Protein-protein interactions in plants

Protein-Protein Interactions Visualized by Bimolecular Fluorescence Complementation in Arabidopsis thaliana Protoplasts from Leaf

Bimolecular fluorescence complementation (BiFC) is a powerful tool for studying protein-protein interactions in living cells. By fusing interacting proteins to fluorescent protein fragments, BiFC allows visualization of spatial localization patterns of protein complexes. This method has been adapted to a variety of expression systems in different organisms and is widely used to study protein interactions in plant cells. The Agrobacterium-mediated transient expression protocol for BiFC assays in Nicotiana benthamiana (N. benthamiana) leaf cells is widely used, but in this chapter, a method for BiFC assay using Arabidopsis thaliana protoplasts is presented.

Computed Protein-Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems

Understanding the thermodynamic signature of protein-peptide binding events is a major challenge in computational chemistry. The complexity generated by both components possessing many degrees of freedom poses a significant issue for methods that attempt to directly compute the enthalpic contribution to binding. Indeed, the prevailing assumption has been that the errors associated with such approaches would be too large for them to be meaningful. Nevertheless, we currently have no indication of how well the present methods would perform in terms of predicting the enthalpy of binding for protein-peptide complexes. To that end, we carefully assembled and curated a set of 11 protein-peptide complexes where there is structural and isothermal titration calorimetry data available and then computed the absolute enthalpy of binding. The initial "out of the box" calculations were, as expected, very modest in terms of agreement with the experiment. However, careful inspection of the outliers allows for the identification of key sampling problems such as distinct conformations of peptide termini not being sampled or suboptimal cofactor parameters. Additional simulations guided by these aspects can lead to a respectable correlation with isothermal titration calorimetry (ITC) experiments (R2 of 0.88 and an RMSE of 1.48 kcal/mol overall). Although one cannot know prospectively whether computed ITC values will be correct or not, this work shows that if experimental ITC data are available, then this in conjunction with computed ITC, can be used as a tool to know if the ensemble being simulated is representative of the true ensemble or not. That is important for allowing the correct interpretation of the detailed dynamics of the system with respect to the measured enthalpy. The results also suggest that computational calorimetry is becoming increasingly feasible. We provide the data set as a resource for the community, which could be used as a benchmark to help further progress in this area.

New insights into comprehensive analysis of magnesium transporter (MGT) gene family in rice (Oryza sativa L.)

Magnesium transporters (MGTs) regulate magnesium absorption, transport, and redistribution in higher plants. To investigate the role of the Oryza sativa MGTs gene family members under salt stress, this study analyzed the protein properties, gene structure, phylogenetic relationship, synteny patterns, expression, and co-expression networks of 23 non-redundant OsMGT. The evolutionary relationship of the OsMGT gene family was fully consistent with their functional domain, and were divided into three main classes based on the conserved domain: MMgT, CorA-like, and NIPA. The α/β patterns in the protein structures were highly similar in the CorA-like and NIPA members, with the conserved structures in the Mg2+-binding and catalytic regions. The CorA-like clade-related proteins demonstrated the highest numbers of protein channels with Pro, Ser, Lys, Gly, and Tyr, as the critical binding residues. The expression analysis of OsMGT genes in various tissues showed that MGTs' gene family may possess critical functions during rice development. Gene expression analysis of candidate OsMGT using reverse-transcription quantitative real-time PCR (RT-qPCR) found that four OsMGT genes exhibited different expression patterns in salt-sensitive and salt-tolerant rice genotypes. We hypothesize that the OsMGT gene family members may be involved in responses to salt stress. These findings could be useful for further functional investigation of MGTs as well as defining their involvement in abiotic stress studies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03735-4.

