Identification Of Protein Interaction Networks Research Articles

Proximity Dependent Biotin Labelling in Zebrafish for Proteome and Interactome Profiling.

Identification of protein interaction networks is key for understanding intricate biological processes, but mapping such networks is challenging with conventional biochemical methods, especially for weak or transient interactions. Proximity-dependent biotin labelling (BioID) using promiscuous biotin ligases and mass spectrometry (MS)-based proteomics has emerged in the past decade as a powerful method for probing local proteomes and protein interactors. Here, we describe the application of an engineered biotin ligase, TurboID, for proteomic mapping and interactor screening in vivo in zebrafish. We generated novel transgenic zebrafish lines that express TurboID fused to a conditionally stabilised GFP-binding nanobody, dGBP, which targets TurboID to the GFP-tagged proteins of interest. The TurboID-dGBP zebrafish lines enable proximity-dependent biotin labelling in live zebrafish simply through outcrossing with existing GFP-tagged lines. Here, we outline a detailed protocol of the BLITZ method (Biotin Labelling In Tagged Zebrafish) for utilising TurboID-dGBP fish lines to map local proteomes and screen novel interactors. Graphic abstract: Schematic overview of the BLITZ method. TurboID-dGBP fish are crossed with GFP-tagged lines to obtain embryos co-expressing TurboID-dGBP (indicated by mKate2) and the GFP-POI (protein of interest). Embryos expressing only TurboID are used as a negative control. Embryos (2 to 7 dpf) are incubated overnight with a 500 μM biotin-supplemented embryo medium. This biotin incubation step allows TurboID to catalyse proximity-dependent biotinylation in live zebrafish embryos. After biotin incubation, embryos are solubilised in lysis buffer, and free biotin is removed using a PD-10 desalting column. The biotinylated proteins are captured by streptavidin affinity purification, and captured proteins are analysed by MS sequencing.

Label-free based quantitative proteomics analysis of primary neonatal porcine Leydig cells exposed to the persistent contaminant 3-methylsulfonyl-DDE

Evidence that persistent environmental pollutants may target the male reproductive system is increasing. The male reproductive system is regulated by secretion of testosterone by testicular Leydig cells, and perturbation of Leydig cell function may have ultimate consequences. 3-Methylsulfonyl-DDE (3-MeSO2-DDE) is a potent adrenal toxicants formed from the persistent insecticide DDT. Although studies have revealed the endocrine disruptive effect of 3-MeSO2-DDE, the underlying mechanisms at cellular level in steroidogenic Leydig cells remains to be established. The current study addresses the effect of 3-MeSO2-DDE on viability, hormone production and proteome response of primary neonatal porcine Leydig cells. The AlamarBlue™ assay was used to evaluate cell viability. Solid phase radioimmunoassay was used to measure concentration of hormones produced by both unstimulated and Luteinizing hormone (LH)-stimulated Leydig cells following 48h exposure. Protein samples from Leydig cells exposed to a non-cytotoxic concentration of 3-MeSO2-DDE (10μM) were subjected to nano-LC–MS/MS and analyzed on a Q Exactive mass spectrometer and quantified using label-free quantitative algorithm. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) were carried out for functional annotation and identification of protein interaction networks. 3-MeSO2-DDE regulated Leydig cell steroidogenesis differentially depending on cell culture condition. Whereas its effect on testosterone secretion at basal condition was stimulatory, the effect on LH-stimulated cells was inhibitory. From triplicate experiments, a total of 6804 proteins were identified in which the abundance of 86 proteins in unstimulated Leydig cells and 145 proteins in LH-stimulated Leydig cells was found to be significantly regulated in response to 3-MeSO2-DDE exposure. These proteins not only are the first reported in relation to 3-MeSO2-DDE exposure, but also display small number of proteins shared between culture conditions, suggesting the action of 3-MeSO2-DDE on several targeted pathways, including mitochondrial dysfunction, oxidative phosphorylation, EIF2-signaling, and glutathione-mediated detoxification. Further identification and characterization of these proteins and pathways may build our understanding to the molecular basis of 3-MeSO2-DDE induced endocrine disruption in Leydig cells.

Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.

