Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

N-terminal proteoforms may engage in different protein complexes

Alternative translation initiation and alternative splicing may give rise to N-terminal proteoforms, proteins that differ at their N-terminus compared with their canonical counterparts. Such proteoforms can have altered localizations, stabilities, and functions. Although proteoforms generated from splice variants can be engaged in different protein complexes, it remained to be studied to what extent this applies to N-terminal proteoforms. To address this, we mapped the interactomes of several pairs of N-terminal proteoforms and their canonical counterparts. First, we generated a catalogue of N-terminal proteoforms found in the HEK293T cellular cytosol from which 22 pairs were selected for interactome profiling. In addition, we provide evidence for the expression of several N-terminal proteoforms, identified in our catalogue, across different human tissues, as well as tissue-specific expression, highlighting their biological relevance. Protein-protein interaction profiling revealed that the overlap of the interactomes for both proteoforms is generally high, showing their functional relation. We also showed that N-terminal proteoforms can be engaged in new interactions and/or lose several interactions compared with their canonical counterparts, thus further expanding the functional diversity of proteomes.

The protein kinases of Dictyostelia and their incorporation into a signalome

Protein kinases are major regulators of cellular processes, but the roles of most kinases remain unresolved. Dictyostelid social amoebas have been useful in identifying functions for 30% of its kinases in cell migration, cytokinesis, vesicle trafficking, gene regulation and other processes but their upstream regulators and downstream effectors are mostly unknown. Comparative genomics can assist to distinguish between genes involved in deeply conserved core processes and those involved in species-specific innovations, while co-expression of genes as evident from comparative transcriptomics can provide cues to the protein complement of regulatory networks. Genomes and developmental and cell-type specific transcriptomes are available for species that span the 0.5 billion years of evolution of Dictyostelia from their unicellular ancestors. In this work we analysed conservation and change in the abundance, functional domain architecture and developmental regulation of protein kinases across the 4 major taxon groups of Dictyostelia. All data are summarized in annotated phylogenetic trees of the kinase subtypes and accompanied by functional information of all kinases that were experimentally studied. We detected 393 different protein kinase domains across the five studied genomes, of which 212 were fully conserved. Conservation was highest (71%) in the previously defined AGC, CAMK, CK1, CMCG, STE and TKL groups and lowest (26%) in the "other" group of typical protein kinases. This was mostly due to species-specific single gene amplification of "other" kinases. Apart from the AFK and α-kinases, the atypical protein kinases, such as the PIKK and histidine kinases were also almost fully conserved. The phylogeny-wide developmental and cell-type specific expression profiles of the protein kinase genes were combined with profiles from the same transcriptomic experiments for the families of G-protein coupled receptors, small GTPases and their GEFs and GAPs, the transcription factors and for all genes that upon lesion generate a developmental defect. This dataset was subjected to hierarchical clustering to identify clusters of co-expressed genes that potentially act together in a signalling network. The work provides a valuable resource that allows researchers to identify protein kinases and other regulatory proteins that are likely to act as intermediates in a network of interest.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Kinase-dead mutation: A novel strategy for improving soybean resistance to soybean cyst nematode Heterodera glycines

Protein kinases phosphorylate proteins for functional changes and are involved in nearly all cellular processes, thereby regulating almost all aspects of plant growth and development, and responses to biotic and abiotic stresses. We generated two independent co-expression networks of soybean genes using control and stress response gene expression data and identified 392 differentially highly interconnected kinase hub genes among the two networks. Of these 392 kinases, 90 genes were identified as "syncytium highly connected hubs", potentially essential for activating kinase signalling pathways in the nematode feeding site. Overexpression of wild-type coding sequences of five syncytium highly connected kinase hub genes using transgenic soybean hairy roots enhanced plant susceptibility to soybean cyst nematode (SCN; Heterodera glycines) Hg Type 0 (race 3). In contrast, overexpression of kinase-dead variants of these five syncytium kinase hub genes significantly enhanced soybean resistance to SCN. Additionally, three of the five tested kinase hub genes enhanced soybean resistance to SCN Hg Type 1.2.5.7 (race 2), highlighting the potential of the kinase-dead approach to generate effective and durable resistance against a wide range of SCN Hg types. Subcellular localization analysis revealed that kinase-dead mutations do not alter protein cellular localization, confirming the structure-function of the kinase-inactive variants in producing loss-of-function phenotypes causing significant decrease in nematode susceptibility. Because many protein kinases are highly conserved and are involved in plant responses to various biotic and abiotic stresses, our approach of identifying kinase hub genes and their inactivation using kinase-dead mutation could be translated for biotic and abiotic stress tolerance.

