Plant protein-protein interaction network and interactome

Identification and characterization of Glycolate oxidase gene family in garden lettuce (Lactuca sativa cv. 'Salinas') and its response under various biotic, abiotic, and developmental stresses

Glycolate oxidase (GLO) is an FMN-containing enzyme localized in peroxisomes and performs in various molecular and biochemical mechanisms. It is a key player in plant glycolate and glyoxylate accumulation pathways. The role of GLO in disease and stress resistance is well-documented in various plant species. Although studies have been conducted regarding the role of GLO genes from spinach on a microbial level, the direct response of GLO genes to various stresses in short-season and leafy plants like lettuce has not been published yet. The genome of Lactuca sativa cultivar 'Salinas' (v8) was used to identify GLO gene members in lettuce by performing various computational analysis. Dual synteny, protein-protein interactions, and targeted miRNA analyses were conducted to understand the function of GLO genes. The identified GLO genes showed further clustering into two groups i.e., glycolate oxidase (GOX) and hydroxyacid oxidase (HAOX). Genes were observed to be distributed unevenly on three chromosomes, and syntenic analysis revealed that segmental duplication was prevalent. Thus, it might be the main reason for GLO gene diversity in lettuce. Almost all LsGLO genes showed syntenic blocks in respective plant genomes under study. Protein-protein interactions of LsGLO genes revealed various functional enrichments, mainly photorespiration, and lactate oxidation, and among biological processes oxidative photosynthetic carbon pathway was highly significant. Results of in-depth analyses disclosed the interaction of GLO genes with other members of the glycolate pathway and the activity of GLO genes in various organs and developmental stages in lettuce. The extensive genome evaluation of GLO gene family in garden lettuce is believed to be a reference for cloning and studying functional analyses of GLO genes and characterizing other members of glycolate/glyoxylate biosynthesis pathway in various plant species.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Discovering Entities Similarities in Biological Networks Using a Hybrid Immune Algorithm

Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules”. Community detection is an interesting and valuable approach to discovering the structure of the community in a complex network, revealing the internal organization of the nodes, and has become a leading research topic in the analysis of complex networks. This work investigates the link between biological modules and network communities in test-case biological networks that are commonly used as a reference point and which include Protein–Protein Interaction Networks, Metabolic Networks and Transcriptional Regulation Networks. In order to identify small and structurally well-defined communities in the biological context, a hybrid immune metaheuristic algorithm Hybrid-IA is proposed and compared with several metaheuristics, hyper-heuristics, and the well-known greedy algorithm Louvain, with respect to modularity maximization. Considering the limitation of modularity optimization, which can fail to identify smaller communities, the reliability of Hybrid-IA was also analyzed with respect to three well-known sensitivity analysis measures (NMI, ARI and NVI) that assess how similar the detected communities are to real ones. By inspecting all outcomes and the performed comparisons, we will see that on one hand Hybrid-IA finds slightly lower modularity values than Louvain, but outperforms all other metaheuristics, while on the other hand, it can detect communities more similar to the real ones when compared to those detected by Louvain.

Detecting Protein-Protein Interactions Using Bimolecular Fluorescence Complementation (BiFC) and Luciferase Complementation Assays (LCA)

In multicellular organisms, establishing the full body plane involves cell-cell signaling where protein associations are important for the diverse cellular functions within the cells. For the study of protein-protein interactions (PPI), bimolecular fluorescence complementation (BiFC) and luciferase complementation assays (LCA) have proven to be reliable tools that can be used to confirm the physical association of two proteins in a semi-in vivo environment. This chapter provides a detailed description of these two techniques using Nicotiana benthamiana as a semi-in vivo transient expression system. As an example, we will use the interaction of the two well-described transcription factors SHORT-ROOT (SHR) and SCARECROW (SCR), which are known as regulators of asymmetric cell division and stem cell specification in the root meristem of the model plant Arabidopsis thaliana. While the BiFC assay provides subcellular information by displaying a fluorescence signal, nuclear in this case, resulting from the reconstituted fluorophore, the LCA generates a quantitative readout of the SCR-SHR interaction. The combination of both assays provides information on the localization and strength of the PPI.

In silico and in vitro approaches allow the identification of the Prosystemin molecular network

Tomato Prosystemin (ProSys), the precursor of Systemin, a small peptidic hormone, is produced at very low concentration in unchallenged plants, while its expression greatly increases in response to several different stressors triggering an array of defence responses. The molecular mechanisms that underpin such a wide array of defence barriers are not fully understood and are likely correlated with the intrinsically disordered (ID) structure of the protein. ID proteins interact with different protein partners forming complexes involved in the modulation of different biological mechanisms. Here we describe the ProSys-protein network that shed light on the molecular mechanisms underpinning ProSys associated defence responses. Three different approaches were used. In silico prediction resulted in 98 direct interactors, most clustering in phytohormone biosynthesis, transcription factors and signal transduction gene classes. The network shows the central role of ProSys during defence responses, that reflects its role as central hub. In vitro ProSys interactors, identified by Affinity Purification-Mass Spectrometry (AP-MS), revealed over three hundred protein partners, while Bimolecular Fluorescent Complementation (BiFC) experiments validated in vivo some interactors predicted in silico and in vitro. Our results demonstrate that ProSys interacts with several proteins and reveal new key molecular events in the ProSys-dependent defence response of tomato plant.

Genome-wide transcriptome profiling revealed biological macromolecules respond to low temperature stress in Brassica napus L

Brassica napus L. (B. napus) is a vital oilseed crop cultivated worldwide; low temperature (LT) is one of the major stress factors that limit its growth, development, distribution, and production. Even though processes have been developed to characterize LT-responsive genes, only limited studies have exploited the molecular response mechanisms in B. napus. Here the transcriptome data of an elite B. napus variety with LT adaptability was acquired and applied to investigate the gene expression profiles of B. napus in response to LT stress. The bioinformatics study revealed a total of 79,061 unigenes, of which 3,703 genes were differentially expressed genes (DEGs), with 2,129 upregulated and 1,574 downregulated. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis pinpointed that the DEGs were enriched in LT-stress-responsive biological functions and metabolic pathways, which included sugar metabolism, antioxidant defense system, plant hormone signal transduction, and photosynthesis. Moreover, a group of LT-stress-responsive transcription factors with divergent expression patterns under LT was summarized. A combined protein interaction suggested that a complex interconnected regulatory network existed in all detected pathways. RNA-seq data was verified using real-time quantitative polymerase chain reaction analysis. Based on these findings, we presented a hypothesis model illustrating valuable information for understanding the LT response mechanisms in B. napus.