Proteomics approach to investigating osmotic stress effects on pistachio

Osmotic stress can occur due to some stresses such as salinity and drought, threatening plant survival. To investigate the mechanism governing the pistachio response to this stress, the biochemical alterations and protein profile of PEG-treated plants was monitored. Also, we selected two differentially abundant proteins to validate via Real-Time PCR. Biochemical results displayed that in treated plants, proline and phenolic content was elevated, photosynthetic pigments except carotenoid decreased and MDA concentration were not altered. Our findings identified a number of proteins using 2DE-MS, involved in mitigating osmotic stress in pistachio. A total of 180 protein spots were identified, of which 25 spots were altered in response to osmotic stress. Four spots that had photosynthetic activities were down-regulated, and the remaining spots were up-regulated. The biological functional analysis of protein spots exhibited that most of them are associated with the photosynthesis and metabolism (36%) followed by stress response (24%). Results of Real-Time PCR indicated that two of the representative genes illustrated a positive correlation among transcript level and protein expression and had a similar trend in regulation of gene and protein. Osmotic stress set changes in the proteins associated with photosynthesis and stress tolerance, proteins associated with the cell wall, changes in the expression of proteins involved in DNA and RNA processing occur. Findings of this research will introduce possible proteins and pathways that contribute to osmotic stress and can be considered for improving osmotic tolerance in pistachio.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network

The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

CPIELA: Computational Prediction of Plant Protein–Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information

Identification and characterization of plant protein–protein interactions (PPIs) are critical in elucidating the functions of proteins and molecular mechanisms in a plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually expensive and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining the position-specific scoring matrix (PSSM), local optimal-oriented pattern (LOOP), and ensemble rotation forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, the ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets: Arabidopsis thaliana, Zea mays, and Oryza sativa. The experimental results demonstrate that the CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that the CPIELA approach could become a useful tool for facilitating the identification of possible plant PPIs.

Purification of MAP-kinase protein complexes and identification of candidate components by XL-TAP-MS

The purification of low-abundance protein complexes and detection of in vivo protein-protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL-TAP-MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL-TAP-MS to study the MKK2-Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde-crosslinked protein complexes under fully denaturing conditions. Combined with 15N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2-MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein-protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL-TAP-MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein-protein interactions.

An Importin-β-like Protein from Nicotiana benthamiana Interacts with the RNA Silencing Suppressor P1b of the Cucumber Vein Yellowing Virus, Modulating Its Activity

During a plant viral infection, host-pathogen interactions are critical for successful replication and propagation of the virus through the plant. RNA silencing suppressors (RSSs) are key players of this interplay, and they often interact with different host proteins, developing multiple functions. In the Potyviridae family, viruses produce two main RSSs, HCPro and type B P1 proteins. We focused our efforts on the less known P1b of cucumber vein yellowing virus (CVYV), a type B P1 protein, to try to identify possible factors that could play a relevant role during viral infection. We used a chimeric expression system based on plum pox virus (PPV) encoding a tagged CVYV P1b in place of the canonical HCPro. We used that tag to purify P1b in Nicotiana-benthamiana-infected plants and identified by mass spectrometry an importin-β-like protein similar to importin 7 of Arabidopsis thaliana. We further confirmed the interaction by bimolecular fluorescence complementation assays and defined its nuclear localization in the cell. Further analyses showed a possible role of this N. benthamiana homolog of Importin 7 as a modulator of the RNA silencing suppression activity of P1b.

Investigation and Computational Analysis of the Sulfotransferase (SOT) Gene Family in Potato (Solanum tuberosum): Insights into Sulfur Adjustment for Proper Development and Stimuli Responses