A novel insight into transcriptional and epigenetic regulation underlying sex expression and flower development in melon (Cucumis melo L.)

Melon (Cucumis melo L.) is an important cucurbit and has been considered as a model plant for studying sex determination. The four most common sexual morphotypes in melon are monoecious (A-G-M), gynoecious (--ggM-), andromonoecious (A-G-mm), and hermaphrodite (--ggmm). Sex expression in melons is complex, as the genes and associated networks that govern the sex expression are not fully explored. Recently, RNA-seq transcriptomic profiling, ChIP-qPCR analysis integrated with gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathways predicted the differentially expressed genes including sex-specific ACS and ACO genes, in regulating the sex-expression, phytohormonal cross-talk, signal transduction, and secondary metabolism in melons. Integration of transcriptional control through genetic interaction in between the ACS7, ACS11, and WIP1 in epistatic or hypostatic manner, along with the recruitment of H3K9ac and H3K27me3, epigenetically, overall determine sex expression. Alignment of protein sequences for establishing phylogenetic evolution, motif comparison, and protein-protein interaction supported the structural conservation while presence of the conserved hydrophilic and charged residues across the diverged evolutionary group predicted the functional conservation of the ACS protein. Presence of the putative cis-binding elements or DNA motifs, and its further comparison with DAP-seq-based cistrome and epicistrome of Arabidopsis, unraveled strong ancestry of melons with Arabidopsis. Motif comparison analysis also characterized putative genes and transcription factors involved in ethylene biosynthesis, signal transduction, and hormonal cross-talk related to sex expression. Overall, we have comprehensively reviewed research findings for a deeper insight into transcriptional and epigenetic regulation of sex expression and flower development in melons.

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.

Prediction of host-pathogen protein interactions by extended network model

Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy against infectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interaction pairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope of this paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rate of unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform this study extensively, encoding methods and learning algorithms of several proteins were tested. Along with human as the host organism, two different pathogen organisms were used in the experiments. For each combination of protein-encoding and prediction method, both the original prediction algorithms were tested using only pathogen-host interactions and the same methodwas testedagain after integrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions of different species on the prediction performance of state-of-the-art methods was also observed. Successwas measured in terms of Matthews correlation coefficient, precision, recall, F1 score, and accuracy metrics. Empirical results showed that integrating the host and pathogen interactions yields better performance consistently in almost all experiments.

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.

The pathogen Moniliophthora perniciosa promotes differential proteomic modulation of cacao genotypes with contrasting resistance to witches´ broom disease

BACKGROUND

Witches' broom disease (WBD) of cacao (Theobroma cacao L.), caused by Moniliophthora perniciosa, is the most important limiting factor for the cacao production in Brazil. Hence, the development of cacao genotypes with durable resistance is the key challenge for control the disease. Proteomic methods are often used to study the interactions between hosts and pathogens, therefore helping classical plant breeding projects on the development of resistant genotypes. The present study compared the proteomic alterations between two cacao genotypes standard for WBD resistance and susceptibility, in response to M. perniciosa infection at 72 h and 45 days post-inoculation; respectively the very early stages of the biotrophic and necrotrophic stages of the cacao x M. perniciosa interaction.

RESULTS

A total of 554 proteins were identified, being 246 in the susceptible Catongo and 308 in the resistant TSH1188 genotypes. The identified proteins were involved mainly in metabolism, energy, defense and oxidative stress. The resistant genotype showed more expressed proteins with more variability associated with stress and defense, while the susceptible genotype exhibited more repressed proteins. Among these proteins, stand out pathogenesis related proteins (PRs), oxidative stress regulation related proteins, and trypsin inhibitors. Interaction networks were predicted, and a complex protein-protein interaction was observed. Some proteins showed a high number of interactions, suggesting that those proteins may function as cross-talkers between these biological functions.