Potential application of TurboID-based proximity labeling in studying the protein interaction network in plant response to abiotic stress

Abiotic stresses are major environmental conditions that reduce plant growth, productivity and quality. Protein-protein interaction (PPI) approaches can be used to screen stress-responsive proteins and reveal the mechanisms of protein response to various abiotic stresses. Biotin-based proximity labeling (PL) is a recently developed technique to label proximal proteins of a target protein. TurboID, a biotin ligase produced by directed evolution, has the advantages of non-toxicity, time-saving and high catalytic efficiency compared to other classic protein-labeling enzymes. TurboID-based PL has been successfully applied in animal, microorganism and plant systems, particularly to screen transient or weak protein interactions, and detect spatially or temporally restricted local proteomes in living cells. This review concludes classic PPI approaches in plant response to abiotic stresses and their limitations for identifying complex network of regulatory proteins of plant abiotic stresses, and introduces the working mechanism of TurboID-based PL, as well as its feasibility and advantages in plant abiotic stress research. We hope the information summarized in this article can serve as technical references for further understanding the regulation of plant adaptation to abiotic stress at the protein level.

Protein-protein interactions in fatty acid elongase complexes are important for very-long-chain fatty acid synthesis

Fatty acid elongase (FAE), which catalyzes the synthesis of very-long-chain fatty acids (VLCFAs), is a multiprotein complex; however, little is known about its quaternary structure. In this study, bimolecular fluorescence complementation and/or yeast two-hybrid assays showed that homo-interactions were observed in β-ketoacyl-CoA synthases (KCS2, KCS9, and KCS6), Eceriferum2-like proteins [CER2 and CER2-Like2 (C2L2)], and FAE complex proteins (KCR1, PAS2, ECR, and PAS1), except for CER2-Like1 (C2L1). Hetero-interactions were observed between KCSs (KCS2, KCS9, and KCS6), between CER2-LIKEs (CER2, C2L2, and C2L1), and between FAE complex proteins (KCR1, PAS2, ECR, and PAS1). PAS1 interacts with FAE complex proteins (KCR1, PAS2, and ECR), but not with KCSs (KCS2, KCS9, and KCS6) and CER2-LIKEs (CER2, C2L2, and C2L1). Asp308 and Arg309-Arg311 of KCS9 were essential for the homo-interactions of KCS9 and hetero-interactions between KCS9 and PAS2 or ECR. Asp339 of KCS9 is involved in its homo- and hetero-interactions with ECR. Complementation analysis of the Arabidopsis kcs9 mutant by the expression of amino acid-substituted KCS9 mutant genes showed that Asp308 and Asp339 of KCS9 are involved in the synthesis of C24 VLCFAs from C22. This study suggests that protein-protein interaction in FAE complexes is important for VLCFA synthesis and provides insight into the quaternary structure of FAE complexes for efficient synthesis of VLCFAs.

Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.

Stress-regulated elements in Lotus spp., as a possible starting point to understand signalling networks and stress adaptation in legumes

Although legumes are of primary economic importance for human and livestock consumption, the information regarding signalling networks during plant stress response in this group is very scarce. Lotus japonicus is a major experimental model within the Leguminosae family, whereas L. corniculatus and L. tenuis are frequent components of natural and agricultural ecosystems worldwide. These species display differences in their perception and response to diverse stresses, even at the genotype level, whereby they have been used in many studies aimed at achieving a better understanding of the plant stress-response mechanisms. However, we are far from the identification of key components of their stress-response signalling network, a previous step for implementing transgenic and editing tools to develop legume stress-resilient genotypes, with higher crop yield and quality. In this review we scope a body of literature, highlighting what is currently known on the stress-regulated signalling elements so far reported in Lotus spp. Our work includes a comprehensive review of transcription factors chaperones, redox signals and proteins of unknown function. In addition, we revised strigolactones and genes regulating phytochelatins and hormone metabolism, due to their involvement as intermediates in several physiological signalling networks. This work was intended for a broad readership in the fields of physiology, metabolism, plant nutrition, genetics and signal transduction. Our results suggest that Lotus species provide a valuable information platform for the study of specific protein-protein (PPI) interactions, as a starting point to unravel signalling networks underlying plant acclimatation to bacterial and abiotic stressors in legumes. Furthermore, some Lotus species may be a source of genes whose regulation improves stress tolerance and growth when introduced ectopically in other plant species.

Plant proteomic research for improvement of food crops under stresses: a review

Crop improvement approaches have been changed due to technological advancements in traditional plant-breeding methods. Abiotic and biotic stresses limit plant growth and development, which ultimately lead to reduced crop yield. Proteins encoded by genomes have a considerable role in the endurance and adaptation of plants to different environmental conditions. Biotechnological applications in plant breeding depend upon the information generated from proteomic studies. Proteomics has a specific advantage to contemplate post-translational modifications, which indicate the functional effects of protein modifications on crop production. Subcellular proteomics helps in exploring the precise cellular responses and investigating the networking among subcellular compartments during plant development and biotic/abiotic stress responses. Large-scale mass spectrometry-based plant proteomic studies with a more comprehensive overview are now possible due to dramatic improvements in mass spectrometry, sample preparation procedures, analytical software, and strengthened availability of genomes for numerous plant species. Development of stress-tolerant or resilient crops is essential to improve crop productivity and growth. Use of high throughput techniques with advanced instrumentation giving efficient results made this possible. In this review, the role of proteomic studies in identifying the stress-response processes in different crops is summarized. Advanced techniques and their possible utilization on plants are discussed in detail. Proteomic studies accelerate marker-assisted genetic augmentation studies on crops for developing high yielding stress-tolerant lines or varieties under stresses.

