Prediction and characterization of protein-protein interaction networks in swine

Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat

Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches—interolog and domain-based methods—a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum–T. caries and T. aestivum–T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.

In silico characterization, docking, and simulations to understand host-pathogen interactions in an effort to enhance crop production in date palms

Food safety remains a significant challenge despite the growth and development in agricultural research and the advent of modern biotechnological and agricultural tools. Though the agriculturist struggles to aid the growing population's needs, many pathogen-based plant diseases by their direct impact on cell division and tissue development have led to the loss of tons of food crops every year. Though there are many conventional and traditional methods to overcome this issue, the amount and time spend are huge. Scientists have developed systems biology tools to study the root cause of the problem and rectify it. Host-pathogen protein interactions (HPIs) have a promising role in identifying the pathogens' strategy to conquer the host organism. In this paper, the interactions between the host Rhynchophorus ferrugineus (an invasive wood-boring pest that destroys palm) and the pathogens Proteus mirabilis, Serratia marcescens, and Klebsiella pneumoniae are comprehensively studied using protein-protein interactions, molecular docking, and followed by 200 ns molecular dynamic simulations. This study elucidates the structural and functional basis of these proteins leading towards better plant health, production, and reliability.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Extracellular Vesicles Produced by the Probiotic Propionibacterium freudenreichii CIRM-BIA 129 Mitigate Inflammation by Modulating the NF-κB Pathway

Extracellular vesicles (EVs) are nanometric spherical structures involved in intercellular communication, whose production is considered to be a widespread phenomenon in living organisms. Bacterial EVs are associated with several processes that include survival, competition, pathogenesis, and immunomodulation. Among probiotic Gram-positive bacteria, some Propionibacterium freudenreichii strains exhibit anti-inflammatory activity, notably via surface proteins such as the surface-layer protein B (SlpB). We have hypothesized that, in addition to surface exposure and secretion of proteins, P. freudenreichii may produce EVs and thus export immunomodulatory proteins to interact with the host. In order to demonstrate their production in this species, EVs were purified from cell-free culture supernatants of the probiotic strain P. freudenreichii CIRM-BIA 129, and their physicochemical characterization, using transmission electron microscopy and nanoparticle tracking analysis (NTA), revealed shapes and sizes typical of EVs. Proteomic characterization showed that EVs contain a broad range of proteins, including immunomodulatory proteins such as SlpB. In silico protein-protein interaction predictions indicated that EV proteins could interact with host proteins, including the immunomodulatory transcription factor NF-κB. This potential interaction has a functional significance because EVs modulate inflammatory responses, as shown by IL-8 release and NF-κB activity, in HT-29 human intestinal epithelial cells. Indeed, EVs displayed an anti-inflammatory effect by modulating the NF-κB pathway; this was dependent on their concentration and on the proinflammatory inducer (LPS-specific). Moreover, while this anti-inflammatory effect partly depended on SlpB, it was not abolished by EV surface proteolysis, suggesting possible intracellular sites of action for EVs. This is the first report on identification of P. freudenreichii-derived EVs, alongside their physicochemical, biochemical and functional characterization. This study has enhanced our understanding of the mechanisms associated with the probiotic activity of P. freudenreichii and identified opportunities to employ bacterial-derived EVs for the development of bioactive products with therapeutic effects.

Construction and analysis of an interologous protein-protein interaction network of Camellia sinensis leaf (TeaLIPIN) from RNA-Seq data sets

An interologous PPI network of tea leaf is designed by developing reference transcriptome assembly and using experimentally validated PPIs in plants. Key regulatory proteins are proposed and potential TFs are predicted. Worldwide, tea (Camellia sinensis) is the most consumed beverage primarily due to the taste, flavour, and aroma of its newly formed leaves; and has been used as an important ingredient in several traditional medicinal systems because of its antioxidant properties. For this medicinally and commercially important plant, design principles of gene-regulatory and protein-protein interaction (PPI) networks at sub-cellular level are largely un-characterized. In this work, we report a tea leaf interologous PPI network (TeaLIPIN) consisting of 11,208 nodes and 197,820 interactions. A reference transcriptome assembly was first developed from all the 44 samples of 6 publicly available leaf transcriptomes (1,567,288,290 raw reads). By inferring the high-confidence interactions among potential proteins coded by these transcripts using known experimental information about PPIs in 14 plants, an interologous PPI network was constructed and its modular architecture was explored. Comparing this network with 10,000 realizations of two types of corresponding random networks (Erdős-Rényi and Barabási-Albert models) and examining over three network centrality metrics, we predict 2750 bottleneck proteins (having p values < 0.01). 247 of these are deduced to have transcription factor domains by in-house developed HMM models of known plant TFs and these were also mapped to the draft tea genome for searching their probable loci of origin. Co-expression analysis of the TeaLIPIN proteins was also performed and top ranking modules are elaborated. We believe that the proposed novel methodology can easily be adopted to develop and explore the PPI interactomes in other plant species by making use of the available transcriptomic data.