Various kinds of primary metabolisms in plants are modulated through sulfate metabolism, and sulfotransferases (SOTs), which are engaged in sulfur metabolism, catalyze sulfonation reactions. In this study, a genome-wide approach was utilized for the recognition and characterization of SOT family genes in the significant nutritional crop potato (Solanum tuberosum L.). Twenty-nine putative StSOT genes were identified in the potato genome and were mapped onto the nine S. tuberosum chromosomes. The protein motifs structure revealed two highly conserved 5'-phosphosulfate-binding (5' PSB) regions and a 3'-phosphate-binding (3' PB) motif that are essential for sulfotransferase activities. The protein-protein interaction networks also revealed an interesting interaction between SOTs and other proteins, such as PRTase, APS-kinase, protein phosphatase, and APRs, involved in sulfur compound biosynthesis and the regulation of flavonoid and brassinosteroid metabolic processes. This suggests the importance of sulfotransferases for proper potato growth and development and stress responses. Notably, homology modeling of StSOT proteins and docking analysis of their ligand-binding sites revealed the presence of proline, glycine, serine, and lysine in their active sites. An expression essay of StSOT genes via potato RNA-Seq data suggested engagement of these gene family members in plants' growth and extension and responses to various hormones and biotic or abiotic stimuli. Our predictions may be informative for the functional characterization of the SOT genes in potato and other nutritional crops.

A rapid, efficient, and low-cost BiFC protocol and its application in studying in vivo interaction of seed-specific transcription factors, RISBZ and RPBF

Proteins regulate cellular and biological processes in all living organisms. More than 80% of the proteins interact with one another to perform their respective functions; therefore, studying the protein-protein-interaction has gained attention in functional characterization studies. Bimolecular fluorescence complement (BiFC) assay is widely adopted to determine the physical interaction of two proteins in vivo. Here, we developed a simple, yet effective BiFC assay for protein-protein-interaction using transient Agrobacterium-mediated-transformation of onion epidermal cells by taking case study of Rice-P-box-Binding-Factor (RPBF) and rice-seed-specific-bZIP (RISBZ) in vivo interaction. Our result revealed that both the proteins, i.e., RISBZ and RPBF, interacted in the nucleus and cytosol. These two transcription factors are known for their coordinate/synergistic regulation of seed-protein content via concurrent binding to the promoter region of the seed storage protein (SSP) encoding genes. We further validated our results with BiFC assay in Nicotiana by agroinfiltration method, which exhibited similar results as Agrobacterium-mediated-transformation of onion epidermal cells. We also examined the subcellular localization of RISBZ and RPBF to assess the efficacy of the protocol. The subcellular localization and BiFC assay presented here is quite easy-to-follow, reliable, and reproducible, which can be completed within 2-3 days without using costly instruments and technologies that demand a high skill set.

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.

Visualizing protein-protein interactions in plants by rapamycin-dependent delocalization

Identifying protein-protein interactions (PPIs) is crucial for understanding biological processes. Many PPI tools are available, yet only some function within the context of a plant cell. Narrowing down even further, only a few tools allow complex multi-protein interactions to be visualized. Here, we present a conditional in vivo PPI tool for plant research that meets these criteria. Knocksideways in plants (KSP) is based on the ability of rapamycin to alter the localization of a bait protein and its interactors via the heterodimerization of FKBP and FRB domains. KSP is inherently free from many limitations of other PPI systems. This in vivo tool does not require spatial proximity of the bait and prey fluorophores and it is compatible with a broad range of fluorophores. KSP is also a conditional tool and therefore the visualization of the proteins in the absence of rapamycin acts as an internal control. We used KSP to confirm previously identified interactions in Nicotiana benthamiana leaf epidermal cells. Furthermore, the scripts that we generated allow the interactions to be quantified at high throughput. Finally, we demonstrate that KSP can easily be used to visualize complex multi-protein interactions. KSP is therefore a versatile tool with unique characteristics and applications that complements other plant PPI methods.