Sequence-Based Prediction of Plant Protein-Protein Interactions by Combining Discrete Sine Transformation With Rotation Forest

Protein-protein interactions (PPIs) in plants are essential for understanding the regulation of biological processes. Although high-throughput technologies have been widely used to identify PPIs, they are usually laborious, expensive, and suffer from high false-positive rates. Therefore, it is imperative to develop novel computational approaches as a supplement tool to detect PPIs in plants. In this work, we presented a method, namely DST-RoF, to identify PPIs in plants by combining an ensemble learning classifier-Rotation Forest (RoF) with discrete sine transformation (DST). Specifically, plant protein sequence is firstly converted into Position-Specific Scoring Matrix (PSSM). Then, the discrete sine transformation was employed to extract effective features for obtaining the evolutionary information of proteins. Finally, these optimal features were fed into the RoF classifier for training and prediction. When performed on the plant datasets Arabidopsis, Rice, and Maize, DST-RoF yielded high prediction accuracy of 82.95%, 88.82%, and 93.70%, respectively. To further evaluate the prediction ability of our approach, we compared it with 4 state-of-the-art classifiers and 3 different feature extraction methods. Comprehensive experimental results anticipated that our method is feasible and robust for predicting potential plant-protein interacted pairs.

Prediction of Protein-Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform

Protein-protein interactions (PPIs) in plants play an essential role in the regulation of biological processes. However, traditional experimental methods are expensive, time-consuming, and need sophisticated technical equipment. These drawbacks motivated the development of novel computational approaches to predict PPIs in plants. In this article, a new deep learning framework, which combined the discrete Hilbert transform (DHT) with deep neural networks (DNN), was presented to predict PPIs in plants. To be more specific, plant protein sequences were first transformed as a position-specific scoring matrix (PSSM). Then, DHT was employed to capture features from the PSSM. To improve the prediction accuracy, we used the singular value decomposition algorithm to decrease noise and reduce the dimensions of the feature descriptors. Finally, these feature vectors were fed into DNN for training and predicting. When performing our method on three plant PPI datasets Arabidopsis thaliana, maize, and rice, we achieved good predictive performance with average area under receiver operating characteristic curve values of 0.8369, 0.9466, and 0.9440, respectively. To fully verify the predictive ability of our method, we compared it with different feature descriptors and machine learning classifiers. Moreover, to further demonstrate the generality of our approach, we also test it on the yeast and human PPI dataset. Experimental results anticipated that our method is an efficient and promising computational model for predicting potential plant-protein interacted pairs.

From Affinity to Proximity Techniques to Investigate Protein Complexes in Plants

The study of protein–protein interactions (PPIs) is fundamental in understanding the unique role of proteins within cells and their contribution to complex biological systems. While the toolkit to study PPIs has grown immensely in mammalian and unicellular eukaryote systems over recent years, application of these techniques in plants remains under-utilized. Affinity purification coupled to mass spectrometry (AP-MS) and proximity labeling coupled to mass spectrometry (PL-MS) are two powerful techniques that have significantly enhanced our understanding of PPIs. Relying on the specific binding properties of a protein to an immobilized ligand, AP is a fast, sensitive and targeted approach used to detect interactions between bait (protein of interest) and prey (interacting partners) under near-physiological conditions. Similarly, PL, which utilizes the close proximity of proteins to identify potential interacting partners, has the ability to detect transient or hydrophobic interactions under native conditions. Combined, these techniques have the potential to reveal an unprecedented spatial and temporal protein interaction network that better understands biological processes relevant to many fields of interest. In this review, we summarize the advantages and disadvantages of two increasingly common PPI determination techniques: AP-MS and PL-MS and discuss their important application to plant systems.

The Molecular Basis of JAZ-MYC Coupling, a Protein-Protein Interface Essential for Plant Response to Stressors

The jasmonic acid (JA) signaling pathway is one of the primary mechanisms that allow plants to respond to a variety of biotic and abiotic stressors. Within this pathway, the JAZ repressor proteins and the basic helix-loop-helix (bHLH) transcription factor MYC3 play a critical role. JA is a volatile organic compound with an essential role in plant immunity. The increase in the concentration of JA leads to the decoupling of the JAZ repressor proteins and the bHLH transcription factor MYC3 causing the induction of genes of interest. The primary goal of this study was to identify the molecular basis of JAZ-MYC coupling. For this purpose, we modeled and validated 12 JAZ-MYC3 3D in silico structures and developed a molecular dynamics/machine learning pipeline to obtain two outcomes. First, we calculated the average free binding energy of JAZ-MYC3 complexes, which was predicted to be -10.94 +/-2.67 kJ/mol. Second, we predicted which ones should be the interface residues that make the predominant contribution to the free energy of binding (molecular hotspots). The predicted protein hotspots matched a conserved linear motif SL••FL•••R, which may have a crucial role during MYC3 recognition of JAZ proteins. As a proof of concept, we tested, both in silico and in vitro, the importance of this motif on PEAPOD (PPD) proteins, which also belong to the TIFY protein family, like the JAZ proteins, but cannot bind to MYC3. By mutating these proteins to match the SL••FL•••R motif, we could force PPDs to bind the MYC3 transcription factor. Taken together, modeling protein-protein interactions and using machine learning will help to find essential motifs and molecular mechanisms in the JA pathway.