CONCLUSIONS

We present the first study reporting the proteomic alterations of resistant and susceptible genotypes in the T. cacao x M. perniciosa pathosystem. The important altered proteins identified in the present study are related to key biologic functions in resistance, such as oxidative stress, especially in the resistant genotype TSH1188, that showed a strong mechanism of detoxification. Also, the positive regulation of defense and stress proteins were more evident in this genotype. Proteins with significant roles against fungal plant pathogens, such as chitinases, trypsin inhibitors and PR 5 were also identified, and they may be good resistance markers. Finally, important biological functions, such as stress and defense, photosynthesis, oxidative stress and carbohydrate metabolism were differentially impacted with M. perniciosa infection in each genotype.

Global Transcriptome Profiling of Multiple Porcine Organs Reveals Toxoplasma gondii-Induced Transcriptional Landscapes

We characterized the porcine tissue transcriptional landscapes that follow Toxoplasma gondii infection. RNAs were isolated from liver, spleen, cerebral cortex, lung, and mesenteric lymph nodes (MLNs) of T. gondii-infected and uninfected (control) pigs at days 6 and 18 postinfection, and were analyzed using next-generation sequencing (RNA-seq). T. gondii altered the expression of 178, 476, 199, 201, and 362 transcripts at 6 dpi and 217, 223, 347, 119, and 161 at 18 dpi in the infected brain, liver, lung, MLNs and spleen, respectively. The differentially expressed transcripts (DETs) were grouped into five expression patterns and 10 sub-clusters. Gene Ontology enrichment and pathway analysis revealed that immune-related genes dominated the overall transcriptomic signature and that metabolic processes, such as steroid biosynthesis, and metabolism of lipid and carboxylic acid, were downregulated in infected tissues. Co-expression network analysis identified transcriptional modules associated with host immune response to infection. These findings not only show how T. gondii infection alters porcine transcriptome in a tissue-specific manner, but also offer a gateway for testing new hypotheses regarding human response to T. gondii infection.

The Applications of Clustering Methods in Predicting Protein Functions

Background:

The understanding of protein function is essential to the study of biological processes. However, the prediction of protein function has been a difficult task for bioinformatics to overcome. This has resulted in many scholars focusing on the development of computational methods to address this problem.

Objective:

In this review, we introduce the recently developed computational methods of protein function prediction and assess the validity of these methods. We then introduce the applications of clustering methods in predicting protein functions.

Computational identification of protein-protein interactions in model plant proteomes

Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

The Protein-Protein Interaction Network of Litopenaeus vannamei Haemocytes

Protein–protein interaction networks (PINs) have been constructed in various organisms and utilized to conduct evolutionary analyses and functional predictions. Litopenaeus vannamei is a high-valued commercial aquaculture species with an uncharacterized interactome. With the development of RNA-seq techniques and systems biology, it is possible to obtain genome-wide transcriptional information for L. vannamei and construct a systematic network based on these data. In this work, based on the RNA-seq of haemocytes we constructed the first L. vannamei PIN including 4,858 proteins and 104,187 interactions. The PIN constructed here is the first large-scale PIN for shrimp. The confidence scores of interactions in the PIN were evaluated on the basis of sequence homology and genetic relationships. The immune-specific sub-network was extracted from global PIN, and more than a third of proteins were found in signaling pathways in the sub-network, which indicates an inseparable relationship between signaling processes and immune mechanisms. Six selected signaling pathways were constructed at different age groups based on evolutionary analyses. Furthermore, we showed that the functions of the pathways’ proteins were associated with their evolutionary history based on the evolutionary analyses combining with protein functional analyses. In addition, the functions of 1,955 unclassified proteins which were associated with 3,191 unigenes were assigned using the PIN, which account for approximately 70.3 and 44.9% of the previously unclassified proteins and unigenes in the network, respectively. The annotation of unclassified proteins and unigenes based on the PIN provides new candidates for further functional studies. The immune-specific sub-network and the pathways extracted from the PIN provide a novel information source for studying of immune mechanisms and disease resistances in shrimp.

Kingdom-wide comparison reveals the evolution of diurnal gene expression in Archaeplastida

Plants have adapted to the diurnal light-dark cycle by establishing elaborate transcriptional programs that coordinate many metabolic, physiological, and developmental responses to the external environment. These transcriptional programs have been studied in only a few species, and their function and conservation across algae and plants is currently unknown. We performed a comparative transcriptome analysis of the diurnal cycle of nine members of Archaeplastida, and we observed that, despite large phylogenetic distances and dramatic differences in morphology and lifestyle, diurnal transcriptional programs of these organisms are similar. Expression of genes related to cell division and the majority of biological pathways depends on the time of day in unicellular algae but we did not observe such patterns at the tissue level in multicellular land plants. Hence, our study provides evidence for the universality of diurnal gene expression and elucidates its evolutionary history among different photosynthetic eukaryotes.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

Background

Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.