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.

Analysis of Predicted Host-Parasite Interactomes Reveals Commonalities and Specificities Related to Parasitic Lifestyle and Tissues Tropism

The study of molecular host–parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require a clear understanding of the molecular crosstalk between parasites and their hosts. Yet, large-scale experimental identification of host–parasite molecular interactions remains challenging, and the use of computational predictions becomes then necessary. Here, we propose a computational integrative approach to predict host—parasite protein—protein interaction (PPI) networks resulting from the human infection by 15 different eukaryotic parasites. We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host–parasite system. Our approach uses either the parasites predicted secretome and membrane proteins, or only the secretome, depending on whether they are uni- or multi-cellular, respectively, to reduce the number of false predictions. Moreover, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasite growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association with known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. We also provided a study case focusing on a closer examination of the human–S. mansoni predicted interactome, detecting central proteins that have relevant roles in the human–S. mansoni network, and identifying tissue-specific interactions with key roles in the life cycle of the parasite. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory

Leptospirosis is the most emerging zoonotic disease of epidemic potential caused by pathogenic species of Leptospira. The bacterium invades the host system and causes the disease by interacting with the host proteins. Analyzing these pathogen-host protein interactions (PHPIs) may provide deeper insight into the disease pathogenesis. For this analysis, inter-species as well as intra-species protein interactions networks of Leptospira interrogans and human were constructed and investigated. The topological analyses of these networks showed lesser connectivity in inter-species network than intra-species, indicating the perturbed nature of the inter-species network. Hence, it can be one of the reasons behind the disease development. A total of 35 out of 586 PHPIs were identified as key interactions based on their sub-cellular localization. Two outer membrane proteins (GpsA and MetXA) and two periplasmic proteins (Flab and GlyA) participating in PHPIs were found conserved in all pathogenic, intermediate and saprophytic spp. of Leptospira. Furthermore, the bacterial membrane proteins involved in PHPIs were found playing major roles in disruption of the immune systems and metabolic processes within host and thereby causing infectious disease. Thus, the present results signify that the membrane proteins participating in such interactions hold potential to serve as effective immunotherapeutic candidates for vaccine development.

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.

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 host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Integrative analysis of differentially expressed microRNAs of pulmonary alveolar macrophages from piglets during H1N1 swine influenza A virus infection

H1N1 swine influenza A virus (H1N1 SwIV) is one key subtype of influenza viruses with pandemic potential. MicroRNAs (miRNAs) are endogenous small RNA molecules that regulate gene expression. MiRNAs relevant with H1N1 SwIV have rarely been reported. To understand the biological functions of miRNAs during H1N1 SwIV infection, this study profiled differentially expressed (DE) miRNAs in pulmonary alveolar macrophages from piglets during the H1N1 SwIV infection using a deep sequencing approach, which was validated by quantitative real-time PCR. Compared to control group, 70 and 16 DE miRNAs were respectively identified on post-infection day (PID) 4 and PID 7. 56 DE miRNAs were identified between PID 4 and PID 7. Our results suggest that most host miRNAs are down-regulated to defend the H1N1 SwIV infection during the acute phase of swine influenza whereas their expression levels gradually return to normal during the recovery phase to avoid the occurrence of too severe porcine lung damage. In addition, targets of DE miRNAs were also obtained, for which bioinformatics analyses were performed. Our results would be useful for investigating the functions and regulatory mechanisms of miRNAs in human influenza because pig serves as an excellent animal model to study the pathogenesis of human influenza.

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.

in silico identification of protein-protein interactions in Silkworm, Bombyx mori

The Domesticated silkworm, Bombyx mori, an economically important insect has been used as a lepidopteran molecular model next only to Drosophila. Compared to the genomic information in silkworm, the protein-protein interaction data are limited. Therefore experimentally identified PPI maps from five model organisms such as E.coli, C.elegans, D.melanogaster, H. sapiens, S. cerevisiae were used to infer the PPI network of silkworm using the well-recognized Interlog based method. Among the 14623 silkworm proteins, 7736 protein-protein interaction pairs were predicted which include 2700 unique proteins of the silkworms. Using the iPfam interaction domains and the gene expression data, these predictions were validated. In that 625 PPI pairs of predicted network were associated with the iPfam domain-domain interactions and the random network has average of 9. In the gene expression method, the average PCC value of the predicted network and random network was 0.29 and 0.23100±0.00042 respectively. It reveals that the predicted PPI networks of silkworm are highly significant and reliable. This is the first PPI network for the silkworm which will provide a framework for deciphering the cellular processes governing key metabolic pathways in the silkworm, Bombyx mori and available at SilkPPI (http://210.212.197.30/SilkPPI/).

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference

Protein–protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.