BackgroundIdentification of protein interaction network is a very important step for understanding the molecular mechanisms in cancer. Several methods have been developed to integrate protein-protein interaction (PPI) data with gene expression data for network identification. However, they often fail to model the dependency between genes in the network, which makes many important genes, especially the upstream genes, unidentified. It is necessary to develop a method to improve the network identification performance by incorporating the dependency between genes.ResultsWe proposed an approach for identifying protein interaction network by incorporating mutual information (MI) into a Markov random field (MRF) based framework to model the dependency between genes. MI is widely used in information theory to measure the uncertainty between random variables. Different from traditional Pearson correlation test, MI is capable of capturing both linear and non-linear relationship between random variables. Among all the existing MI estimators, we choose to use k-nearest neighbor MI (kNN-MI) estimator which is proved to have minimum bias. The estimated MI is integrated with an MRF framework to model the gene dependency in the context of network. The maximum a posterior (MAP) estimation is applied on the MRF-based model to estimate the network score. In order to reduce the computational complexity of finding the optimal network, a probabilistic searching algorithm is implemented. We further increase the robustness and reproducibility of the results by applying a non-parametric bootstrapping method to measure the confidence level of the identified genes. To evaluate the performance of the proposed method, we test the method on simulation data under different conditions. The experimental results show an improved accuracy in terms of subnetwork identification compared to existing methods. Furthermore, we applied our method onto real breast cancer patient data; the identified protein interaction network shows a close association with the recurrence of breast cancer, which is supported by functional annotation. We also show that the identified subnetworks can be used to predict the recurrence status of cancer patients by survival analysis.ConclusionsWe have developed an integrated approach for protein interaction network identification, which combines Markov random field framework and mutual information to model the gene dependency in PPI network. Improvements in subnetwork identification have been demonstrated with simulation datasets compared to existing methods. We then apply our method onto breast cancer patient data to identify recurrence related subnetworks. The experiment results show that the identified genes are enriched in the pathway and functional categories relevant to progression and recurrence of breast cancer. Finally, the survival analysis based on identified subnetworks achieves a good result of classifying the recurrence status of cancer patients.

Using SILAC proteomics to investigate the effect of the mycotoxin, alternariol, in the human H295R steroidogenesis model.

The mycotoxin alternariol (AOH) is an important contaminant of fruits and cereal products. The current study sought to address the effect of a non-toxic AOH concentration on the proteome of the steroidogenic H295R cell model. Quantitative proteomics based on stable isotope labeling by amino acids in cell culture (SILAC) coupled to 1D-SDS-PAGE-LC-MS/MS was applied to subcellular-enriched protein samples. Gene ontology (GO) and ingenuity pathway analysis (IPA) were further carried out for functional annotation and identification of protein interaction networks. Furthermore, the effect of AOH on apoptosis and cell cycle distribution was also determined by the use of flow cytometry analysis. This work identified 22 proteins that were regulated significantly. The regulated proteins are those involved in early stages of steroid biosynthesis (SOAT1, NPC1, and ACBD5) and C21-steroid hormone metabolism (CYP21A2 and HSD3B1). In addition, several proteins known to play a role in cellular assembly, organization, protein synthesis, and cell cycle were regulated. These findings provide a new framework for studying the mechanisms by which AOH modulates steroidogenesis in H295R cell model.

Proteomic study reveals a functional network of cancer markers in the G1-Stage of the breast cancer cell cycle.

BackgroundCancer cells are characterized by a deregulated cell cycle that facilitates abnormal proliferation by allowing cells to by-pass tightly regulated molecular checkpoints such as the G1/S restriction point. To facilitate early diagnosis and the identification of new drug targets, current research efforts focus on studies that could lead to the development of protein panels that collectively can improve the effectiveness of our response to the detection of a life-threatening disease.MethodsEstrogen-responsive MCF-7 cells were cultured and arrested by serum deprivation in the G1-stage of the cell cycle, and fractionated into nuclear and cytoplasmic fractions. The protein extracts were trypsinized and analyzed by liquid chromatography - mass spectrometry (MS), and the data were interpreted with the Thermo Electron Bioworks software. Biological characterization of the data, selection of cancer markers, and identification of protein interaction networks was accomplished with a combination of bioinformatics tools provided by GoMiner, DAVID and STRING.ResultsThe objective of this work was to explore via MS proteomic profiling technologies and bioinformatics data mining whether randomly identified cancer markers can be associated with the G1-stage of the cell cycle, i.e., the stage in which cancer cells differ most from normal cells, and whether any functional networks can be identified between these markers and placed in the broader context of cell regulatory pathways. The study enabled the identification of over 2000 proteins and 153 cancer markers, and revealed for the first time that the G1-stage of the cell cycle is not only a rich source of cancer markers, but also a host to an intricate network of functional relationships within the majority of these markers. Three major clusters of interacting proteins emerged: (a) signaling, (b) DNA repair, and (c) oxidative phosphorylation.ConclusionsThe identification of cancer marker regulatory components that act not alone, but within networks, represents an invaluable resource for elucidating the moxlecular mechanisms that govern the uncontrolled proliferation of cancer cells, as well as for catalyzing the development of protein panels with biomarker and drug target potential, screening tests with improved sensitivity and specificity, and novel cancer therapies aimed at pursuing multiple drug targets.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2407-14-710) contains supplementary material, which is available to authorized users.

