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Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients. PLoS One 2014; 9:e86879. [PMID: 24466278 PMCID: PMC3900678 DOI: 10.1371/journal.pone.0086879] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2013] [Accepted: 12/18/2013] [Indexed: 12/14/2022] Open
Abstract
One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods.
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102
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Abstract
As more and more systems biology approaches are used to investigate the different types of biological macromolecules, increasing numbers of whole genomic studies are now available for a large array of organisms. Whether it is genomics, transcriptomics, proteomics, interactomics or metabolomics, the full complement of genomic information on all different levels can be juxtaposed between different organisms to reveal similarities or differences, and even to provide consensus models. At the intersection of comparative genomics and systems biology lies great possibility for discovery, analysis and prediction. This paper explores this nexus and the relationship from four general levels: DNA, RNA, protein and extragenomic. For each level, we provide an overview of the methods, discuss the potential challenges and survey the current research. Finally, we suggest some organizing principles and make proposals for new areas that will be important for future research.
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Affiliation(s)
- Jimmy Lin
- Wilmer Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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103
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Yu D, Kim M, Xiao G, Hwang TH. Review of biological network data and its applications. Genomics Inform 2013; 11:200-10. [PMID: 24465231 PMCID: PMC3897847 DOI: 10.5808/gi.2013.11.4.200] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Revised: 11/20/2013] [Accepted: 11/21/2013] [Indexed: 12/16/2022] Open
Abstract
Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.
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Affiliation(s)
- Donghyeon Yu
- Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Minsoo Kim
- Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Guanghua Xiao
- Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tae Hyun Hwang
- Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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104
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Chen Y, Wu X, Jiang R. Integrating human omics data to prioritize candidate genes. BMC Med Genomics 2013; 6:57. [PMID: 24344781 PMCID: PMC3878333 DOI: 10.1186/1755-8794-6-57] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Accepted: 12/12/2013] [Indexed: 01/07/2023] Open
Abstract
Background The identification of genes involved in human complex diseases remains a great challenge in computational systems biology. Although methods have been developed to use disease phenotypic similarities with a protein-protein interaction network for the prioritization of candidate genes, other valuable omics data sources have been largely overlooked in these methods. Methods With this understanding, we proposed a method called BRIDGE to prioritize candidate genes by integrating disease phenotypic similarities with such omics data as protein-protein interactions, gene sequence similarities, gene expression patterns, gene ontology annotations, and gene pathway memberships. BRIDGE utilizes a multiple regression model with lasso penalty to automatically weight different data sources and is capable of discovering genes associated with diseases whose genetic bases are completely unknown. Results We conducted large-scale cross-validation experiments and demonstrated that more than 60% known disease genes can be ranked top one by BRIDGE in simulated linkage intervals, suggesting the superior performance of this method. We further performed two comprehensive case studies by applying BRIDGE to predict novel genes and transcriptional networks involved in obesity and type II diabetes. Conclusion The proposed method provides an effective and scalable way for integrating multi omics data to infer disease genes. Further applications of BRIDGE will be benefit to providing novel disease genes and underlying mechanisms of human diseases.
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Affiliation(s)
| | | | - Rui Jiang
- Department of Automation, MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Tsinghua University, Beijing 100084, China.
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105
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Behar M, Barken D, Werner SL, Hoffmann A. The dynamics of signaling as a pharmacological target. Cell 2013; 155:448-61. [PMID: 24120141 DOI: 10.1016/j.cell.2013.09.018] [Citation(s) in RCA: 94] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2012] [Revised: 06/17/2013] [Accepted: 09/09/2013] [Indexed: 02/06/2023]
Abstract
Highly networked signaling hubs are often associated with disease, but targeting them pharmacologically has largely been unsuccessful in the clinic because of their functional pleiotropy. Motivated by the hypothesis that a dynamic signaling code confers functional specificity, we investigated whether dynamic features may be targeted pharmacologically to achieve therapeutic specificity. With a virtual screen, we identified combinations of signaling hub topologies and dynamic signal profiles that are amenable to selective inhibition. Mathematical analysis revealed principles that may guide stimulus-specific inhibition of signaling hubs, even in the absence of detailed mathematical models. Using the NFκB signaling module as a test bed, we identified perturbations that selectively affect the response to cytokines or pathogen components. Together, our results demonstrate that the dynamics of signaling may serve as a pharmacological target, and we reveal principles that delineate the opportunities and constraints of developing stimulus-specific therapeutic agents aimed at pleiotropic signaling hubs.
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Affiliation(s)
- Marcelo Behar
- Signaling Systems Laboratory, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093, USA; San Diego Center for Systems Biology, University of California, San Diego, La Jolla, CA 92093, USA; Computational Biosciences Institute, University of California, Los Angeles, Los Angeles, CA 90025, USA; Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90025, USA
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106
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Anchoori RK, Karanam B, Peng S, Wang JW, Jiang R, Tanno T, Orlowski RZ, Matsui W, Zhao M, Rudek MA, Hung CF, Chen X, Walters KJ, Roden RBS. A bis-benzylidine piperidone targeting proteasome ubiquitin receptor RPN13/ADRM1 as a therapy for cancer. Cancer Cell 2013; 24:791-805. [PMID: 24332045 PMCID: PMC3881268 DOI: 10.1016/j.ccr.2013.11.001] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 05/21/2013] [Accepted: 11/04/2013] [Indexed: 11/16/2022]
Abstract
The bis-benzylidine piperidone RA190 covalently binds to cysteine 88 of ubiquitin receptor RPN13 in the 19S regulatory particle and inhibits proteasome function, triggering rapid accumulation of polyubiquitinated proteins. Multiple myeloma (MM) lines, even those resistant to bortezomib, were sensitive to RA190 via endoplasmic reticulum stress-related apoptosis. RA190 stabilized targets of human papillomavirus (HPV) E6 oncoprotein, and preferentially killed HPV-transformed cells. After oral or intraperitoneal dosing of mice, RA190 distributed to plasma and major organs except the brain and inhibited proteasome function in skin and muscle. RA190 administration profoundly reduced growth of MM and ovarian cancer xenografts, and oral RA190 treatment retarded HPV16(+) syngeneic mouse tumor growth, without affecting spontaneous HPV-specific CD8(+) T cell responses, suggesting its therapeutic potential.