Genome-Wide Analysis of Potassium Channel Genes in Rice: Expression of the OsAKT and OsKAT Genes under Salt Stress

Potassium (K+), as a vital element, is involved in regulating important cellular processes such as enzyme activity, cell turgor, and nutrient movement in plant cells, which affects plant growth and production. Potassium channels are involved in the transport and release of potassium in plant cells. In the current study, three OsKAT genes and two OsAKT genes, along with 11 nonredundant putative potassium channel genes in the rice genome, were characterized based on their physiochemical properties, protein structure, evolution, duplication, in silico gene expression, and protein-protein interactions. In addition, the expression patterns of OsAKTs and OsKATs were studied in root and shoot tissues under salt stress using real-time PCR in three rice cultivars. K+ channel genes were found to have diverse functions and structures, and OsKATs showed high genetic divergence from other K+ channel genes. Furthermore, the Ka/Ks ratios of duplicated gene pairs from the K+ channel gene family in rice suggested that these genes underwent purifying selection. Among the studied K+ channel proteins, OsKAT1 and OsAKT1 were identified as proteins with high potential N-glycosylation and phosphorylation sites, and LEU, VAL, SER, PRO, HIS, GLY, LYS, TYR, CYC, and ARG amino acids were predicted as the binding residues in the ligand-binding sites of K+ channel proteins. Regarding the coexpression network and KEGG ontology results, several metabolic pathways, including sugar metabolism, purine metabolism, carbon metabolism, glycerophospholipid metabolism, monoterpenoid biosynthesis, and folate biosynthesis, were recognized in the coexpression network of K+ channel proteins. Based on the available RNA-seq data, the K+ channel genes showed differential expression levels in rice tissues in response to biotic and abiotic stresses. In addition, the real-time PCR results revealed that OsAKTs and OsKATs are induced by salt stress in root and shoot tissues of rice cultivars, and OsKAT1 was identified as a key gene involved in the rice response to salt stress. In the present study, we found that the repression of OsAKTs, OsKAT2, and OsKAT2 in roots was related to salinity tolerance in rice. Our findings provide valuable insights for further structural and functional assays of K+ channel genes in rice.

New Insights Into Structure and Function of TIFY Genes in Zea mays and Solanum lycopersicum: A Genome-Wide Comprehensive Analysis

The TIFY gene family, a key plant-specific transcription factor (TF) family, is involved in diverse biological processes including plant defense and growth regulation. Despite TIFY proteins being reported in some plant species, a genome-wide comparative and comprehensive analysis of TIFY genes in plant species can reveal more details. In the current study, the members of the TIFY gene family were significantly increased by the identification of 18 and six new members using maize and tomato reference genomes, respectively. Thus, a genome-wide comparative analysis of the TIFY gene family between 48 tomato (Solanum lycopersicum, a dicot plant) genes and 26 maize (Zea mays, a monocot plant) genes was performed in terms of sequence structure, phylogenetics, expression, regulatory systems, and protein interaction. The identified TIFYs were clustered into four subfamilies, namely, TIFY-S, JAZ, ZML, and PPD. The PPD subfamily was only detected in tomato. Within the context of the biological process, TIFY family genes in both studied plant species are predicted to be involved in various important processes, such as reproduction, metabolic processes, responses to stresses, and cell signaling. The Ka/Ks ratios of the duplicated paralogous gene pairs indicate that all of the duplicated pairs in the TIFY gene family of tomato have been influenced by an intense purifying selection, whereas in the maize genome, there are three duplicated blocks containing Ka/Ks > 1, which are implicated in evolution with positive selection. The amino acid residues present in the active site pocket of TIFY proteins partially differ in each subfamily, although the Mg or Ca ions exist heterogeneously in the centers of the active sites of all the predicted TIFY protein models. Based on the expression profiles of TIFY genes in both plant species, JAZ subfamily proteins are more associated with the response to abiotic and biotic stresses than other subfamilies. In conclusion, globally scrutinizing and comparing the maize and tomato TIFY genes showed that TIFY genes play a critical role in cell reproduction, plant growth, and responses to stress conditions, and the conserved regulatory mechanisms may control their expression.