Envisioning the immune interactome in Arabidopsis

During plant-pathogen interaction, immune targets were regulated by protein-protein interaction events such as ligand-receptor/co-receptor, kinase-substrate, protein sequestration, activation or repression via post-translational modification and homo/oligo/hetro-dimerisation of proteins. A judicious use of molecular machinery requires coordinated protein interaction among defence components. Immune signalling in Arabidopsis can be broadly represented in successive or simultaneous steps; pathogen recognition at cell surface, Ca2+ and reactive oxygen species signalling, MAPK signalling, post-translational modification, transcriptional regulation and phyto-hormone signalling. Proteome wide interaction studies have shown the existence of interaction hubs associated with physiological function. So far, a number of protein interaction events regulating immune targets have been identified, but their understanding in an interactome view is lacking. We focussed specifically on the integration of protein interaction signalling in context to plant-pathogenesis and identified the key targets. The present review focuses towards a comprehensive view of the plant immune interactome including signal perception, progression, integration and physiological response during plant pathogen interaction.

Exploring dynamic protein-protein interactions in cassava through the integrative interactome network

Protein-protein interactions (PPIs) play an essential role in cellular regulatory processes. Despite, in-depth studies to uncover the mystery of PPI-mediated regulations are still lacking. Here, an integrative interactome network (MePPI-Ux) was obtained by incorporating expression data into the improved genome-scale interactome network of cassava (MePPI-U). The MePPI-U, constructed by both interolog- and domain-based approaches, contained 3,638,916 interactions and 24,590 proteins (59% of proteins in the cassava AM560 genome version 6). After incorporating expression data as information of state, the MePPI-U rewired to represent condition-dependent PPIs (MePPI-Ux), enabling us to envisage dynamic PPIs (DPINs) that occur at specific conditions. The MePPI-Ux was exploited to demonstrate timely PPIs of cassava under various conditions, namely drought stress, brown streak virus (CBSV) infection, and starch biosynthesis in leaf/root tissues. MePPI-Ux_drought and MePPI-Ux_CBSV suggested involved PPIs in response to stress. MePPI-Ux_SB,leaf and MePPI-Ux_SB,root suggested the involvement of interactions among transcription factor proteins in modulating how leaf or root starch is synthesized. These findings deepened our knowledge of the regulatory roles of PPIs in cassava and would undeniably assist targeted breeding efforts to improve starch quality and quantity.

A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

Investigation of the EIL/EIN3 Transcription Factor Gene Family Members and Their Expression Levels in the Early Stage of Cotton Fiber Development

The ethylene-insensitive3-like/ethylene-insensitive3 (EIL/EIN3) protein family can serve as a crucial factor for plant growth and development under diverse environmental conditions. EIL/EIN3 protein is a form of a localized nuclear protein with DNA-binding activity that potentially contributes to the intricate network of primary and secondary metabolic pathways of plants. In light of recent research advances, next-generation sequencing (NGS) and novel bioinformatics tools have provided significant breakthroughs in the study of the EIL/EIN3 protein family in cotton. In turn, this paved the way to identifying and characterizing the EIL/EIN3 protein family. Hence, the high-throughput, rapid, and cost-effective meta sequence analyses have led to a remarkable understanding of protein families in addition to the discovery of novel genes, enzymes, metabolites, and other biomolecules of the higher plants. Therefore, this work highlights the recent advance in the genomic-sequencing analysis of higher plants, which has provided a plethora of function profiles of the EIL/EIN3 protein family. The regulatory role and crosstalk of different metabolic pathways, which are apparently affected by these transcription factor proteins in one way or another, are also discussed. The ethylene hormone plays an important role in the regulation of reactive oxygen species in plants under various environmental stress circumstances. EIL/EIN3 proteins are the key ethylene-signaling regulators and play important roles in promoting cotton fiber developmental stages. However, the function of EIL/EIN3 during initiation and early elongation stages of cotton fiber development has not yet been fully understood. The results provided valuable information on cotton EIL/EIN3 proteins, as well as a new vision into the evolutionary relationships of this gene family in cotton species.

Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer

Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction network is essential for understanding the functional organization of the proteome. Numerous small-scale studies and a couple of large-scale ones have elucidated a fraction of the estimated 300,000 binary protein-protein interactions in Arabidopsis. In this study, we provide evidence that a docking algorithm has the ability to identify real interactions using both experimentally determined and predicted protein structures. We ranked 0.91 million interactions generated by all possible pairwise combinations of 1,346 predicted structure models from an Arabidopsis predicted "structure-ome" and found a significant enrichment of real interactions for the top-ranking predicted interactions, as shown by cosubcellular enrichment analysis and yeast two-hybrid validation. Our success rate for computationally predicted, structure-based interactions was 63% of the success rate for published interactions naively tested using the yeast two-hybrid system and 2.7 times better than for randomly picked pairs of proteins. This study provides another perspective in interactome exploration and biological network reconstruction using protein structural information. We have made these interactions freely accessible through an improved Arabidopsis Interactions Viewer and have created community tools for accessing these and ∼2.8 million other protein-protein and protein-DNA interactions for hypothesis generation by researchers worldwide. The Arabidopsis Interactions Viewer is freely available at http://bar.utoronto.ca/interactions2/.

Exploring the protein-protein interaction landscape in plants

Protein-protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.

The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs)

Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.

Augmentation of crop productivity through interventions of omics technologies in India: challenges and opportunities

With the continuous increase in the population of developing countries and decline of natural resources, there is an urgent need to qualitatively and quantitatively augment crop productivity by using new tools and technologies for improvement of agriculturally important traits. The new scientific and technological omics-based approaches have enabled us to deal with several issues and challenges faced by modern agricultural system and provided us novel opportunities for ensuring food and nutritional security. Recent developments in sequencing techniques have made available huge amount of genomic and transcriptomic data on model and cultivated crop plants including Arabidopsis thaliana, Oryza sativa, Triticum aestivum etc. The sequencing data along with other data generated through several omics platforms have significantly influenced the disciplines of crop sciences. Gene discovery and expression profiling-based technologies are offering enormous opportunities to the scientific community which can now apply marker-assisted selection technology to assess and enhance diversity in their collected germplasm, introgress essential traits from new sources and investigate genes that control key traits of crop plants. Utilization of omics science and technologies for crop productivity, protection and management has recently been receiving a lot of attention; the majority of the efforts have been put into signifying the possible applications of various omics technologies in crop plant sciences. This article highlights the background of challenges and opportunities for augmentation of crop productivity through interventions of omics technologies in India.