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Affiliation(s)
- Ravi K Anchoori
- Department of Oncology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | | | - Shiwen Peng
- Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Joshua W Wang
- Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Rosie Jiang
- Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Toshihiko Tanno
- Department of Oncology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Robert Z Orlowski
- Division of Cancer Medicine, Department of Lymphoma/Myeloma, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - William Matsui
- Department of Oncology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Ming Zhao
- Department of Oncology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michelle A Rudek
- Department of Oncology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Chien-fu Hung
- Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231, USA
| | - Xiang Chen
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; Protein Processing Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Kylie J Walters
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA; Protein Processing Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Richard B S Roden
- Department of Oncology, The Johns Hopkins University, Baltimore, MD 21231, USA; Department of Pathology, The Johns Hopkins University, Baltimore, MD 21231, USA; Department of Gynecology and Obstetrics, The Johns Hopkins University, Baltimore, MD 21231, USA.
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107
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Integrating GWASs and human protein interaction networks identifies a gene subnetwork underlying alcohol dependence. Am J Hum Genet 2013; 93:1027-34. [PMID: 24268660 DOI: 10.1016/j.ajhg.2013.10.021] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 10/14/2013] [Accepted: 10/21/2013] [Indexed: 12/31/2022] Open
Abstract
Despite a significant genetic contribution to alcohol dependence (AD), few AD-risk genes have been identified to date. In the current study, we aimed to integrate genome-wide association studies (GWASs) and human protein interaction networks to investigate whether a subnetwork of genes whose protein products interact with one another might collectively contribute to AD. By using two discovery GWAS data sets of the Study of Addiction: Genetics and Environment (SAGE) and the Collaborative Study on the Genetics of Alcoholism (COGA), we identified a subnetwork of 39 genes that not only was enriched for genes associated with AD, but also collectively associated with AD in both European Americans (p < 0.0001) and African Americans (p = 0.0008). We replicated the association of the gene subnetwork with AD in three independent samples, including two samples of European descent (p = 0.001 and p = 0.006) and one sample of African descent (p = 0.0069). To evaluate whether the significant associations are likely to be false-positive findings and to ascertain their specificity, we examined the same gene subnetwork in three other human complex disorders (bipolar disorder, major depressive disorder, and type 2 diabetes) and found no significant associations. Functional enrichment analysis revealed that the gene subnetwork was enriched for genes involved in cation transport, synaptic transmission, and transmission of nerve impulses, all of which are biologically meaningful processes that may underlie the risk for AD. In conclusion, we identified a gene subnetwork underlying AD that is biologically meaningful and highly reproducible, providing important clues for future research into AD etiology and treatment.
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108
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Simple topological features reflect dynamics and modularity in protein interaction networks. PLoS Comput Biol 2013; 9:e1003243. [PMID: 24130468 PMCID: PMC3794914 DOI: 10.1371/journal.pcbi.1003243] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 08/14/2013] [Indexed: 11/30/2022] Open
Abstract
The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes. A better understanding of protein interaction networks would be a great aid in furthering our knowledge of the molecular biology of the cell. Towards this end, large-scale protein-protein physical interaction data have been determined for organisms across the evolutionary spectrum. However, the resulting networks give a static view of interactomes, and our knowledge about protein interactions is rarely time or context specific. A previous prominent but controversial attempt to characterize the dynamic modularity of the interactome was based on integrating physical interaction data with gene activity measurements from transcript expression data. This analysis distinguished between proteins that are co-expressed with their interacting partners and those that are not, and argued that the former are intramodular and the latter are intermodular. By analyzing the interactomes of five organisms, we largely confirm the biological significance of this characterization through a variety of statistical tests and computational experiments. Surprisingly, however, we find that similar results can be obtained using just network information without additionally integrating expression data, suggesting that purely topological characteristics of interaction networks strongly reflect certain aspects of the dynamics and modularity of interactomes.
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109
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Abstract
High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of "omics" data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various "omics" data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions.
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110
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Jacunski A, Tatonetti NP. Connecting the dots: applications of network medicine in pharmacology and disease. Clin Pharmacol Ther 2013; 94:659-69. [PMID: 23995266 DOI: 10.1038/clpt.2013.168] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 08/16/2013] [Indexed: 11/09/2022]
Abstract
In 2011, >2.5 million people died from only 15 causes in the United States. Ten of these involved complex or infectious diseases for which there is insufficient knowledge or treatment, such as heart disease, influenza, and Alzheimer's disease.(1) Complex diseases have been difficult to understand due to their multifarious genetic and molecular fingerprints, while certain infectious agents have evolved to elude treatment and prophylaxis. Network medicine provides a macroscopic approach to understanding and treating such illnesses. It integrates experimental data on gene, protein, and metabolic interactions with clinical knowledge of disease and pharmacology in order to extend the understanding of diseases and their treatments. The resulting "big picture" allows for the development of computational and mathematical methods to identify novel disease pathways and predict patient drug response, among others. In this review, we discuss recent advances in network medicine.
