Prediction and functional analysis of the sweet orange protein-protein interaction network

Identification of Candidate Genes Regulating Drought Tolerance in Pearl Millet

Pearl millet is an important crop of the arid and semi-arid ecologies to sustain food and fodder production. The greater tolerance to drought stress attracts us to examine its cellular and molecular mechanisms via functional genomics approaches to augment the grain yield. Here, we studied the drought response of 48 inbreds representing four different maturity groups at the flowering stage. A set of 74 drought-responsive genes were separated into five major phylogenic groups belonging to eight functional groups, namely ABA signaling, hormone signaling, ion and osmotic homeostasis, TF-mediated regulation, molecular adaptation, signal transduction, physiological adaptation, detoxification, which were comprehensively studied. Among the conserved motifs of the drought-responsive genes, the protein kinases and MYB domain proteins were the most conserved ones. Comparative in-silico analysis of the drought genes across millet crops showed foxtail millet had most orthologs with pearl millet. Of 698 haplotypes identified across millet crops, MyC2 and Myb4 had maximum haplotypes. The protein–protein interaction network identified ABI2, P5CS, CDPK, DREB, MYB, and CYP707A3 as major hub genes. The expression assay showed the presence of common as well as unique drought-responsive genes across maturity groups. Drought tolerant genotypes in respective maturity groups were identified from the expression pattern of genes. Among several gene families, ABA signaling, TFs, and signaling proteins were the prospective contributors to drought tolerance across maturity groups. The functionally validated genes could be used as promising candidates in backcross breeding, genomic selection, and gene-editing schemes in pearl millet and other millet crops to increase the yield in drought-prone arid and semi-arid ecologies.

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.

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.

Molecular Analysis of 14-3-3 Genes in Citrus sinensis and Their Responses to Different Stresses

14-3-3 proteins (14-3-3s) are among the most important phosphorylated molecules playing crucial roles in regulating plant development and defense responses to environmental constraints. No report thus far has documented the gene family of 14-3-3s in Citrus sinensis and their roles in response to stresses. In this study, nine 14-3-3 genes, designated as CitGF14s (CitGF14a through CitGF14i) were identified from the latest C. sinensis genome. Phylogenetic analysis classified them into ε-like and non-ε groups, which were supported by gene structure analysis. The nine CitGF14s were located on five chromosomes, and none had duplication. Publicly available RNA-Seq raw data and microarray databases were mined for 14-3-3 expression profiles in different organs of citrus and in response to biotic and abiotic stresses. RT-qPCR was used for further examining spatial expression patterns of CitGF14s in citrus and their temporal expressions in one-year-old C. sinensis “Xuegan” plants after being exposed to different biotic and abiotic stresses. The nine CitGF14s were expressed in eight different organs with some isoforms displayed tissue-specific expression patterns. Six of the CitGF14s positively responded to citrus canker infection (Xanthomonas axonopodis pv. citri). The CitGF14s showed expressional divergence after phytohormone application and abiotic stress treatments, suggesting that 14-3-3 proteins are ubiquitous regulators in C. sinensis. Using the yeast two-hybrid assay, CitGF14a, b, c, d, g, and h were found to interact with CitGF14i proteins to form a heterodimer, while CitGF14i interacted with itself to form a homodimer. Further analysis of CitGF14s co-expression and potential interactors established a 14-3-3s protein interaction network. The established network identified 14-3-3 genes and several candidate clients which may play an important role in developmental regulation and stress responses in this important fruit crop. This is the first study of 14-3-3s in citrus, and the established network may help further investigation of the roles of 14-3-3s in response to abiotic and biotic constraints.

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

PlaPPISite: a comprehensive resource for plant protein-protein interaction sites

BACKGROUND

Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs (e.g., the 3D structural contexts of interaction sites) is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants.

DESCRIPTION

PlaPPISite (http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page.

CONCLUSION

PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species.

Genome-wide identification, characterization, interaction network and expression profile of GAPDH gene family in sweet orange (Citrus sinensis)

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a key glycolytic enzyme that plays important roles in multiple cellular processes including phytohormone signaling, plant development, and transcriptional regulation. Although GAPDH genes have been well characterized in various plant species such as Arabidopsis, tobacco, wheat, rice, and watermelon, comprehensive analysis has yet to be completed at the whole genome level in sweet orange (Citrus sinensis). In this study, six GAPDH genes distributed across four chromosomes were identified within the sweet orange genome. Their gene structures, conserved subunits, and subcellular localization were also characterized. Cis-element analysis of CsGAPDHs’ promoter regions and the results of dark treatments indicate that CsGAPDH may be involved in photosynthesis. CsGAPDH genes expressed either in a tissue-specific manner or constitutively were ultimately identified along with their expression response to phosphorus deficiency treatments. In addition, a dual-luciferase transient assay was performed to reveal the transcriptional activation of CsGAPDH proteins. Gene Ontology (GO) analysis for proteins interacting with CsGAPDHs helped to uncover the roles these CsGAPDHs play in other plant processes such as citrus seed germination. This study provides a systematic analysis of the CsGAPDH gene family in the sweet orange genome, which can serve as a strong foundation for further research into the biochemical properties and physiological functions of CsGAPDHs.