Genome-wide comparative analysis of Mg transporter gene family between Triticum turgidum and Camelina sativa

Magnesium (Mg) as a bimetal plays critical roles in biochemical processes, membrane stability, and enzyme activity. Mg transporters (MGTs) are involving in maintaining Mg homeostasis in cells. Although the MGT family members have been identified in different plant species, there is no comprehensive analysis of the other plants' MGT genes. In the current study, 62 and 41 non-redundant putative MGT proteins were recognized into the genome of Camelina sativa, and Triticum turgidum and they were compared based on physicochemical properties, protein structure, expression, and interaction. All identified MGTs were classified into three subgroups, NIPA, CorA, and MRS2/MGT, based on conserved-motifs distribution. The results showed that the secondary structure pattern in NIPA and MRS2 subfamily members in both studied plant species were highly similar. Furthermore, MGTs encompass the conserved structures and the critical sites mainly in the metal ion and Mg²⁺ binding centers as well as the catalytic sites were observed. The highest numbers of protein channels were predicted in CorA proteins in both C. sativa and T. turgidum with 24 and 17 channel numbers, respectively. The Ser, Pro, Gly, Lys, Tyr, and Arg amino acids were predicted as the binding residues in MGTs channel regions. The expression pattern of identified genes demonstrated that MGT genes have diverse tissue-specific expression and stress response expression patterns. Besides, 147 co-expressed genes with MGTs were clustered into the eight co-expression nodes involved in N-glycan biosynthesis, protein processing in the endoplasmic reticulum, carbon metabolism, biosynthesis of amino acids, and endocytosis. In the present study, all interpretations are based on in silico predictions, which can be used in further studies related to functional genomics of MGT genes.

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.

Virulence Factors in the Phytopathogen-Host Interactions: An Overview

Phytopathogens are responsible for great losses in agriculture, once they are able to subvert or elude the host defense mechanisms through virulence factors secretion for their dissemination. Herein, it is reviewed phytotoxins that act as virulence factors and are produced by bacterial phytopathogens (Candidatus Liberibacter spp., Erwinia amylovora, Pseudomonas syringae pvs and Xanthomonas spp.) and fungi (Alternaria alternata, Botrytis cinerea, Cochliobolus spp., Fusarium spp., Magnaporthe spp., and Penicillium spp.), which were selected in accordance to their worldwide importance due to the biochemical and economical aspects. In the current review, it is sought to understand the role of virulence factors in the pathogen-host interactions that result in plant diseases.

Ammonium transporter 1 (AMT1) gene family in tomato (Solanum lycopersicum L.): Bioinformatics, physiological and expression analyses under drought and salt stresses

Nitrogen (N) is an essential macronutrient for plants, and mainly taken from the soil as ammonium (NH⁺⁴). It is particularly transported into the plants by AMmonium Transporters (AMTs), which are plasma membrane proteins. In the present study, genome-wide identification, physiological and expression analyses of tomato (Solanum lycopersicum L.) ammonium transporters 1 (SlAMT1) genes under drought and salt stresses were performed. Sequence analyses revealed the presence of variations in SlAMT1s at nucleotide and protein levels. While all the SlAMT1s comprise an ammonium transporter domain (PF00909), the numbers of their transmembrane helices were found to be diverse. Digital expression analyses proved that SlAMT1-3 gene had different expression patterns compared to the others, suggesting its functional diversities. The expression analyses revealed that SlAMT1 genes were 0.16 and 5.94 -fold down-regulated under drought and salt stresses, respectively. The results suggested that expression of SlAMT1 genes were adversely affected by abiotic stress conditions.

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.

Analyses of Protein Interaction Networks Using Computational Tools

The knowledge of protein-protein interactions (PPIs) and PPI networks (PPINs) is the key to starting to understand the biological processes inside the cell. Many computational tools have been designed to help explore PPIs and PPINs, such as those for interaction detection, reliability assessment and interaction network construction. Here, the application of computational tools is reviewed from three perspectives: PPI database construction, PPI prediction, and interaction network construction and analysis. This overview will provide researchers guidance on choosing appropriate methods for exploring PPIs.