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Affiliation(s)
- A Jacunski
- 1] Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Medical Center, New York, New York, USA [2] Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA [3] Department of Systems Biology, Columbia University Medical Center, New York, New York, USA [4] Department of Medicine, Columbia University Medical Center, New York, New York, USA
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111
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Yang M, Chen JL, Xu LW, Ji G. Navigating traditional chinese medicine network pharmacology and computational tools. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2013; 2013:731969. [PMID: 23983798 PMCID: PMC3747450 DOI: 10.1155/2013/731969] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 07/04/2013] [Indexed: 12/17/2022]
Abstract
The concept of "network target" has ushered in a new era in the field of traditional Chinese medicine (TCM). As a new research approach, network pharmacology is based on the analysis of network models and systems biology. Taking advantage of advancements in systems biology, a high degree of integration data analysis strategy and interpretable visualization provides deeper insights into the underlying mechanisms of TCM theories, including the principles of herb combination, biological foundations of herb or herbal formulae action, and molecular basis of TCM syndromes. In this study, we review several recent developments in TCM network pharmacology research and discuss their potential for bridging the gap between traditional and modern medicine. We briefly summarize the two main functional applications of TCM network models: understanding/uncovering and predicting/discovering. In particular, we focus on how TCM network pharmacology research is conducted and highlight different computational tools, such as network-based and machine learning algorithms, and sources that have been proposed and applied to the different steps involved in the research process. To make network pharmacology research commonplace, some basic network definitions and analysis methods are presented.
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Affiliation(s)
- Ming Yang
- Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai 200032, China
- Institute of Digestive Disease, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Jia-Lei Chen
- Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai 200032, China
| | - Li-Wen Xu
- Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai 200032, China
| | - Guang Ji
- Institute of Digestive Disease, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
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112
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A predicted functional gene network for the plant pathogen Phytophthora infestans as a framework for genomic biology. BMC Genomics 2013; 14:483. [PMID: 23865555 PMCID: PMC3734169 DOI: 10.1186/1471-2164-14-483] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Accepted: 07/15/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Associations between proteins are essential to understand cell biology. While this complex interplay between proteins has been studied in model organisms, it has not yet been described for the oomycete late blight pathogen Phytophthora infestans. RESULTS We present an integrative probabilistic functional gene network that provides associations for 37 percent of the predicted P. infestans proteome. Our method unifies available genomic, transcriptomic and comparative genomic data into a single comprehensive network using a Bayesian approach. Enrichment of proteins residing in the same or related subcellular localization validates the biological coherence of our predictions. The network serves as a framework to query existing genomic data using network-based methods, which thus far was not possible in Phytophthora. We used the network to study the set of interacting proteins that are encoded by genes co-expressed during sporulation. This identified potential novel roles for proteins in spore formation through their links to proteins known to be involved in this process such as the phosphatase Cdc14. CONCLUSIONS The functional association network represents a novel genome-wide data source for P. infestans that also acts as a framework to interrogate other system-wide data. In both capacities it will improve our understanding of the complex biology of P. infestans and related oomycete pathogens.
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113
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Zhang Z, Zhao Z, Liu B, Li D, Zhang D, Chen H, Liu D. Systems biomedicine: It’s your turn—Recent progress in systems biomedicine. QUANTITATIVE BIOLOGY 2013. [DOI: 10.1007/s40484-013-0009-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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114
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Abstract
UNLABELLED 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/.
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Affiliation(s)
- Robert Pesch
- Institute for Informatics, Ludwig-Maximilians-Universität München, Munich, Germany
- * E-mail:
| | - Ralf Zimmer
- Institute for Informatics, Ludwig-Maximilians-Universität München, Munich, Germany
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115
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Optimization criteria and biological process enrichment in homologous multiprotein modules. Proc Natl Acad Sci U S A 2013; 110:10872-7. [PMID: 23757502 DOI: 10.1073/pnas.1308621110] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Biological process enrichment is a widely used metric for evaluating the quality of multiprotein modules. In this study, we examine possible optimization criteria for detecting homologous multiprotein modules and quantify their effects on biological process enrichment. We find that modularity, linear density, and module size are the most important criteria considered, complementary to each other, and that graph theoretical attributes account for 36% of the variance in biological process enrichment. Variations in protein interaction similarity within module pairs have only minor effects on biological process enrichment. As random modules increase in size, both biological process enrichment and modularity tend to improve, although modularity does not show this upward trend in modules with size at most 50 proteins. To adjust for these trends, we recommend a size correction based on random sampling of modules when using biological process enrichment or other attributes to evaluate module boundaries. Characteristics of homologous multiprotein modules optimized for each of the optimization criteria are examined.
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116
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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117
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Lehne B, Schlitt T. Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes. Pharmacogenomics 2013; 13:1967-78. [PMID: 23215889 DOI: 10.2217/pgs.12.170] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation. This article provides an overview of the methods currently available and reviews the advantages and challenges of this approach. In detail, it highlights the particular issues of de novo pathway discovery based on genome-wide association studies data, for which multiple different strategies have been proposed.
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Affiliation(s)
- Benjamin Lehne
- Bioinformatics Group, Department of Medical & Molecular Genetics, 8th Floor Tower Wing Guy's Hospital, London SE1 9RT, UK
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118
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Das J, Vo TV, Wei X, Mellor JC, Tong V, Degatano AG, Wang X, Wang L, Cordero NA, Kruer-Zerhusen N, Matsuyama A, Pleiss JA, Lipkin SM, Yoshida M, Roth FP, Yu H. Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways. Sci Signal 2013; 6:ra38. [PMID: 23695164 DOI: 10.1126/scisignal.2003350] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The fission yeast Schizosaccharomyces pombe has more metazoan-like features than the budding yeast Saccharomyces cerevisiae, yet it has similarly facile genetics. We present a large-scale verified binary protein-protein interactome network, "StressNet," based on high-throughput yeast two-hybrid screens of interacting proteins classified as part of stress response and signal transduction pathways in S. pombe. We performed systematic, cross-species interactome mapping using StressNet and a protein interactome network of orthologous proteins in S. cerevisiae. With cross-species comparative network studies, we detected a previously unidentified component (Snr1) of the S. pombe mitogen-activated protein kinase Sty1 pathway. Coimmunoprecipitation experiments showed that Snr1 interacted with Sty1 and that deletion of snr1 increased the sensitivity of S. pombe cells to stress. Comparison of StressNet with the interactome network of orthologous proteins in S. cerevisiae showed that most of the interactions among these stress response and signaling proteins are not conserved between species but are "rewired"; orthologous proteins have different binding partners in both species. In particular, transient interactions connecting proteins in different functional modules were more likely to be rewired than conserved. By directly testing interactions between proteins in one yeast species and their corresponding binding partners in the other yeast species with yeast two-hybrid assays, we found that about half of the interactions that are traditionally considered "conserved" form modified interaction interfaces that may potentially accommodate novel functions.