Structural, Functional, and Evolutionary Characterization of Major Drought Transcription Factors Families in Maize

Drought is one of the major threats to the maize yield especially in subtropical production systems. Understanding the genes and regulatory mechanisms of drought tolerance is important to sustain the yield. Transcription factors (TFs) play a major role in gene regulation under drought stress. In the present study, a set of 15 major TF families comprising 1,436 genes was structurally and functionally characterized. The functional annotation indicated that the genes were involved in ABA signaling, ROS scavenging, photosynthesis, stomatal regulation, and sucrose metabolism. Duplication was identified as the primary force in divergence and expansion of TF families. Phylogenetic relationship was developed for individual TF and combined TF families. Phylogenetic analysis clustered the genes into specific and mixed groups. Gene structure analysis revealed that more number of genes were intron-rich as compared to intron-less. Drought-responsive cis-regulatory elements such as ABREA, ABREB, DRE1, and DRECRTCOREAT have been identified. Expression and interaction analyses identified leaf-specific bZIP TF, GRMZM2G140355, as a potential contributor toward drought tolerance in maize. Protein-protein interaction network of 269 drought-responsive genes belonging to different TFs has been provided. The information generated on structural and functional characteristics, expression, and interaction of the drought-related TF families will be useful to decipher the drought tolerance mechanisms and to breed drought-tolerant genotypes in maize.

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

Prediction of cassava protein interactome based on interolog method

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

Prediction of Protein-Protein Interactions

The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field-highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.

Systems biology study of transcriptional and post-transcriptional co-regulatory network sheds light on key regulators involved in important biological processes in Citrus sinensis

Transcriptional and post-transcriptional regulators including transcription regulator, transcription factor and miRNA are the main endogenous molecular elements which control complex cellular mechanisms such as development, growth and response to biotic and abiotic stresses in a coordinated manner in plants. Utilizing the most recent information on such relationships in a plant species, obtained from high-throughput experimental technologies and advanced computational tools, we can reconstruct its co-regulatory network which consequently sheds light on key regulators involved in its important biological processes. In this article, combined systems biology approaches such as mining the literatures, various databases and network reconstruction, analysis, and visualization tools were employed to infer and visualize the coregulatory relationships between miRNAs and transcriptional regulators in Citrus sinensis. Using computationally and experimentally verified miRNA-target interactions and constructed co-expression networks on array-based data, 10 coregulatory networks and 10 corresponding subgraphs include FFL motifs were obtained for 10 distinct tissues/conditions. Then PPI subnetworks were extracted for transcripts/genes included in mentioned subgraphs in order to the functional analysis of extracted coregulatory circuits. These proposed coregulatory connections shed light on precisely identifying C. sinensis metabolic pathways key switches, which are demanded for ultimate goals such as genome editing.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Global De Novo Protein-Protein Interactome Elucidates Interactions of Drought-Responsive Proteins in Horse Gram (Macrotyloma uniflorum)

Inspired by the availability of de novo transcriptome of horse gram (Macrotyloma uniflorum) and recent developments in systems biology studies, the first ever global protein-protein interactome (PPI) map was constructed for this highly drought-tolerant legume. Large-scale studies of PPIs and the constructed database would provide rationale behind the interplay at cascading translational levels for drought stress-adaptive mechanisms in horse gram. Using a bidirectional approach (interolog and domain-based), a high-confidence interactome map and database for horse gram was constructed. Available transcriptomic information for shoot and root tissues of a sensitive (M-191; genotype 1) and a drought-tolerant (M-249; genotype 2) genotype of horse gram was utilized to draw comparative PPI subnetworks under drought stress. High-confidence 6804 interactions were predicted among 1812 proteins covering about one-fourth of the horse gram proteome. The highest number of interactions (33.86%) in horse gram interactome matched with Arabidopsis PPI data. The top five hub nodes mostly included ubiquitin and heat-shock-related proteins. Higher numbers of PPIs were found to be responsive in shoot tissue (416) and root tissue (2228) of genotype 2 compared with shoot tissue (136) and root tissue (579) of genotype 1. Characterization of PPIs using gene ontology analysis revealed that kinase and transferase activities involved in signal transduction, cellular processes, nucleocytoplasmic transport, protein ubiquitination, and localization of molecules were most responsive to drought stress. Hence, these could be framed in stress adaptive mechanisms of horse gram. Being the first legume global PPI map, it would provide new insights into gene and protein regulatory networks for drought stress tolerance mechanisms in horse gram. Information compiled in the form of database (MauPIR) will provide the much needed high-confidence systems biology information for horse gram genes, proteins, and involved processes. This information would ease the effort and increase the efficacy for similar studies on other legumes. Public access is available at http://14.139.59.221/MauPIR/ .

Construction of citrus gene coexpression networks from microarray data using random matrix theory

After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus.

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize.

Citrus sinensis annotation project (CAP): a comprehensive database for sweet orange genome

Citrus is one of the most important and widely grown fruit crop with global production ranking firstly among all the fruit crops in the world. Sweet orange accounts for more than half of the Citrus production both in fresh fruit and processed juice. We have sequenced the draft genome of a double-haploid sweet orange (C. sinensis cv. Valencia), and constructed the Citrus sinensis annotation project (CAP) to store and visualize the sequenced genomic and transcriptome data. CAP provides GBrowse-based organization of sweet orange genomic data, which integrates ab initio gene prediction, EST, RNA-seq and RNA-paired end tag (RNA-PET) evidence-based gene annotation. Furthermore, we provide a user-friendly web interface to show the predicted protein-protein interactions (PPIs) and metabolic pathways in sweet orange. CAP provides comprehensive information beneficial to the researchers of sweet orange and other woody plants, which is freely available at http://citrus.hzau.edu.cn/.