A genome-wide scan for correlated mutations detects macromolecular and chromatin interactions in Arabidopsis thaliana

The concept of exploiting correlated mutations has been introduced and applied successfully to identify interactions within and between biological macromolecules. Its rationale lies in the preservation of physical interactions via compensatory mutations. With the massive increase of available sequence information, approaches based on correlated mutations have regained considerable attention. We analyzed a set of 10 707 430 single nucleotide polymorphisms detected in 1135 accessions of the plant Arabidopsis thaliana. To measure their covariance and to reveal the global genome-wide sequence correlation structure of the Arabidopsis genome, the adjusted mutual information has been estimated for each possible pair of polymorphic sites. We developed a series of filtering steps to account for genetic linkage and lineage relations between Arabidopsis accessions, as well as transitive covariance as possible confounding factors. We show that upon appropriate filtering, correlated mutations prove indeed informative with regard to molecular interactions, and furthermore, appear to reflect on chromosomal interactions. Our study demonstrates that the concept of correlated mutations can also be applied successfully to within-species sequence variation and establishes a promising approach to help unravel the complex molecular interactions in A. thaliana and other species with broad sequence information.

Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs

Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.

Reconstructing phylogenetic tree using a protein-protein interaction technique

In this study, a novel substitution method for finding potential protein-protein interactions (PPIs) has been discussed. This newly designed method for analyzing PPI also aids in the comparison of evolutionary distances. The method deals with various data sets, and additionally performs measurable assessment to determine PPIs is introduced. PPIs are biologically relevant and aid in better conceptual framework of phylogenetic profiling. The newly designed framework gives vision to relate the topological properties of the system with evolutionary behavior of datasets. Firstly, this study found that the most conserved protein motifs exist at the roots of the system, whereas newer motifs with mutations have a tendency to dwell on the branches. In-depth functional analysis revealed that the most conserved motifs have high specificity for improved structural procedures and pathway engagements, which may help identify their formative parts in cells. In conclusion, this study demonstrates several important aspects for future studies focusing to enhance phylogenetic profiling systems. This study can also be used effectively to utilize such strategies to develop new biological insights which will further lead to understanding of disease mechanisms.

Enhancement of Environmental Hazard Degradation in the Presence of Lignin: a Proteomics Study

Proteomics studies of fungal systems have progressed dramatically based on the availability of more fungal genome sequences in recent years. Different proteomics strategies have been applied toward characterization of fungal proteome and revealed important gene functions and proteome dynamics. Presented here is the application of shot-gun proteomic technology to study the bio-remediation of environmental hazards by white-rot fungus. Lignin, a naturally abundant component of the plant biomass, is discovered to promote the degradation of Azo dye by white-rot fungus Irpex lacteus CD2 in the lignin/dye/fungus system. Shotgun proteomics technique was used to understand degradation mechanism at the protein level for the lignin/dye/fungus system. Our proteomics study can identify about two thousand proteins (one third of the predicted white-rot fungal proteome) in a single experiment, as one of the most powerful proteomics platforms to study the fungal system to date. The study shows a significant enrichment of oxidoreduction functional category under the dye/lignin combined treatment. An in vitro validation is performed and supports our hypothesis that the synergy of Fenton reaction and manganese peroxidase might play an important role in DR5B dye degradation. The results could guide the development of effective bioremediation strategies and efficient lignocellulosic biomass conversion.

Mapping the 'Two-component system' network in rice

Two-component system (TCS) in plants is a histidine to aspartate phosphorelay based signaling system. Rice genome has multifarious TCS signaling machinery comprising of 11 histidine kinases (OsHKs), 5 histidine phosphotransferases (OsHPTs) and 36 response regulators (OsRRs). However, how these TCS members interact with each other and comprehend diverse signaling cascades remains unmapped. Using a highly stringent yeast two-hybrid (Y2H) platform and extensive in planta bimolecular fluorescence complementation (BiFC) assays, distinct arrays of interaction between various TCS proteins have been identified in the present study. Based on these results, an interactome map of TCS proteins has been assembled. This map clearly shows a cross talk in signaling, mediated by different sensory OsHKs. It also highlights OsHPTs as the interaction hubs, which interact with OsRRs, mostly in a redundant fashion. Remarkably, interactions between type-A and type-B OsRRs have also been revealed for the first time. These observations suggest that feedback regulation by type-A OsRRs may also be mediated by interference in signaling at the level of type-B OsRRs, in addition to OsHPTs, as known previously. The interactome map presented here provides a starting point for in-depth molecular investigations for signal(s) transmitted by various TCS modules into diverse biological processes.

PCoM-DB Update: A Protein Co-Migration Database for Photosynthetic Organisms

The identification of protein complexes is important for the understanding of protein structure and function and the regulation of cellular processes. We used blue-native PAGE and tandem mass spectrometry to identify protein complexes systematically, and built a web database, the protein co-migration database (PCoM-DB, http://pcomdb.lowtem.hokudai.ac.jp/proteins/top), to provide prediction tools for protein complexes. PCoM-DB provides migration profiles for any given protein of interest, and allows users to compare them with migration profiles of other proteins, showing the oligomeric states of proteins and thus identifying potential interaction partners. The initial version of PCoM-DB (launched in January 2013) included protein complex data for Synechocystis whole cells and Arabidopsis thaliana thylakoid membranes. Here we report PCoM-DB version 2.0, which includes new data sets and analytical tools. Additional data are included from whole cells of the pelagic marine picocyanobacterium Prochlorococcus marinus, the thermophilic cyanobacterium Thermosynechococcus elongatus, the unicellular green alga Chlamydomonas reinhardtii and the bryophyte Physcomitrella patens. The Arabidopsis protein data now include data for intact mitochondria, intact chloroplasts, chloroplast stroma and chloroplast envelopes. The new tools comprise a multiple-protein search form and a heat map viewer for protein migration profiles. Users can compare migration profiles of a protein of interest among different organelles or compare migration profiles among different proteins within the same sample. For Arabidopsis proteins, users can compare migration profiles of a protein of interest with putative homologous proteins from non-Arabidopsis organisms. The updated PCoM-DB will help researchers find novel protein complexes and estimate their evolutionary changes in the green lineage.