Computational analysis of Concanavalin A binding glycoproteins of human seminal plasma.

Glycoproteins have immense clinical importance and comparative glycoproteomics has become a powerful tool for biomarker discovery and disease diagnosis. Seminal plasma glycoproteins participate in fertility related processes including sperm-egg recognition, modulation of capacitation and acrosome reaction inhibition. Affinity chromatography using broad specificity lectin such as Con A is widely applied for glycoproteins enrichment. More notably, Con A-interacting fraction of human seminal plasma has decapacitating activity which makes this fraction critically important. In our previous study, we isolated Con A-interacting glycoproteins from human seminal plasma and subsequently identified them by mass spectrometry. Here, we report the computational analysis of these proteins using bioinformatics tools. The analysis includes: prediction of glycosylation sites using sequence information (NetNGlyc 1.0), functional annotations to cluster these proteins into various functional groups (InterProScan and Blast2GO) and identification of protein interaction networks (STRING database). The results indicate that these proteins are involved in various biological processes including transport, morphogenesis, metabolic processes, cell differentiation and homeostasis. The clusters illustrate two major molecular functions - hydrolase activity (6) and protein (4)/carbohydrate (1)/lipid binding (1). The large interactomes of proteins point towards their versatile roles in wide range of biological processes.

Hubs and bottlenecks in plant molecular signalling networks

Conditional control of plant cell function and development relies on appropriate signal perception, signal integration and processing. The development of high throughput technologies such as proteomics and interactomics has enabled the identification of protein interaction networks that mediate signal processing from inputs to appropriate outputs. Such networks can be depicted in graphical representations using nodes and edges allowing for the immediate visualization and analysis of the network's topology. Hubs are network elements characterized by many edges (often degree grade k ≥ 5) which confer a degree of topological importance to them. The review introduces the concept of networks, hubs and bottlenecks and describes four examples from plant science in more detail, namely hubs in the redox regulatory network of the chloroplast with ferredoxin, thioredoxin and peroxiredoxin, in mitogen activated protein (MAP) kinase signal processing, in photomorphogenesis with the COP9 signalosome, COP1 and CDD, and monomeric GTPase function. Some guidance is provided to appropriate internet resources, web repositories, databases and their use. Plant networks can be generated from existing public databases and this type of analysis is valuable in support of existing hypotheses, or to allow for the generation of new concepts or ideas. However, intensive manual curating of in silico networks is still always necessary.

Interaction of Zonula Occludens-1 (ZO-1) with α-Actinin-4: Application of Functional Proteomics for Identification of PDZ Domain-Associated Proteins

The use of recombinant "bait" proteins to capture protein-binding partners, followed by identification of protein interaction networks by mass spectrometry (MS), has gained popularity and widespread acceptance. We have developed an approach using recombinant PDZ protein interaction modules of the membrane-associated guanylate kinase (MAGUK) protein zonula occludens-1 (ZO-1) to pull-down and screen for proteins that interact with these modules via their PDZ domain binding motifs. Identification of proteins by MS of pull-down material was achieved using a vacuum-based chromatography sample preparation device designed for matrix-assisted laser desorption/ionization (MALDI) MS. MS analysis of tryptic fragments in pull-down material revealed a number of potential ZO-1 interacting candidates, including the presence of peptides corresponding to the cortical membrane scaffolding protein alpha-actinin-4. Interaction of alpha-actinin-4 with ZO-1 was confirmed by coimmunoprecipitation of these two proteins from cultured cells, as well as from brain, liver, and heart, and by immunoblot detection of alpha-actinin-4 after pull-down with the first PDZ domain of ZO-1. In contrast, the highly homologous alpha-actinin family member, alpha-actinin-1, displayed no association with ZO-1. Immunofluorescence showed colocalization of alpha-actinin-4 with ZO-1 in cultured HeLa and C6 glioma cells, as well as in a variety of tissues in vivo, including brain, heart, liver, and lung. This study demonstrates the utility of MS-based functional proteomics for identifying cellular components of the ZO-1 scaffolding network. Our finding of the interaction of ZO-1 with alpha-actinin-4 provides a mechanism for linking the known protein recruitment and signaling activities of ZO-1 with alpha-actinin-4-associated plasma membrane proteins that have regulatory activities at cell-cell and cell-extracellular matrix contacts.