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Affiliation(s)
- Jishnu Das
- Department of Biological Statistics and Computational Biology Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
| | - Tommy V Vo
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiaomu Wei
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA.,Department of Medicine, Weill Cornell College of Medicine, New York, NY 10021, USA
| | - Joseph C Mellor
- Donnelly Centre, University of Toronto, Toronto, ON M5S-3E1, Canada
| | - Virginia Tong
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
| | - Andrew G Degatano
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
| | - Xiujuan Wang
- Department of Biological Statistics and Computational Biology Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
| | - Lihua Wang
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
| | - Nicolas A Cordero
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
| | - Nathan Kruer-Zerhusen
- Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Akihisa Matsuyama
- Chemical Genetics Laboratory, RIKEN Advanced Science Institute, Wako, Saitama 351-0198, Japan.,CREST Research Project, JST, Kawaguchi, Saitama 332-0012, Japan
| | - Jeffrey A Pleiss
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Steven M Lipkin
- Department of Medicine, Weill Cornell College of Medicine, New York, NY 10021, USA
| | - Minoru Yoshida
- Chemical Genetics Laboratory, RIKEN Advanced Science Institute, Wako, Saitama 351-0198, Japan.,CREST Research Project, JST, Kawaguchi, Saitama 332-0012, Japan.,Department of Biotechnology, Graduate School of Agriculture and Life Sciences, University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON M5S-3E1, Canada.,Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S-3E1, Canada.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115.,Harvard Medical School, Boston, MA 02115.,Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, ON M5G-1X5, Canada.,Genetic Networks Program, Canadian Institute for Advanced Research, Toronto, ON M5G-1Z8, Canada
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology Cornell University, Ithaca, NY 14853, USA
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119
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Ehlinger A, Walters KJ. Structural insights into proteasome activation by the 19S regulatory particle. Biochemistry 2013; 52:3618-28. [PMID: 23672618 DOI: 10.1021/bi400417a] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Since its discovery in the late 1970s, the ubiquitin-proteasome system (UPS) has become recognized as the major pathway for regulated cellular proteolysis. Processes such as cell cycle control, pathogen resistance, and protein quality control rely on selective protein degradation at the proteasome for homeostatic function. Perhaps as a consequence of the importance of this pathway, and the genesis of severe diseases upon its dysregulation, protein degradation by the UPS is highly controlled from the level of substrate recognition to proteolysis. Technological advances over the past decade have created an explosion of structural and mechanistic information that has underscored the complexity of the proteasome and its upstream regulatory factors. Significant insights have come from the study of the 19S proteasome regulatory particle (RP) responsible for recognition and processing of ubiquitinated substrates destined for proteolysis. Established as a highly dynamic proteasome activator, the RP has a large number of both permanent and transient components with specialized functional roles that are critical for proteasome function. In this review, we highlight recent mechanistic developments in the study of proteasome activation by the RP and how they provide context to our current understanding of the UPS.
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Affiliation(s)
- Aaron Ehlinger
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota , Minneapolis, Minnesota 55455, United States
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120
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Penrod NM, Moore JH. Key genes for modulating information flow play a temporal role as breast tumor coexpression networks are dynamically rewired by letrozole. BMC Med Genomics 2013; 6 Suppl 2:S2. [PMID: 23819860 PMCID: PMC3654875 DOI: 10.1186/1755-8794-6-s2-s2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Background Genes do not act in isolation but instead as part of complex regulatory networks. To understand how breast tumors adapt to the presence of the drug letrozole, at the molecular level, it is necessary to consider how the expression levels of genes in these networks change relative to one another. Methods Using transcriptomic data generated from sequential tumor biopsy samples, taken at diagnosis, following 10-14 days and following 90 days of letrozole treatment, and a pairwise partial correlation statistic, we build temporal gene coexpression networks. We characterize the structure of each network and identify genes that hold prominent positions for maintaining network integrity and controlling information-flow. Results Letrozole treatment leads to extensive rewiring of the breast tumor coexpression network. Approximately 20% of gene-gene relationships are conserved over time in the presence of letrozole while 80% of relationships are condition dependent. The positions of influence within the networks are transiently held with few genes stably maintaining high centrality scores across the three time points. Conclusions Genes integral for maintaining network integrity and controlling information flow are dynamically changing as the breast tumor coexpression network adapts to perturbation by the drug letrozole.
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Affiliation(s)
- Nadia M Penrod
- Department of Pharmacology and Toxicology, Geisel School of Medicine at Dartmouth College, HB7937 One Medical Center Dr., Lebanon, NH 03766, USA
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121
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Abstract
Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.
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Affiliation(s)
- Yana Bromberg
- Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, New Jersey, USA.
