Genomes GBrowse, the Protein Knowledgebase, the Kyoto Encyclopedia of Genes and Genomes, and the Ensembl Genome Browser to significantly aid functional network analysis. The tool is available open access at http://gmdd.shgmo.org/Computational-Biology/ANAP.

Protein-protein interactions in plants

The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.

Inferring the Brassica rapa Interactome Using Protein-Protein Interaction Data from Arabidopsis thaliana

Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein-protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain-domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.

Detection and interpretation of metabolite-transcript coresponses using combined profiling data

Motivation: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise.

Results: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes.

Availability: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites.

Contact:henning@psc.riken.jp; igcf@cin.ufpe.br

Supplementary information:Supplementary data are available at Bioinformatics online.

Changes in twelve homoeologous genomic regions in soybean following three rounds of polyploidy

With the advent of high-throughput sequencing, the availability of genomic sequence for comparative genomics is increasing exponentially. Numerous completed plant genome sequences enable characterization of patterns of the retention and evolution of genes within gene families due to multiple polyploidy events, gene loss and fractionation, and differential evolutionary pressures over time and across different gene families. In this report, we trace the changes that have occurred in 12 surviving homoeologous genomic regions from three rounds of polyploidy that contributed to the current Glycine max genome: a genome triplication before the origin of the rosids (~130 to 240 million years ago), a genome duplication early in the legumes (~58 million years ago), and a duplication in the Glycine lineage (~13 million years ago). Patterns of gene retention following the genome triplication event generally support predictions of the Gene Balance Hypothesis. Finally, we find that genes in networks with a high level of connectivity are more strongly conserved than those with low connectivity and that the enrichment of these highly connected genes in the 12 highly conserved homoeologous segments may in part explain their retention over more than 100 million years and repeated polyploidy events.

Unraveling tobacco BY-2 protein complexes with BN PAGE/LC-MS/MS and clustering methods

To understand physiological processes, insight into protein complexes is very important. Through a combination of blue native gel electrophoresis and LC-MS/MS, we were able to isolate protein complexes and identify their potential subunits from Nicotiana tabacum cv. Bright Yellow-2. For this purpose, a bioanalytical approach was used that works without a priori knowledge of the interacting proteins. Different clustering methods (e.g., k-means and hierarchical clustering) and a biclustering approach were evaluated according to their ability to group proteins by their migration profile and to correlate the proteins to a specific complex. The biclustering approach was identified as a very powerful tool for the exploration of protein complexes of whole cell lysates since it allows for the promiscuous nature of proteins. Furthermore, it searches for associations between proteins that co-occur frequently throughout the BN gel, which increases the confidence of the putative associations between co-migrating proteins. The statistical significance and biological relevance of the profile clusters were verified using functional gene ontology annotation. The proof of concept for identifying protein complexes by our BN PAGE/LC-MS/MS approach is provided through the analysis of known protein complexes. Both well characterized long-lived protein complexes as well as potential temporary sequential multi-enzyme complexes were characterized.

Experimental and computational approaches for the study of calmodulin interactions

Ca(2+), a universal messenger in eukaryotes, plays a major role in signaling pathways that control many growth and developmental processes in plants as well as their responses to various biotic and abiotic stresses. Cellular changes in Ca(2+) in response to diverse signals are recognized by protein sensors that either have their activity modulated or that interact with other proteins and modulate their activity. Calmodulins (CaMs) and CaM-like proteins (CMLs) are Ca(2+) sensors that have no enzymatic activity of their own but upon binding Ca(2+) interact and modulate the activity of other proteins involved in a large number of plant processes. Protein-protein interactions play a key role in Ca(2+)/CaM-mediated in signaling pathways. In this review, using CaM as an example, we discuss various experimental approaches and computational tools to identify protein-protein interactions. During the last two decades hundreds of CaM-binding proteins in plants have been identified using a variety of approaches ranging from simple screening of expression libraries with labeled CaM to high-throughput screens using protein chips. However, the high-throughput methods have not been applied to the entire proteome of any plant system. Nevertheless, the data provided by these screens allows the development of computational tools to predict CaM-interacting proteins. Using all known binding sites of CaM, we developed a computational method that predicted over 700 high confidence CaM interactors in the Arabidopsis proteome. Most (>600) of these are not known to bind calmodulin, suggesting that there are likely many more CaM targets than previously known. Functional analyses of some of the experimentally identified Ca(2+) sensor target proteins have uncovered their precise role in Ca(2+)-mediated processes. Further studies on identifying novel targets of CaM and CMLs and generating their interaction network - "calcium sensor interactome" - will help us in understanding how Ca(2+) regulates a myriad of cellular and physiological processes.

Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis

Background

Combining multiple evidence-types from different information sources has the potential to reveal new relationships in biological systems. The integrated information can be represented as a relationship network, and clustering the network can suggest possible functional modules. The value of such modules for gaining insight into the underlying biological processes depends on their functional coherence. The challenges that we wish to address are to define and quantify the functional coherence of modules in relationship networks, so that they can be used to infer function of as yet unannotated proteins, to discover previously unknown roles of proteins in diseases as well as for better understanding of the regulation and interrelationship between different elements of complex biological systems.

Results

We have defined the functional coherence of modules with respect to the Gene Ontology (GO) by considering two complementary aspects: (i) the fragmentation of the GO functional categories into the different modules and (ii) the most representative functions of the modules. We have proposed a set of metrics to evaluate these two aspects and demonstrated their utility in Arabidopsis thaliana. We selected 2355 proteins for which experimentally established protein-protein interaction (PPI) data were available. From these we have constructed five relationship networks, four based on single types of data: PPI, co-expression, co-occurrence of protein names in scientific literature abstracts and sequence similarity and a fifth one combining these four evidence types. The ability of these networks to suggest biologically meaningful grouping of proteins was explored by applying Markov clustering and then by measuring the functional coherence of the clusters.

Conclusions

Relationship networks integrating multiple evidence-types are biologically informative and allow more proteins to be assigned to a putative functional module. Using additional evidence types concentrates the functional annotations in a smaller number of modules without unduly compromising their consistency. These results indicate that integration of more data sources improves the ability to uncover functional association between proteins, both by allowing more proteins to be linked and producing a network where modular structure more closely reflects the hierarchy in the gene ontology.

PRIN: a predicted rice interactome network

Background

Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa.

Results

To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization.

Conclusions

PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology.

PRIN is available online at http://bis.zju.edu.cn/prin/.

The predicted Arabidopsis interactome resource and network topology-based systems biology analyses

Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the predicted arabidopsis interactome resource (PAIR; ) presents 149,900 potential molecular interactions, which are expected to cover approximately 24% of the entire interactome with approximately 40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.

Proteome databases and other online resources for chloroplast research in Arabidopsis

Proteomics aimed at addressing sub cellular fractions, such as chloroplasts, are a complex challenge. In the past few years, several studies in different laboratories have identified and, more recently, quantified, thousands of proteins within whole chloroplasts or chloroplast fractions. A considerable number of these studies are available for querying, using online resources, such as databases containing the proteins identified, encoding genes, acquired spectra, and phosphopeptides. The main purpose of this review is to identity and highlight useful features of these online resourses, mainly focused in proteomics databases related to chloroplast research in Arabidopsis thaliana. Several web sites were consulted. Among them, 11 were selected and discussed herein. The databases were classified into Plastid Databases, General Organelle Proteome Databases, and General Arabidopsis Proteome Databases. Special care was taken to present information regarding protein identification, protein quantification, and data integration. A selected list of online resources is presented in two tables. The databases analyzed are a useful source of information for researchers in the plastid organelle and plant proteomics fields.