Methods

In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l₂-regularized logistic regression model.

Results

The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance.

Conclusions

The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4873-9) contains supplementary material, which is available to authorized users.

A computational framework for distinguishing direct versus indirect interactions in human functional protein-protein interaction networks

Recognition of indirect interactions is instrumental to in silico reconstruction of signaling pathways and sheds light on the exploration of unknown physical paths between two indirectly interacting genes. However, very limited computational methods have explicitly exploited the indirect interactions with experimental evidence thus far. In this work, we attempt to distinguish direct versus indirect interactions in human functional protein-protein interaction (PPI) networks via a predictive l₂-regularized logistic regression model built on the experimental data. The l₂-regularized logistic regression method is adopted to counteract the potential homolog noise and reduce the computational complexity on large training data. Computational results show that the proposed model demonstrates promising performance even though the training data are highly skewed. From the 304 799 PPIs that are curated in several databases, the proposed method detects 23 131 indirect interactions, most of which have been verified by the breadth-first graph search algorithm to find dozens of physical paths between the interacting partners. Pathway enrichment analysis shows that most of the physical paths can be mapped onto more than one human signaling pathway, indicating that there do exist a series of biochemical signals between the two indirectly interacting genes. The interactome-scale computational results promise to provide useful cues to the following applications: (1) exploration of unknown physical PPIs or physical paths between two indirectly interacting genes; (2) amending or extending the existing signaling pathways; (3) recognition of the physical PPIs for druggable target discovery.

Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource

An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.

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.

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.

OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation

Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

BACKGROUND

Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.

RESULTS

In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway.

CONCLUSIONS

Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.

Personalized characterization of diseases using sample-specific networks

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Proteomic Profiling and the Predicted Interactome of Host Proteins in Compatible and Incompatible Interactions Between Soybean and Fusarium virguliforme

Sudden death syndrome (SDS) is a complex of two diseases of soybean (Glycine max), caused by the soil borne pathogenic fungus Fusarium virguliforme. The root rot and leaf scorch diseases both result in significant yield losses worldwide. Partial SDS resistance has been demonstrated in multiple soybean cultivars. This study aimed to highlight proteomic changes in soybean roots by identifying proteins which are differentially expressed in near isogenic lines (NILs) contrasting at the Rhg1/Rfs2 locus for partial resistance or susceptibility to SDS. Two-dimensional gel electrophoresis resolved approximately 1000 spots on each gel; 12 spots with a significant (P < 0.05) difference in abundance of 1.5-fold or more were picked, trypsin-digested, and analyzed using quadruple time-of-flight tandem mass spectrometry. Several spots contained more than one protein, so that 18 distinct proteins were identified overall. A functional analysis performed to categorize the proteins depicted that the major pathways altered by fungal infection include disease resistance, stress tolerance, and metabolism. This is the first report which identifies proteins whose abundances are altered in response to fungal infection leading to SDS. The results provide valuable information about SDS resistance in soybean plants, and plant partial resistance responses in general. More importantly, several of the identified proteins could be good candidates for the development of SDS-resistant soybean plants.

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.

A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways

A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus.

Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways

Epstein-Barr virus (EBV) plays important roles in the origin and the progression of human carcinomas, e.g. diffuse large B cell tumors, T cell lymphomas, etc. Discovering EBV targeted human genes and signaling pathways is vital to understand EBV tumorigenesis. In this study we propose a noise-tolerant homolog knowledge transfer method to reconstruct functional protein-protein interactions (PPI) networks between Epstein-Barr virus and Homo sapiens. The training set is augmented via homolog instances and the homolog noise is counteracted by support vector machine (SVM). Additionally we propose two methods to define subcellular co-localization (i.e. stringent and relaxed), based on which to further derive physical PPI networks. Computational results show that the proposed method achieves sound performance of cross validation and independent test. In the space of 648,672 EBV-human protein pairs, we obtain 51,485 functional interactions (7.94%), 869 stringent physical PPIs and 46,050 relaxed physical PPIs. Fifty-eight evidences are found from the latest database and recent literature to validate the model. This study reveals that Epstein-Barr virus interferes with normal human cell life, such as cholesterol homeostasis, blood coagulation, EGFR binding, p53 binding, Notch signaling, Hedgehog signaling, etc. The proteome-wide predictions are provided in the supplementary file for further biomedical research.