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122
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Effective identification of Akt interacting proteins by two-step chemical crosslinking, co-immunoprecipitation and mass spectrometry. PLoS One 2013; 8:e61430. [PMID: 23613850 PMCID: PMC3629208 DOI: 10.1371/journal.pone.0061430] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Accepted: 03/12/2013] [Indexed: 11/19/2022] Open
Abstract
Akt is a critical protein for cell survival and known to interact with various proteins. However, Akt binding partners that modulate or regulate Akt activation have not been fully elucidated. Identification of Akt-interacting proteins has been customarily achieved by co-immunoprecipitation combined with western blot and/or MS analysis. An intrinsic problem of the method is loss of interacting proteins during procedures to remove non-specific proteins. Moreover, antibody contamination often interferes with the detection of less abundant proteins. Here, we developed a novel two-step chemical crosslinking strategy to overcome these problems which resulted in a dramatic improvement in identifying Akt interacting partners. Akt antibody was first immobilized on protein A/G beads using disuccinimidyl suberate and allowed to bind to cellular Akt along with its interacting proteins. Subsequently, dithiobis[succinimidylpropionate], a cleavable crosslinker, was introduced to produce stable complexes between Akt and binding partners prior to the SDS-PAGE and nanoLC-MS/MS analysis. This approach enabled identification of ten Akt partners from cell lysates containing as low as 1.5 mg proteins, including two new potential Akt interacting partners. None of these but one protein was detectable without crosslinking procedures. The present method provides a sensitive and effective tool to probe Akt-interacting proteins. This strategy should also prove useful for other protein interactions, particularly those involving less abundant or weakly associating partners.
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123
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Schuldiner M, Weissman JS. The contribution of systematic approaches to characterizing the proteins and functions of the endoplasmic reticulum. Cold Spring Harb Perspect Biol 2013; 5:a013284. [PMID: 23359093 DOI: 10.1101/cshperspect.a013284] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The endoplasmic reticulum (ER) is a complex organelle responsible for a range of functions including protein folding and secretion, lipid biosynthesis, and ion homeostasis. Despite its central and essential roles in eukaryotic cells during development, growth, and disease, many ER proteins are poorly characterized. Moreover, the range of biochemical reactions that occur within the ER membranes, let alone how these different activities are coordinated, is not yet defined. In recent years, focused studies on specific ER functions have been complemented by systematic approaches and innovative technologies for high-throughput analysis of the location, levels, and biological impact of given components. This article focuses on the recent progress of these efforts, largely pioneered in the budding yeast Saccharomyces cerevisiae, and also addresses how future systematic studies can be geared to uncover the "dark matter" of uncharted ER functions.
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Affiliation(s)
- Maya Schuldiner
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel 76100.
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124
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New measurement methods of network robustness and response ability via microarray data. PLoS One 2013; 8:e55230. [PMID: 23383119 PMCID: PMC3557243 DOI: 10.1371/journal.pone.0055230] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Accepted: 12/28/2012] [Indexed: 11/19/2022] Open
Abstract
"Robustness", the network ability to maintain systematic performance in the face of intrinsic perturbations, and "response ability", the network ability to respond to external stimuli or transduce them to downstream regulators, are two important complementary system characteristics that must be considered when discussing biological system performance. However, at present, these features cannot be measured directly for all network components in an experimental procedure. Therefore, we present two novel systematic measurement methods--Network Robustness Measurement (NRM) and Response Ability Measurement (RAM)--to estimate the network robustness and response ability of a gene regulatory network (GRN) or protein-protein interaction network (PPIN) based on the dynamic network model constructed by the corresponding microarray data. We demonstrate the efficiency of NRM and RAM in analyzing GRNs and PPINs, respectively, by considering aging- and cancer-related datasets. When applied to an aging-related GRN, our results indicate that such a network is more robust to intrinsic perturbations in the elderly than in the young, and is therefore less responsive to external stimuli. When applied to a PPIN of fibroblast and HeLa cells, we observe that the network of cancer cells possesses better robustness than that of normal cells. Moreover, the response ability of the PPIN calculated from the cancer cells is lower than that from healthy cells. Accordingly, we propose that generalized NRM and RAM methods represent effective tools for exploring and analyzing different systems-level dynamical properties via microarray data. Making use of such properties can facilitate prediction and application, providing useful information on clinical strategy, drug target selection, and design specifications of synthetic biology from a systems biology perspective.
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125
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Abstract
Large amounts of protein-protein interaction (PPI) data are available. The human PPI network currently contains over 56 000 interactions between 11 100 proteins. It has been demonstrated that the structure of this network is not random and that the same wiring patterns in it underlie the same biological processes and diseases. In this paper, we ask if there exists a subnetwork of the human PPI network such that its topology is the key to disease formation and hence should be the primary object of therapeutic intervention. We demonstrate that such a subnetwork exists and can be obtained purely computationally. In particular, by successively pruning the entire human PPI network, we are left with a "core" subnetwork that is not only topologically and functionally homogeneous, but is also enriched in disease genes, drug targets, and it contains genes that are known to drive disease formation. We call this subnetwork the Core Diseasome. Furthermore, we show that the topology of the Core Diseasome is unique in the human PPI network suggesting that it may be the wiring of this network that governs the mutagenesis that leads to disease. Explaining the mechanisms behind this phenomenon and exploiting them remains a challenge.
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Affiliation(s)
- Vuk Janjić
- Department of Computing, Imperial College London, London, SW7 2AZ, UK.
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126
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Pálfy M, Farkas IJ, Vellai T, Korcsmáros T. Uniform curation protocol of metazoan signaling pathways to predict novel signaling components. Methods Mol Biol 2013; 1021:285-297. [PMID: 23715991 DOI: 10.1007/978-1-62703-450-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
A relatively large number of signaling databases available today have strongly contributed to our understanding of signaling pathway properties. However, pathway comparisons both within and across databases are currently severely hampered by the large variety of data sources and the different levels of detail of their information content (on proteins and interactions). In this chapter, we present a protocol for a uniform curation method of signaling pathways, which intends to overcome this insufficiency. This uniformly curated database called SignaLink ( http://signalink.org ) allows us to systematically transfer pathway annotations between different species, based on orthology, and thereby to predict novel signaling pathway components. Thus, this method enables the compilation of a comprehensive signaling map of a given species and identification of new potential drug targets in humans. We strongly believe that the strict curation protocol we have established to compile a signaling pathway database can also be applied for the compilation of other (e.g., metabolic) databases. Similarly, the detailed guide to the orthology-based prediction of novel signaling components across species may also be utilized for predicting components of other biological processes.