Current Understanding of the TCTP Interactome

Evolutionarily conserved and pleiotropic, the translationally controlled tumor protein (TCTP) is a housekeeping protein present in eukaryotic organisms. It plays an important role in regulating many fundamental processes, such as cell proliferation, cell death, immune responses, and apoptosis. As a result of the pioneer work by Adam Telerman and Robert Amson, the critical role of TCTP in tumor reversion was revealed. Moreover, TCTP has emerged as a regulator of cell fate determination and a promising therapeutic target for cancers. The multifaceted action of TCTP depends on its ability to interact with different proteins. Through this interaction network, TCTP regulates diverse physiological and pathological processes in a context-dependent manner. Complete mapping of the entire sets of TCTP protein interactions (interactome) is essential to understand its various cellular functions and to lay the foundation for the rational design of TCTP-based therapeutic approaches. So far, the global profiling of the interacting partners of TCTP has rarely been performed, but many interactions have been identified in small-scale studies in a specific biological system. This chapter, based on information from protein interaction databases and the literature, illustrates current knowledge of the TCTP interactome.

Mapping Protein-Protein Interactions Using Affinity Purification and Mass Spectrometry

The mapping of protein-protein interaction (PPI) networks and their dynamics are crucial steps to deciphering the function of a protein and its role in cellular pathways, making it critical to have comprehensive knowledge of a protein's interactome. Advances in affinity purification and mass spectrometry technology (AP-MS) have provided a powerful and unbiased method to capture higher-order protein complexes and decipher dynamic PPIs. However, the unbiased calling of nonspecific interactions and the ability to detect transient interactions remains challenging when using AP-MS, thereby hampering the detection of biologically meaningful complexes. Additionally, there are plant-specific challenges with AP-MS, such as a lack of protein-specific antibodies, which must be overcome to successfully identify PPIs. Here we discuss and describe a protocol designed to bypass the traditional challenges of AP-MS and provide a roadmap to identify bona fide PPIs in plants.

Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA

The d-Ala:d-Lac ligase, VanA, plays a critical role in the resistance of vancomycin. Indeed, it is involved in the synthesis of a peptidoglycan precursor, to which vancomycin cannot bind. The reaction catalyzed by VanA requires the opening of the so-called “ω-loop”, so that the substrates can enter the active site. Here, the conformational landscape of VanA is explored by an enhanced sampling approach: the temperature-accelerated molecular dynamics (TAMD). Analysis of the molecular dynamics (MD) and TAMD trajectories recorded on VanA permits a graphical description of the structural and kinetics aspects of the conformational space of VanA, where the internal mobility and various opening modes of the ω-loop play a major role. The other important feature is the correlation of the ω-loop motion with the movements of the opposite domain, defined as containing the residues A149–Q208. Conformational and kinetic clusters have been determined and a path describing the ω-loop opening was extracted from these clusters. The determination of this opening path, as well as the relative importance of hydrogen bonds along the path, permit one to propose some key residue interactions for the kinetics of the ω-loop opening.

Phytochrome-interacting ankyrin repeat protein 2 modulates phytochrome A-mediated PIF3 phosphorylation in light signal transduction

Light signals recognized by phytochromes are transduced through interactions between down-stream signaling components. Phytochrome-interacting ankyrin repeat protein 2 (PIA2) was found to interact with phytochrome interacting factor 3 (PIF3), a well-known repressor of plant photomorphogenesis in response to phytochrome-mediated light signalling. Both PIA2 and PIF3 are known to be positive regulators of anthocyanin accumulation in Arabidopsis seedlings under far-red conditions. Thus, we investigated the functional relationship between PIA2 and PIF3 in light signalling. We found that PIA2 suppressed PIF3 phosphorylation by phyA. To elucidate how PIA2 modulates phyA-mediated PIF3 phosphorylation, we generated non-phosphorylation mutants and N-terminal α-helix breaking mutants of PIA2. PIF3 phosphorylation by phyA was not suppressed by α-helix breaking PIA2 mutants. The α-helix breaking mutations also resulted in remarkably decreased interactions between PIA2 and PIF3. However, the non-phosphorylation mutants exhibited no effect on phyA-mediated PIF3 phosphorylation. In addition, decreased anthocyanin accumulation in pia2 knockout plant seedlings was not rescued by overexpression of the α-helix breaking mutant in transgenic plants under far-red conditions. These results suggest that PIA2 modulates phyA-mediated PIF3 phosphorylation by physical interaction with PIF3 and that the secondary structure of the PIA2 N-terminus is important in this modulation.

Emerging Genomic Tools for Legume Breeding: Current Status and Future Prospects

Legumes play a vital role in ensuring global nutritional food security and improving soil quality through nitrogen fixation. Accelerated higher genetic gains is required to meet the demand of ever increasing global population. In recent years, speedy developments have been witnessed in legume genomics due to advancements in next-generation sequencing (NGS) and high-throughput genotyping technologies. Reference genome sequences for many legume crops have been reported in the last 5 years. The availability of the draft genome sequences and re-sequencing of elite genotypes for several important legume crops have made it possible to identify structural variations at large scale. Availability of large-scale genomic resources and low-cost and high-throughput genotyping technologies are enhancing the efficiency and resolution of genetic mapping and marker-trait association studies. Most importantly, deployment of molecular breeding approaches has resulted in development of improved lines in some legume crops such as chickpea and groundnut. In order to support genomics-driven crop improvement at a fast pace, the deployment of breeder-friendly genomics and decision support tools seems appear to be critical in breeding programs in developing countries. This review provides an overview of emerging genomics and informatics tools/approaches that will be the key driving force for accelerating genomics-assisted breeding and ultimately ensuring nutritional and food security in developing countries.

