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Identification of RNA-protein interaction networks involved in the norovirus life cycle.

ABSTRACT: Human noroviruses are one of the major causes of acute gastroenteritis in the developed world, yet our understanding of their molecular mechanisms of genome translation and replication lags behind that for many RNA viruses. Due to the nonculturable nature of human noroviruses, many related members of the Caliciviridae family of small RNA viruses are often used as model systems to dissect the finer details of the norovirus life cycle. Murine norovirus (MNV) has provided one such system with which to study the basic mechanisms of norovirus translation and replication in cell culture. In this report we describe the use of riboproteomics to identify host factors that interact with the extremities of the MNV genome. This network of RNA-protein interactions contains many well-characterized host factors, including PTB, La, and DDX3, which have been shown to play a role in the life cycle of other RNA viruses. By using RNA coimmunoprecipitation, we confirmed that a number of the factors identified using riboproteomics are associated with the viral RNA during virus replication in cell culture. We further demonstrated that RNA inhibition-mediated knockdown of the intracellular levels of a number of these factors inhibits or slows norovirus replication in cell culture, allowing identification of new intracellular targets for this important group of pathogens.

SUBMITTER: Vashist S

PROVIDER: S-EPMC3486512 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Identification of RNA-protein interaction networks involved in the norovirus life cycle.

Vashist Surender S Urena Luis L Chaudhry Yasmin Y Goodfellow Ian I

Journal of virology 20120829 22

Human noroviruses are one of the major causes of acute gastroenteritis in the developed world, yet our understanding of their molecular mechanisms of genome translation and replication lags behind that for many RNA viruses. Due to the nonculturable nature of human noroviruses, many related members of the Caliciviridae family of small RNA viruses are often used as model systems to dissect the finer details of the norovirus life cycle. Murine norovirus (MNV) has provided one such system with which ...[more]

PMID: 22933270

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Project description:MotivationIdentifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of 'higher than expected connectivity', those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks.ResultsIn this article, we propose a novel optimization formulation LCP(2) (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP(2) by random walk. A spectral approximate algorithm SLCP(2) is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algorithm (greedy algorithm for LCP(2)) GLCP(2) to identify overlapping functional modules. We compare SLCP(2) and GLCP(2) with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP(2) outperform all other compared algorithms.Availability and implementationAll data and code are available at http://www.cse.usf.edu/~xqian/fmi/slcp2hop/.

Project description:Norovirus (NoV) infections are a significant health burden to society, yet the lack of reliable tissue culture systems has hampered the development of appropriate antiviral therapies. Here we show that the NoV NS3 protein, derived from murine NoV (MNV), is intimately associated with the MNV replication complex and the viral replication intermediate double-stranded RNA (dsRNA). We observed that when expressed individually, MNV NS3 and NS3 encoded by human Norwalk virus (NV) induced the formation of distinct vesicle-like structures that did not colocalize with any particular protein markers to cellular organelles but localized to cellular membranes, in particular those with a high cholesterol content. Both proteins also showed some degree of colocalization with the cytoskeleton marker β-tubulin. Although the distribution of MNV and NV NS3s were similar, NV NS3 displayed a higher level of colocalization with the Golgi apparatus and the endoplasmic reticulum (ER). However, we observed that although both proteins colocalized in membranes counterstained with filipin, an indicator of cholesterol content, MNV NS3 displayed a greater association with flotillin and stomatin, proteins known to associate with sphingolipid- and cholesterol-rich microdomains. Utilizing time-lapse epifluorescence microscopy, we observed that the membrane-derived vesicular structures induced by MNV NS3 were highly motile and dynamic in nature, and their movement was dependent on intact microtubules. These results begin to interrogate the functions of NoV proteins during virus replication and highlight the conserved properties of the NoV NS3 proteins among the seven Norovirus genogroups. Many mechanisms involved in the replication of norovirus still remain unclear, including the role for the NS3 protein, one of seven nonstructural viral proteins, which remains to be elucidated. This study reveals that murine norovirus (MNV) NS3 is intimately associated with the viral replication complex and dsRNA. We observed that the NS3 proteins of both MNV and Norwalk virus (NV) induce prominent vesicular structures and that this formation is dependent on microtubules and cellular cholesterol. Thus, this study contributes to our understanding of protein function within different Norovirus genogroups and expands a growing knowledge base on the interaction between positive-strand RNA [(+)RNA] viruses and cellular membranes that contribute to the biogenesis of virus-induced membrane organelles. This study contributes to our understanding of viral protein function and the ability of a viral protein to recruit specific cellular organelles and lipids that enable replication.

Project description:MotivationProtein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes.ResultsIn this study, we propose a new algorithm, NEOComplex ( NE CC- and O rtholog-based Complex identification by multiple network alignment), which integrates functional orthology information that can be obtained from different types of multiple network alignment (MNA) approaches to expand the search space of protein complex detection. As part of our approach, we also define a new edge clustering coefficient (NECC) to assign weights to interaction edges in PPI networks so that protein complexes can be identified more accurately. The NECC is based on the intuition that there is functional information captured in the common neighbors of the common neighbors as well. Our results show that our algorithm outperforms well-known protein complex identification tools in a balance between precision and recall on three eukaryotic species: human, yeast, and fly. As a result of MNAs of the species, the proposed approach can tolerate edge loss in PPI networks and even discover sparse protein complexes which have traditionally been a challenge to predict.Availability and implementationhttp://acolab.ie.nthu.edu.tw/bionetwork/NEOComplex.Contactbab@csail.mit.edu or csliao@ie.nthu.edu.tw.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Dataset Information

Identification of RNA-protein interaction networks involved in the norovirus life cycle.

Publications

Identification of RNA-protein interaction networks involved in the norovirus life cycle.

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