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Affiliation(s)
- Máté Pálfy
- Department of Genetics, Eötvös Loránd University, Budapest, Hungary
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127
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Durmuş Tekir SD, Ülgen KÖ. Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era. Biotechnol J 2013; 8:85-96. [PMID: 23193100 PMCID: PMC7161785 DOI: 10.1002/biot.201200110] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Revised: 09/17/2012] [Accepted: 10/11/2012] [Indexed: 12/13/2022]
Abstract
Infectious diseases comprise some of the leading causes of death and disability worldwide. Interactions between pathogen and host proteins underlie the process of infection. Improved understanding of pathogen-host molecular interactions will increase our knowledge of the mechanisms involved in infection, and allow novel therapeutic solutions to be devised. Complete genome sequences for a number of pathogenic microorganisms, as well as the human host, has led to the revelation of their protein-protein interaction (PPI) networks. In this post-genomic era, pathogen-host interactions (PHIs) operating during infection can also be mapped. Detailed systematic analyses of PPI and PHI data together are required for a complete understanding of pathogenesis of infections. Here we review the striking results recently obtained during the construction and investigation of these networks. Emphasis is placed on studies producing large-scale interaction data by high-throughput experimental techniques.
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Affiliation(s)
| | - Kutlu Ö. Ülgen
- Department of Chemical Engineering, Boǧaziçi University, Istanbul, Turkey
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128
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Abstract
Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.
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Affiliation(s)
- Mileidy W. Gonzalez
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Maricel G. Kann
- Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, United States of America
- * E-mail:
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129
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Kim J, Kim I, Han SK, Bowie JU, Kim S. Network rewiring is an important mechanism of gene essentiality change. Sci Rep 2012. [PMID: 23198090 PMCID: PMC3509348 DOI: 10.1038/srep00900] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Gene essentiality changes are crucial for organismal evolution. However, it is unclear how essentiality of orthologs varies across species. We investigated the underlying mechanism of gene essentiality changes between yeast and mouse based on the framework of network evolution and comparative genomic analysis. We found that yeast nonessential genes become essential in mouse when their network connections rapidly increase through engagement in protein complexes. The increased interactions allowed the previously nonessential genes to become members of vital pathways. By accounting for changes in gene essentiality, we firmly reestablished the centrality-lethality rule, which proposed the relationship of essential genes and network hubs. Furthermore, we discovered that the number of connections associated with essential and non-essential genes depends on whether they were essential in ancestral species. Our study describes for the first time how network evolution occurs to change gene essentiality.
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Affiliation(s)
- Jinho Kim
- Division of Molecular and Life Science, Pohang University of Science and Technology, Pohang 790-784, Korea
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130
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Hennig J, de Vries SJ, Hennig KD, Randles L, Walters KJ, Sunnerhagen M, Bonvin AMJJ. MTMDAT-HADDOCK: high-throughput, protein complex structure modeling based on limited proteolysis and mass spectrometry. BMC STRUCTURAL BIOLOGY 2012; 12:29. [PMID: 23153250 PMCID: PMC3557227 DOI: 10.1186/1472-6807-12-29] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Accepted: 11/06/2012] [Indexed: 01/27/2023]
Abstract
BACKGROUND MTMDAT is a program designed to facilitate analysis of mass spectrometry data of proteins and biomolecular complexes that are probed structurally by limited proteolysis. This approach can provide information about stable fragments of multidomain proteins, yield tertiary and quaternary structure data, and help determine the origin of stability changes at the amino acid residue level. Here, we introduce a pipeline between MTMDAT and HADDOCK, that facilitates protein-protein complex structure probing in a high-throughput and highly automated fashion. RESULTS A new feature of MTMDAT allows for the direct identification of residues that are involved in complex formation by comparing the mass spectra of bound and unbound proteins after proteolysis. If 3D structures of the unbound components are available, this data can be used to define restraints for data-driven docking to calculate a model of the complex. We describe here a new implementation of MTMDAT, which includes a pipeline to the data-driven docking program HADDOCK, thus streamlining the entire procedure. This addition, together with usability improvements in MTMDAT, enables high-throughput modeling of protein complexes from mass spectrometry data. The algorithm has been validated by using the protein-protein interaction between the ubiquitin-binding domain of proteasome component Rpn13 and ubiquitin. The resulting structural model, based on restraints extracted by MTMDAT from limited proteolysis and modeled by HADDOCK, was compared to the published NMR structure, which relied on twelve unambiguous intermolecular NOE interactions. The MTMDAT-HADDOCK structure was of similar quality to structures generated using only chemical shift perturbation data derived by NMR titration experiments. CONCLUSIONS The new MTMDAT-HADDOCK pipeline enables direct high-throughput modeling of protein complexes from mass spectrometry data. MTMDAT-HADDOCK can be downloaded from http://www.ifm.liu.se/chemistry/molbiotech/maria_sunnerhagens_group/mtmdat/together with the manual and example files. The program is free for academic/non-commercial purposes.
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Affiliation(s)
- Janosch Hennig
- Department of Physics, Chemistry, and Biology, Linköping University, SE-581 83 Linköping, Sweden.
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131
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Velasco-García R, Vargas-Martínez R. The study of protein–protein interactions in bacteria. Can J Microbiol 2012; 58:1241-57. [DOI: 10.1139/w2012-104] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.