Reconstruction and Application of Protein-Protein Interaction Network

The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.

Tools of the trade: studying molecular networks in plants

Driven by recent technological improvements, genes can be now studied in a larger biological context. Genes and their protein products rarely operate as a single entity and large-scale mapping by protein-protein interactions can unveil the molecular complexes that form in the cell to carry out various functions. Expression analysis under multiple conditions, supplemented with protein-DNA binding data can highlight when genes are active and how they are regulated. Representing these data in networks and finding strongly connected sub-graphs has proven to be a powerful tool to predict the function of unknown genes. As such networks are gradually becoming available for various plant species, it becomes possible to study how networks evolve. This review summarizes currently available network data and related tools for plants. Furthermore we aim to provide an outlook of future analyses that can be done in plants based on work done in other fields.

Topological characteristics of target genes regulated by abiotic-stress-responsible miRNAs in a rice interactome network

A great number of microRNAs (miRNAs) have been identified in responding and acting in gene regulatory networks associated with plant tolerance to abiotic stress conditions, such as drought, salinity, and high temperature. The topological exploration of target genes regulated by abiotic-stress-responsible miRNAs (ASRmiRs) in a network facilitates to discover the molecular basis of plant abiotic stress response. This study was based on the staple food rice (Oryza sativa) in which ASRmiRs were manually curated. After having compared the topological properties of target genes (stress-miR-targets) with those (non-stress-miR-targets) not regulated by ASRmiRs in a rice interactome network, we found that stress-miR-targets exhibited distinguishable topological properties. The interaction probability analysis and k-core decomposition showed that stress-miR-targets preferentially interacted with non-stress-miR-targets and located at the peripheral positions in the network. Our results indicated an obvious topological distinction between the two types of genes, reflecting the specific mechanisms of action of stress-miR-targets in rice abiotic stress response. Also, the results may provide valuable clues to elucidate molecular mechanisms of crop response to abiotic stress.

A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks

Signaling pathways play important roles in understanding the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. Protein-protein interaction (PPI) networks are commonly-used infrastructure to infer signaling pathways. However, PPI networks generally carry no information of upstream/downstream relationship between interacting proteins, which retards our inferring the signal flow of signaling pathways. In this work, we propose a simple feature construction method to train a SVM (support vector machine) classifier to predict PPI upstream/downstream relations. The domain based asymmetric feature representation naturally embodies domain-domain upstream/downstream relations, providing an unconventional avenue to predict the directionality between two objects. Moreover, we propose a semantically interpretable decision function and a macro bag-level performance metric to satisfy the need of two-instance depiction of an interacting protein pair. Experimental results show that the proposed method achieves satisfactory cross validation performance and independent test performance. Lastly, we use the trained model to predict the PPIs in HPRD, Reactome and IntAct. Some predictions have been validated against recent literature.

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.

Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource

Background

Physcomitrella patens, a haploid dominant plant, is fast becoming a useful molecular genetics and bioinformatics tool due to its key phylogenetic position as a bryophyte in the post-genomic era. Genome sequences from select reference species were compared bioinformatically to Physcomitrella patens using reciprocal blasts with the InParanoid software package. A reference protein interaction database assembled using MySQL by compiling BioGrid, BIND, DIP, and Intact databases was queried for moss orthologs existing for both interacting partners. This method has been used to successfully predict interactions for a number of angiosperm plants.

Results

The first predicted protein-protein interactome for a bryophyte based on the interolog method contains 67,740 unique interactions from 5,695 different Physcomitrella patens proteins. Most conserved interactions among proteins were those associated with metabolic processes. Over-represented Gene Ontology categories are reported here.