Hubs and bottlenecks in plant molecular signalling networks

Conditional control of plant cell function and development relies on appropriate signal perception, signal integration and processing. The development of high throughput technologies such as proteomics and interactomics has enabled the identification of protein interaction networks that mediate signal processing from inputs to appropriate outputs. Such networks can be depicted in graphical representations using nodes and edges allowing for the immediate visualization and analysis of the network's topology. Hubs are network elements characterized by many edges (often degree grade k ≥ 5) which confer a degree of topological importance to them. The review introduces the concept of networks, hubs and bottlenecks and describes four examples from plant science in more detail, namely hubs in the redox regulatory network of the chloroplast with ferredoxin, thioredoxin and peroxiredoxin, in mitogen activated protein (MAP) kinase signal processing, in photomorphogenesis with the COP9 signalosome, COP1 and CDD, and monomeric GTPase function. Some guidance is provided to appropriate internet resources, web repositories, databases and their use. Plant networks can be generated from existing public databases and this type of analysis is valuable in support of existing hypotheses, or to allow for the generation of new concepts or ideas. However, intensive manual curating of in silico networks is still always necessary.

PAIR: the predicted Arabidopsis interactome resource

The predicted Arabidopsis interactome resource (PAIR, http://www.cls.zju.edu.cn/pair/), comprised of 5990 experimentally reported molecular interactions in Arabidopsis thaliana together with 145 494 predicted interactions, is currently the most comprehensive data set of the Arabidopsis interactome with high reliability. PAIR predicts interactions by a fine-tuned support vector machine model that integrates indirect evidences for interaction, such as gene co-expressions, domain interactions, shared GO annotations, co-localizations, phylogenetic profile similarities and homologous interactions in other organisms (interologs). These predictions were expected to cover 24% of the entire Arabidopsis interactome, and their reliability was estimated to be 44%. Two independent example data sets were used to rigorously validate the prediction accuracy. PAIR features a user-friendly query interface, providing rich annotation on the relationships between two proteins. A graphical interaction network browser has also been integrated into the PAIR web interface to facilitate mining of specific pathways.

Deciphering the Arabidopsis floral transition process by integrating a protein-protein interaction network and gene expression data

In a plant, the progression from vegetative growth to reproductive growth is called the floral transition. Over the past several decades, the floral transition has been shown to be determined not by a single gene but by a complicated gene network. This important biological process, however, has not been investigated at a genome-wide network level. We collected Arabidopsis (Arabidopsis thaliana) protein-protein interaction data from several public databases and compiled them into a genome-wide Arabidopsis interactome. Then, we integrated gene expression profiles during the Arabidopsis floral transition process into the established protein-protein interaction network to identify two types of anticorrelated modules associated with vegetative and reproductive growth. Generally, the vegetative modules are conserved in plants, while the reproductive modules are more specific to advanced plants. The existence of floral transition switches demonstrates that vegetative and reproductive processes might be coordinated by the interacting interface of these modules. Our work also provides many candidates for mediating the interactions between these modules, which may play important roles during the Arabidopsis vegetative/reproductive switch.

Evolutionary history of Arabidopsis thaliana aminoacyl-tRNA synthetase dual-targeted proteins

Aminoacyl-transfer RNA (tRNA) synthetases (aaRS) are key players in translation and act early in protein synthesis by mediating the attachment of amino acids to their cognate tRNA molecules. In plants, protein synthesis may occur in three subcellular compartments (cytosol, mitochondria, and chloroplasts), which requires multiple versions of the protein to be correctly delivered to its proper destination. The organellar aaRS are nuclear encoded and equipped with targeting information at the N-terminal sequence, which enables them to be specifically translocated to their final location. Most of the aaRS families present organellar proteins that are dual targeted to mitochondria and chloroplasts. Here, we examine the dual targeting behavior of aaRS from an evolutionary perspective. Our results show that Arabidopsis thaliana aaRS sequences are a result of a horizontal gene transfer event from bacteria. However, there is no evident bias indicating one single ancestor (Cyanobacteria or Proteobacteria). The dual-targeted aaRS phylogenetic relationship was characterized into two different categories (paralogs and homologs) depending on the state recovered for both dual-targeted and cytosolic proteins. Taken together, our results suggest that the dual-targeted condition is a gain-of-function derived from gene duplication. Selection may have maintained the original function in at least one of the copies as the additional copies diverged.