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Affiliation(s)
- Roberto Velasco-García
- Laboratorio de Osmorregulación, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Estado de México, 54090
| | - Rocío Vargas-Martínez
- Laboratorio de Osmorregulación, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla, Estado de México, 54090
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132
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Yang P, Li XL, Mei JP, Kwoh CK, Ng SK. Positive-unlabeled learning for disease gene identification. Bioinformatics 2012; 28:2640-7. [PMID: 22923290 PMCID: PMC3467748 DOI: 10.1093/bioinformatics/bts504] [Citation(s) in RCA: 99] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2012] [Revised: 07/24/2012] [Accepted: 08/06/2012] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not exist) to build classifiers to identify new disease genes from the unknown genes. However, such kind of classifiers is actually built from a noisy negative set N as there can be unknown disease genes in N itself. As a result, the classifiers do not perform as well as they could be. RESULT Instead of treating the unknown genes as negative examples in N, we treat them as an unlabeled set U. We design a novel positive-unlabeled (PU) learning algorithm PUDI (PU learning for disease gene identification) to build a classifier using P and U. We first partition U into four sets, namely, reliable negative set RN, likely positive set LP, likely negative set LN and weak negative set WN. The weighted support vector machines are then used to build a multi-level classifier based on the four training sets and positive training set P to identify disease genes. Our experimental results demonstrate that our proposed PUDI algorithm outperformed the existing methods significantly. CONCLUSION The proposed PUDI algorithm is able to identify disease genes more accurately by treating the unknown data more appropriately as unlabeled set U instead of negative set N. Given that many machine learning problems in biomedical research do involve positive and unlabeled data instead of negative data, it is possible that the machine learning methods for these problems can be further improved by adopting PU learning methods, as we have done here for disease gene identification. AVAILABILITY AND IMPLEMENTATION The executable program and data are available at http://www1.i2r.a-star.edu.sg/~xlli/PUDI/PUDI.html.
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Affiliation(s)
- Peng Yang
- Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore.
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133
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Goh KI, Choi IG. Exploring the human diseasome: the human disease network. Brief Funct Genomics 2012; 11:533-42. [PMID: 23063808 DOI: 10.1093/bfgp/els032] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Advances in genome-scale molecular biology and molecular genetics have greatly elevated our knowledge on the basic components of human biology and diseases. At the same time, the importance of cellular networks between those biological components is increasingly appreciated. Built upon these recent technological and conceptual advances, a new discipline called the network medicine, an approach to understand human diseases from a network point-of-view, is about to emerge. In this review article, we will survey some recent endeavours along this direction, centred on the concept and applications of the human diseasome and the human disease network. Questions, and partial answers thereof, such as how the connectivity between molecular parts translates into the relationships between the related disorders on a global scale and how central the disease-causing genetic components are in the cellular network, will be discussed. The use of the diseasome in combination with various interactome networks and other disease-related factors is also reviewed.
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Affiliation(s)
- Kwang-Il Goh
- Department of Physics, Korea University, Seoul 136-713, Korea.
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134
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Guney E, Oliva B. Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization. PLoS One 2012; 7:e43557. [PMID: 23028459 PMCID: PMC3448640 DOI: 10.1371/journal.pone.0043557] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Accepted: 07/23/2012] [Indexed: 11/23/2022] Open
Abstract
Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.
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Affiliation(s)
- Emre Guney
- Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Barcelona, Catalonia, Spain
| | - Baldo Oliva
- Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra, Barcelona Research Park of Biomedicine (PRBB), Barcelona, Catalonia, Spain
- * E-mail:
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135
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Lewis ACF, Jones NS, Porter MA, Deane CM. What evidence is there for the homology of protein-protein interactions? PLoS Comput Biol 2012; 8:e1002645. [PMID: 23028270 PMCID: PMC3447968 DOI: 10.1371/journal.pcbi.1002645] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2011] [Accepted: 06/21/2012] [Indexed: 12/17/2022] Open
Abstract
The notion that sequence homology implies functional similarity underlies much of computational biology. In the case of protein-protein interactions, an interaction can be inferred between two proteins on the basis that sequence-similar proteins have been observed to interact. The use of transferred interactions is common, but the legitimacy of such inferred interactions is not clear. Here we investigate transferred interactions and whether data incompleteness explains the lack of evidence found for them. Using definitions of homology associated with functional annotation transfer, we estimate that conservation rates of interactions are low even after taking interactome incompleteness into account. For example, at a blastp E-value threshold of 10(-70), we estimate the conservation rate to be about 11 % between S. cerevisiae and H. sapiens. Our method also produces estimates of interactome sizes (which are similar to those previously proposed). Using our estimates of interaction conservation we estimate the rate at which protein-protein interactions are lost across species. To our knowledge, this is the first such study based on large-scale data. Previous work has suggested that interactions transferred within species are more reliable than interactions transferred across species. By controlling for factors that are specific to within-species interaction prediction, we propose that the transfer of interactions within species might be less reliable than transfers between species. Protein-protein interactions appear to be very rarely conserved unless very high sequence similarity is observed. Consequently, inferred interactions should be used with care.
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Affiliation(s)
- Anna C. F. Lewis
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Systems Biology Doctoral Training Centre, University of Oxford, Oxford, United Kingdom
| | - Nick S. Jones
- Department of Mathematics, Imperial College, London, United Kingdom
- Department of Physics, University of Oxford, Oxford, United Kingdom
- CABDyN Complexity Centre, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Integrative Systems Biology, University of Oxford, Oxford, United Kingdom
| | - Mason A. Porter
- CABDyN Complexity Centre, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Charlotte M. Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Integrative Systems Biology, University of Oxford, Oxford, United Kingdom
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Singh R, Lee MO, Lee JE, Choi J, Park JH, Kim EH, Yoo RH, Cho JI, Jeon JS, Rakwal R, Agrawal GK, Moon JS, Jwa NS. Rice mitogen-activated protein kinase interactome analysis using the yeast two-hybrid system. PLANT PHYSIOLOGY 2012; 160:477-87. [PMID: 22786887 PMCID: PMC3440221 DOI: 10.1104/pp.112.200071] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Accepted: 07/08/2012] [Indexed: 05/03/2023]
Abstract
Mitogen-activated protein kinase (MAPK) cascades support the flow of extracellular signals to intracellular target molecules and ultimately drive a diverse array of physiological functions in cells, tissues, and organisms by interacting with other proteins. Yet, our knowledge of the global physical MAPK interactome in plants remains largely fragmented. Here, we utilized the yeast two-hybrid system and coimmunoprecipitation, pull-down, bimolecular fluorescence complementation, subcellular localization, and kinase assay experiments in the model crop rice (Oryza sativa) to systematically map what is to our knowledge the first plant MAPK-interacting proteins. We identified 80 nonredundant interacting protein pairs (74 nonredundant interactors) for rice MAPKs and elucidated the novel proteome-wide network of MAPK interactors. The established interactome contains four membrane-associated proteins, seven MAP2Ks (for MAPK kinase), four MAPKs, and 59 putative substrates, including 18 transcription factors. Several interactors were also validated by experimental approaches (in vivo and in vitro) and literature survey. Our results highlight the importance of OsMPK1, an ortholog of tobacco (Nicotiana benthamiana) salicyclic acid-induced protein kinase and Arabidopsis (Arabidopsis thaliana) AtMPK6, among the rice MAPKs, as it alone interacts with 41 unique proteins (51.2% of the mapped MAPK interaction network). Additionally, Gene Ontology classification of interacting proteins into 34 functional categories suggested MAPK participation in diverse physiological functions. Together, the results obtained essentially enhance our knowledge of the MAPK-interacting protein network and provide a valuable research resource for developing a nearly complete map of the rice MAPK interactome.