Conclusion

Addition of moss, a plant representative 200 million years diverged from angiosperms to interactomic research greatly expands the possibility of conducting comparative analyses giving tremendous insight into network evolution of land plants. This work helps demonstrate the utility of “guilt-by-association” models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches. Included with this dataset is a method for characterizing subnetworks and investigating specific processes, such as the Calvin-Benson-Bassham cycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0524-1) contains supplementary material, which is available to authorized users.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between Salmonella and human proteins

Pathogen-host protein-protein interaction (PPI) plays an important role in revealing the underlying pathogenesis of viruses and bacteria. The need of rapidly mapping proteome-wide pathogen-host interactome opens avenues for and imposes burdens on computational modeling. For Salmonella typhimurium, only 62 interactions with human proteins are reported to date, and the computational modeling based on such a small training data is prone to yield model overfitting. In this work, we propose a multi-instance transfer learning method to reconstruct the proteome-wide Salmonella-human PPI networks, wherein the training data is augmented by homolog knowledge transfer in the form of independent homolog instances. We use AdaBoost instance reweighting to counteract the noise from homolog instances, and deliberately design three experimental settings to validate the assumption that the homolog instances are effective to address the problems of data scarcity and data unavailability. The experimental results show that the proposed method outperforms the existing models and some predictions are validated by the findings from recent literature. Lastly, we conduct gene ontology based clustering analysis of the predicted networks to provide insights into the pathogenesis of Salmonella.

The (r)evolution of gene regulatory networks controlling Arabidopsis plant reproduction: a two-decade history

Successful plant reproduction relies on the perfect orchestration of singular processes that culminate in the product of reproduction: the seed. The floral transition, floral organ development, and fertilization are well-studied processes and the genetic regulation of the various steps is being increasingly unveiled. Initially, based predominantly on genetic studies, the regulatory pathways were considered to be linear, but recent genome-wide analyses, using high-throughput technologies, have begun to reveal a different scenario. Complex gene regulatory networks underlie these processes, including transcription factors, microRNAs, movable factors, hormones, and chromatin-modifying proteins. Here we review recent progress in understanding the networks that control the major steps in plant reproduction, showing how new advances in experimental and computational technologies have been instrumental. As these recent discoveries were obtained using the model species Arabidopsis thaliana, we will restrict this review to regulatory networks in this important model species. However, more fragmentary information obtained from other species reveals that both the developmental processes and the underlying regulatory networks are largely conserved, making this review also of interest to those studying other plant species.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens

Background

Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.

Results

In this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses.

Conclusions

The Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-245) contains supplementary material, which is available to authorized users.

Global protein-protein interaction network of rice sheath blight pathogen

Rhizoctonia solani is the major pathogenic fungi of rice sheath blight. It is responsible for the most serious disease of rice (Oryza sativa L.) and causes significant yield losses in rice-growing countries. Identifying the protein-protein interaction (PPI) maps of R. solani can provide insights into the potential pathogenic mechanisms and assign putative functions to unknown genes. Here, we exploited a PPI map of R. solani anastomosis group 1 IA (AG-1 IA) based on the interolog and domain-domain interaction methods. We constructed a core subset of high-confidence protein networks consisting of 6705 interactions among 1773 proteins. The high quality of the network was revealed by comprehensive methods, including yeast two-hybrid experiments. Pathogenic interaction subnetwork, secreted proteins subnetwork, and mitogen-activated protein kinase (MAPK) cascade subnetwork and their interacting partners were constructed and analyzed. Moreover, to exactly predict the pathogenic factors, the expression levels of the interaction proteins were investigated by analyzing RNA sequences that consisted of samples from the entire infection progress. The PPIs offer an exceptionally rich source of data that can be used to understand the gene functions and biological processes of this serious disease at the system level.

IIS--Integrated Interactome System: a web-based platform for the annotation, analysis and visualization of protein-metabolite-gene-drug interactions by integrating a variety of data sources and tools

BACKGROUND

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted.

RESULTS

We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web.

CONCLUSIONS

We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

An integrated approach (CLuster Analysis Integration Method) to combine expression data and protein-protein interaction networks in agrigenomics: application on Arabidopsis thaliana

Experimental co-expression data and protein-protein interaction networks are frequently used to analyze the interactions among genes or proteins. Recent studies have investigated methods to integrate these two sources of information. We propose a new method to integrate co-expression data obtained through DNA microarray analysis (MA) and protein-protein interaction (PPI) network data, and apply it to Arabidopsis thaliana. The proposed method identifies small subsets of highly interacting proteins. Based on the analysis of the basis of co-localization and mRNA developmental expression, we show that these groups provide important biological insights; additionally, these subsets are significantly enriched with respect to KEGG Pathways and can be used to predict successfully whether proteins belong to known pathways. Thus, the method is able to provide relevant biological information and support the functional identification of complex genetic traits of economic value in plant agrigenomics research. The method has been implemented in a prototype software tool named CLAIM (CLuster Analysis Integration Method) and can be downloaded from http://bio.cs.put.poznan.pl/research_fields . CLAIM is based on the separate clustering of MA and PPI data; the clusters are merged in a special graph; cliques of this graph are subsets of strongly connected proteins. The proposed method was successfully compared with existing methods. CLAIM appears to be a useful semi-automated tool for protein functional analysis and warrants further evaluation in agrigenomics research.