Isolation of protein complexes from the model legume Medicago truncatula by tandem affinity purification in hairy root cultures

Tandem affinity purification coupled to mass spectrometry (TAP-MS) is one of the most powerful techniques to isolate protein complexes and elucidate protein interaction networks. Here, we describe the development of a TAP-MS strategy for the model legume Medicago truncatula, which is widely studied for its ability to produce valuable natural products and to engage in endosymbiotic interactions. As biological material, transgenic hairy roots, generated through Agrobacterium rhizogenes-mediated transformation of M. truncatula seedlings, were used. As proof of concept, proteins involved in the cell cycle, transcript processing and jasmonate signalling were chosen as bait proteins, resulting in a list of putative interactors, many of which confirm the interologue concept of protein interactions, and which can contribute to biological information about the functioning of these bait proteins in planta. Subsequently, binary protein-protein interactions among baits and preys, and among preys were confirmed by a systematic yeast two-hybrid screen. Together, by establishing a M. truncatula TAP-MS platform, we extended the molecular toolbox of this model species.

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these "omics" approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs.

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.

Protocol: an improved and universal procedure for whole-mount immunolocalization in plants

Rapid advances in microscopy have boosted research on cell biology. However sample preparation enabling excellent reproducible tissue preservation and cell labeling for in depth microscopic analysis of inner cell layers, tissues and organs still represents a major challenge for immunolocalization studies. Here we describe a protocol for whole-mount immunolocalization of proteins which is applicable to a wide range of plant species. The protocol is improved and robust for optimal sample fixation, tissue clearing and multi-protein staining procedures and can be used in combination with simultaneous detection of specific sequences of nucleic acids. In addition, cell wall and nucleus labelling can be implemented in the protocol, thereby allowing a detailed analysis of morphology and gene expression patterns with single-cell resolution. Besides enabling accurate, high resolution and reproducible protein detection in expression and localization studies, the procedure takes a single working day to complete without the need for robotic equipment.

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.

Expression of a translationally fused TAP-tagged plasma membrane proton pump in Arabidopsis thaliana

The Arabidopsis thaliana plasma membrane proton ATPase genes, AHA1 and AHA2, are the two most highly expressed isoforms of an 11 gene family and are collectively essential for embryo development. We report the translational fusion of a tandem affinity-purification tag to the 5′ end of the AHA1 open reading frame in a genomic clone. Stable expression of TAP-tagged AHA1 in Arabidopsis rescues the embryonic lethal phenotype of endogenous double aha1/aha2 knockdowns. Western blots of SDS-PAGE and Blue Native gels show enrichment of AHA1 in plasma membrane fractions and indicate a hexameric quaternary structure. TAP-tagged AHA1 rescue lines exhibited reduced vertical root growth. Analysis of the plasma membrane and soluble proteomes identified several plasma membrane-localized proteins with alterred abundance in TAP-tagged AHA1 rescue lines compared to wild type. Using affinity-purification mass spectrometry, we uniquely identified two additional AHA isoforms, AHA9 and AHA11, which copurified with TAP-tagged AHA1. In conclusion, we have generated transgenic Arabidopsis lines in which a TAP-tagged AHA1 transgene has complemented all essential endogenous AHA1 and AHA2 functions and have shown that these plants can be used to purify AHA1 protein and to identify in planta interacting proteins by mass spectrometry.

Agroinfiltration of Nicotiana benthamiana leaves for co-localization of regulatory proteins involved in jasmonate signaling

Protein-protein interactions play important roles in many cellular processes, including the regulation of phytohormone signaling pathways. Identification of interacting partners of key proteins involved in the cellular signaling control can provide potentially unexpected insights into the molecular events occurring in any signaling pathway. Over the years, various techniques have been developed to examine protein-protein interactions, but, besides certain advantages, most of them have various pitfalls, such as yielding nonspecific interactions. Therefore, additional information obtained through different methods may be needed to substantiate protein-protein interaction data. One of these techniques involves the co-localization of proteins suspected to interact in the same subcellular compartment. In this chapter, we describe a method for co-expression of proteins associated with jasmonate signaling in Nicotiana benthamiana for studies such as co-localization.