Asparagus IRX9, IRX10, and IRX14A Are Components of an Active Xylan Backbone Synthase Complex that Forms in the Golgi Apparatus

Heteroxylans are abundant components of plant cell walls and provide important raw materials for the food, pharmaceutical, and biofuel industries. A number of studies in Arabidopsis (Arabidopsis thaliana) have suggested that the IRREGULAR XYLEM9 (IRX9), IRX10, and IRX14 proteins, as well as their homologs, are involved in xylan synthesis via a Golgi-localized complex termed the xylan synthase complex (XSC). However, both the biochemical and cell biological research lags the genetic and molecular evidence. In this study, we characterized garden asparagus (Asparagus officinalis) stem xylan biosynthesis genes (AoIRX9, AoIRX9L, AoIRX10, AoIRX14A, and AoIRX14B) by heterologous expression in Nicotiana benthamiana We reconstituted and partially purified an active XSC and showed that three proteins, AoIRX9, AoIRX10, and AoIRX14A, are necessary for xylan xylosyltranferase activity in planta. To better understand the XSC structure and its composition, we carried out coimmunoprecipitation and bimolecular fluorescence complementation analysis to show the molecular interactions between these three IRX proteins. Using a site-directed mutagenesis approach, we showed that the DxD motifs of AoIRX10 and AoIRX14A are crucial for the catalytic activity. These data provide, to our knowledge, the first lines of biochemical and cell biological evidence that AoIRX9, AoIRX10, and AoIRX14A are core components of a Golgi-localized XSC, each with distinct roles for effective heteroxylan biosynthesis.

Detection of Protein-Protein Interactions in Tobacco BY-2 Cells Using Bimolecular Fluorescence Complementation

Knowledge of protein-protein interactions in the plant cell is invaluable for furthering our understanding of the functions of these proteins. Many of the methods available for the study of these interactions, such as yeast two-hybrid and co-immunoprecipitation assays, rely on in vitro techniques. Here we describe the use of bimolecular fluorescence complementation for the study of protein-protein interactions in vivo, using simple techniques and accessible materials.

The female gametophyte: an emerging model for cell type-specific systems biology in plant development

Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods ("omics") now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis). Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.

Mitogen Activated Protein Kinase (MPK) Interacts With Auxin Influx Carrier (OsAux/LAX1) Involved in Auxin Signaling in Plant

Background

Mitogen activated protein kinases (MPKs) are serine/threonine protein kinases that contain characteristic T-x-Y motif in the activation loop region. MPKs are important signaling molecules involved in diverse signaling cascades that regulate plant growth, development and stress responses by conducting phosphorylation events in their target proteins. MPKs phosphorylate their target proteins at either S-P/T-P (Serine/Proline/Threonine) amino acid. To understand, if MPKs are involved in the auxin signaling cascade, we identified probable target proteins of MPKs involved in auxin signaling or transport processes.

Results

A genome-wide search of the rice genome database led us to identification of the OsAux/LAX1 gene as a potential downstream target protein of MPKs. In-silico analysis predicted that MPKs interact with OsAux/LAX1 proteins which were validated by a yeast two-hybrid assay that showed OsMPK3, OsMPK4 and OsMPK6 are physically interact with OsAux/LAX1 protein.

Conclusion

The yeast two-hybrid interaction showed that MPKs are directly involved in auxin signaling events in plants. This is the first study to report direct involvement of MPKs in the auxin signaling pathway.

Rule-Based Design of Plant Expression Vectors Using GenoCAD

Plant synthetic biology requires software tools to assist on the design of complex multi-genic expression plasmids. Here a vector design strategy to express genes in plants is formalized and implemented as a grammar in GenoCAD, a Computer-Aided Design software for synthetic biology. It includes a library of plant biological parts organized in structural categories and a set of rules describing how to assemble these parts into large constructs. Rules developed here are organized and divided into three main subsections according to the aim of the final construct: protein localization studies, promoter analysis and protein-protein interaction experiments. The GenoCAD plant grammar guides the user through the design while allowing users to customize vectors according to their needs. Therefore the plant grammar implemented in GenoCAD will help plant biologists take advantage of methods from synthetic biology to design expression vectors supporting their research projects.

In vivo interaction between the tobacco lectin and the core histone proteins

Nictaba, a lectin accumulating in tobacco (Nicotiana tabacum) leaves treated with jasmonate, is considered to act as a signaling protein in the stress physiology of the plant. Immunolocalization studies revealed that Nictaba has a nucleocytoplasmic localization. In previous research, histones were identified as primary interaction partners for Nictaba. Here, the interaction between Nictaba and tobacco histones was scrutinized in vivo. Localization studies, performed in stably transformed Nicotiana benthamiana plants, confirmed the nucleocytoplasmic localization of the lectin and colocalization with the presumed binding partners in the nucleus. Furthermore, bimolecular fluorescence complementation (BiFC) assays confirmed the interaction in vivo. Since BiFC signals were also observed for a Nictaba mutant incapable of binding sugar moieties, this interaction may be mediated by alternative binding sites. The interaction of Nictaba with core histones possibly reflects a role of this stress inducible lectin in gene regulation or chromatin remodeling.

The potential of transcription factor-based genetic engineering in improving crop tolerance to drought

Drought is one of the major constraints in crop production and has an effect on a global scale. In order to improve crop production, it is necessary to understand how plants respond to stress. A good understanding of regulatory mechanisms involved in plant responses during drought will enable researchers to explore and manipulate key regulatory points in order to enhance stress tolerance in crops. Transcription factors (TFs) have played an important role in crop improvement from the dawn of agriculture. TFs are therefore good candidates for genetic engineering to improve crop tolerance to drought because of their role as master regulators of clusters of genes. Many families of TFs, such as CCAAT, homeodomain, bHLH, NAC, AP2/ERF, bZIP, and WRKY have members that may have the potential to be tools for improving crop tolerance to drought. In this review, the roles of TFs as tools to improve drought tolerance in crops are discussed. The review also focuses on current strategies in the use of TFs, with emphasis on several major TF families in improving drought tolerance of major crops. Finally, many promising transgenic lines that may have improved drought responses have been poorly characterized and consequently their usefulness in the field is uncertain. New advances in high-throughput phenotyping, both greenhouse and field based, should facilitate improved phenomics of transgenic lines. Systems biology approaches should then define the underlying changes that result in higher yields under water stress conditions. These new technologies should help show whether manipulating TFs can have effects on yield under field conditions.