Coexpression patterns indicate that GPI-anchored non-specific lipid transfer proteins are involved in accumulation of cuticular wax, suberin and sporopollenin

The non-specific lipid transfer proteins (nsLTP) are unique to land plants. The nsLTPs are characterized by a compact structure with a central hydrophobic cavity and can be classified to different types based on sequence similarity, intron position or spacing between the cysteine residues. The type G nsLTPs (LTPGs) have a GPI-anchor in the C-terminal region which attaches the protein to the exterior side of the plasma membrane. The function of these proteins, which are encoded by large gene families, has not been systematically investigated so far. In this study we have explored microarray data to investigate the expression pattern of the LTPGs in Arabidopsis and rice. We identified that the LTPG genes in each plant can be arranged in three expression modules with significant coexpression within the modules. According to expression patterns and module sizes, the Arabidopsis module AtI is functionally equivalent to the rice module OsI, AtII corresponds to OsII and AtIII is functionally comparable to OsIII. Starting from modules AtI, AtII and AtIII we generated extended networks with Arabidopsis genes coexpressed with the modules. Gene ontology analyses of the obtained networks suggest roles for LTPGs in the synthesis or deposition of cuticular waxes, suberin and sporopollenin. The AtI-module is primarily involved with cuticular wax, the AtII-module with suberin and the AtIII-module with sporopollenin. Further transcript analysis revealed that several transcript forms exist for several of the LTPG genes in both Arabidopsis and rice. The data suggests that the GPI-anchor attachment and localization of LTPGs may be controlled to some extent by alternative splicing.

Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins

Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

A guide to CORNET for the construction of coexpression and protein-protein interaction networks

To enable easy access and interpretation of heterogenous and scattered data, we have developed a user-friendly tool for data mining and integration in Arabidopsis thaliana, designated CORrelation NETworks (acronym CORNET), allowing browsing of microarray data, construction of coexpression and protein-protein interactions (PPIs), analysis of gene association and transcription factor (TF) regulatory networks, and exploration of diverse functional annotations. CORNET consists of three tools that can be used individually or in combination, namely, the coexpression tool, the PPI tool, and the TF tool. Different search options are implemented to enable the creation of networks centered around multiple input genes or proteins. Functional annotation resources are included to retrieve relevant literature, phenotypes, localization, gene ontology, plant ontology, and biological pathways. Networks and associated evidence of the majority of the currently available data types are visualized in Cytoscape. CORNET is available at https://bioinformatics.psb.ugent.be/cornet.

Prediction and characterization of protein-protein interaction network in Xanthomonas oryzae pv. oryzae PXO99 A

Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of bacterial blight disease in rice, is one of the most serious plant pathogens worldwide. In the current analysis, we constructed a protein-protein interaction network of Xoo strain PXO99(A) with two computational approaches (interolog method and domain combination method), and verified by K-Nearest Neighbors classification method. The predicted PPI network of Xoo PXO99(A) contains 36,886 interactions among 1988 proteins. KNN verification and GO annotation confirm the reliability of the network. Detailed analysis of flagellar synthesis and chemotaxis system shows that σ factors (especially σ(28), σ(54)) in Xoo PXO99(A) are very important for flagellar synthesis and motility, and transcription factors RpoA, RpoB and RpoC are hubs to connect most σ factors. Furthermore, Xoo PXO99(A) may have both cAMP and c-di-GMP signal transduction system, and the latter is especially important for this plant pathogen. This study therefore provides valuable clues to explore the pathogenicity and metabolic regulation of Xoo PXO99(A).

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

Background

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

Methods

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

Results and conclusions

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

Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS

Background

The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.

Results

In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants.

Conclusion

We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.

A multilevel gamma-clustering layout algorithm for visualization of biological networks

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs.

Complementing the Eukaryotic Protein Interactome

Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels.

AVAILABILITY

http://services.bio.ifi.lmu.de/coin-db/.