Synthetic metabolons for metabolic engineering

It has been proposed that enzymes can associate into complexes (metabolons) that increase the efficiency of metabolic pathways by channelling substrates between enzymes. Metabolons may increase flux by increasing the local concentration of intermediates, decreasing the concentration of enzymes needed to maintain a given flux, directing the products of a pathway to a specific subcellular location or minimizing the escape of reactive intermediates. Metabolons can be formed by relatively loose non-covalent protein-protein interaction, anchorage to membranes, and (in bacteria) by encapsulation of enzymes in protein-coated microcompartments. Evidence that non-coated metabolons are effective at channelling substrates is scarce and difficult to obtain. In plants there is strong evidence that small proportions of glycolytic enzymes are associated with the outside of mitochondria and are effective in substrate channelling. More recently, synthetic metabolons, in which enzymes are scaffolded to synthetic proteins or nucleic acids, have been expressed in microorganisms and these provide evidence that scaffolded enzymes are more effective than free enzymes for metabolic engineering. This provides experimental evidence that metabolons may have a general advantage and opens the way to improving the outcome of metabolic engineering in plants by including synthetic metabolons in the toolbox.

Computational Prediction of Protein-Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.

Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein-protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Interaction proteins of invertase and invertase inhibitor in cold-stored potato tubers suggested a protein complex underlying post-translational regulation of invertase

The activity of vacuolar invertase (VI) is vital to potato cold-induced sweetening (CIS). A post-translational regulation of VI activity has been proposed which involves invertase inhibitor (VIH), but the mechanism for the interaction between VI and VIH has not been fully understood. To identify the potential partners of VI and VIH, two cDNA libraries were respectively constructed from CIS-resistant wild potato species Solanum berthaultii and CIS-sensitive potato cultivar AC035-01 for the yeast two-hybrid analysis. The StvacINV1 (one of the potato VIs) and StInvInh2B (one of the potato VIHs), previously identified to be associated with potato CIS, were used as baits to screen the two libraries. Through positive selection and sequencing, 27 potential target proteins of StvacINV1 and eight of StInvInh2B were clarified. The Kunitz-type protein inhibitors were captured by StvacINV1 in both libraries and the interaction between them was confirmed by bimolecular fluorescence complementation assay in tobacco cells, reinforcing a fundamental interaction between VI and VIH. Notably, a sucrose non-fermenting-1-related protein kinase 1 was captured by both the baits, suggesting that a protein complex could be necessary for fine turning of the invertase activity. The target proteins clarified in present research provide a route to elucidate the mechanism by which the VI activity can be subtly modulated.

Application of an improved proteomics method for abundant protein cleanup: molecular and genomic mechanisms study in plant defense

High abundance proteins like ribulose-1,5-bisphosphate carboxylase oxygenase (Rubisco) impose a consistent challenge for the whole proteome characterization using shot-gun proteomics. To address this challenge, we developed and evaluated Polyethyleneimine Assisted Rubisco Cleanup (PARC) as a new method by combining both abundant protein removal and fractionation. The new approach was applied to a plant insect interaction study to validate the platform and investigate mechanisms for plant defense against herbivorous insects. Our results indicated that PARC can effectively remove Rubisco, improve the protein identification, and discover almost three times more differentially regulated proteins. The significantly enhanced shot-gun proteomics performance was translated into in-depth proteomic and molecular mechanisms for plant insect interaction, where carbon re-distribution was used to play an essential role. Moreover, the transcriptomic validation also confirmed the reliability of PARC analysis. Finally, functional studies were carried out for two differentially regulated genes as revealed by PARC analysis. Insect resistance was induced by over-expressing either jacalin-like or cupin-like genes in rice. The results further highlighted that PARC can serve as an effective strategy for proteomics analysis and gene discovery.

Subcellular localization and self-interaction of plant-specific Nt-4/1 protein

The Nicotiana tabacum Nt-4/1 protein is a plant-specific protein of unknown function. Analysis of bacterially expressed Nt-4/1 protein in vitro revealed that the protein secondary structure is mostly alpha-helical and suggested that it could consist of three structural domains. Earlier studies of At-4/1, the Arabidopsis thaliana-encoded ortholog of Nt-4/1, demonstrated that GFP-fused At-4/1 was capable of polar localization in plant cells, association with plasmodesmata, and cell-to-cell transport. Together with the At-4/1 ability to interact with a plant virus movement protein, these data supported the hypothesis of the At-4/1 protein involvement in viral transport through plasmodesmata. Studies of the Nt-4/1-GFP fusion protein reported in this paper revealed that the protein was localized to cytoplasmic bodies, which were co-aligned with actin filaments and capable of actin-dependent intracellular movement. The Nt-4/1-GFP bodies, being non-membrane structures, were found in association with the plasma membrane, the tubular endoplasmic reticulum and endosome-like structures. Bimolecular fluorescence complementation experiments and inhibition of nuclear export showed that the Nt-4/1 protein was capable of nuclear-cytoplasmic transport. The nuclear export signal (NES) was identified in the Nt-4/1 protein by site-directed mutagenesis. The Nt-4/1 NES mutant was localized to the nucleoplasm forming spherical bodies. Immunogold labeling and electron microscopy of cytoplasmic Nt-4/1-containing bodies and nuclear structures containing the Nt-4/1 NES mutant revealed differences in their fine structure. In mammalian cells, Nt-4/1-GFP formed cytoplasmic spherical bodies similar to those found for the Nt-4/1 NES mutant in plant cell nuclei. Using dynamic laser light scattering and electron microscopy, the Nt-4/1 protein was found to form multimeric complexes in vitro.