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Affiliation(s)
- Raksha Singh
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Mi-Ok Lee
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Jae-Eun Lee
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Jihyun Choi
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Ji Hun Park
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Eun Hye Kim
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Ran Hee Yoo
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Jung-Il Cho
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Jong-Seong Jeon
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Randeep Rakwal
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Ganesh Kumar Agrawal
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
| | - Jae Sun Moon
- Department of Molecular Biology, College of Life Sciences, Sejong University, Gunja-dong, Gwangjin-gu, Seoul 143–747, Republic of Korea (R.S., M.-O.L., J.-E.L., J.C., J.H.P., E.H.K., N.-S.J.)
- Plant Systems Engineering Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 305–333, Republic of Korea (R.H.Y., J.S.M.); Biosystems and Bioengineering Program, University of Science and Technology, Yuseong-gu, Daejeon 305–350, Republic of Korea (R.H.Y., J.S.M.)
- Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin 446–701, Republic of Korea (J.-I.C., J.-S.J.)
- Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8572, Japan (R.R.)
- Department of Anatomy I, Showa University School of Medicine, Shinagawa, Tokyo 142–8555, Japan (R.R.)
- Research Laboratory for Biotechnology and Biochemistry, Kathmandu 44600, Nepal (R.R., G.K.A.)
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A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers. Hum Genet 2012; 132:15-27. [DOI: 10.1007/s00439-012-1218-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2012] [Accepted: 08/05/2012] [Indexed: 02/04/2023]
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138
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George CH, Parthimos D, Silvester NC. A network-oriented perspective on cardiac calcium signaling. Am J Physiol Cell Physiol 2012; 303:C897-910. [PMID: 22843795 DOI: 10.1152/ajpcell.00388.2011] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The normal contractile, electrical, and energetic function of the heart depends on the synchronization of biological oscillators and signal integrators that make up cellular signaling networks. In this review we interpret experimental data from molecular, cellular, and transgenic models of cardiac signaling behavior in the context of established concepts in cell network architecture and organization. Focusing on the cellular Ca(2+) handling machinery, we describe how the plasticity and adaptability of normal Ca(2+) signaling is dependent on dynamic network configurations that operate across a wide range of functional states. We consider how (mal)adaptive changes in signaling pathways restrict the dynamic range of the network such that it cannot respond appropriately to physiologic stimuli or perturbation. Based on these concepts, a model is proposed in which pathologic abnormalities in cardiac rhythm and contractility (e.g., arrhythmias and heart failure) arise as a consequence of progressive desynchronization and reduction in the dynamic range of the Ca(2+) signaling network. We discuss how a systems-level understanding of the network organization, cellular noise, and chaotic behavior may inform the design of new therapeutic modalities that prevent or reverse the disease-linked unraveling of the Ca(2+) signaling network.
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Affiliation(s)
- Christopher H George
- Wales Heart Research Institute and Institute of Molecular and Experimental Medicine, School of Medicine, Cardiff Univ., Heath Park, Cardiff, Wales, UK CF14 4XN.
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139
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140
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Integration of biological networks and pathways with genetic association studies. Hum Genet 2012; 131:1677-86. [PMID: 22777728 DOI: 10.1007/s00439-012-1198-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Accepted: 06/27/2012] [Indexed: 12/13/2022]
Abstract
Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.
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141
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Ho CL, Wu Y, Shen HB, Provart NJ, Geisler M. A predicted protein interactome for rice. RICE (NEW YORK, N.Y.) 2012; 5:15. [PMID: 24279740 PMCID: PMC4883691 DOI: 10.1186/1939-8433-5-15] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2012] [Accepted: 06/13/2012] [Indexed: 05/21/2023]
Abstract
BACKGROUND Protein-protein interactions (PPIs) create the steps in signaling and regulatory networks central to most fundamental biological processes. It is possible to predict these interactions by making use of experimentally determined orthologous interactions in other species. RESULTS In this study, prediction of PPIs in rice was carried out by the interolog method of mapping deduced orthologous genes to protein interactions supported by experimental evidence from reference organisms. We predicted 37112 interactions for 4567 rice proteins, including 1671 predicted self interactions (homo-interactions) and 35441 predicted interactions between different proteins (hetero-interactions). These matched 168 of 675 experimentally-determined interactions in rice. Interacting proteins were significantly more co-expressed than expected by chance, which is typical of experimentally-determined interactomes. The rice interacting proteins were divided topologically into 981 free ends (proteins with single interactions), 499 pipes (proteins with two interactions) and 3087 hubs of different sizes ranging from three to more than 100 interactions. CONCLUSIONS This predicted rice interactome extends known pathways and improves functional annotation of unknown rice proteins and networks in rice, and is easily explored with software tools presented here.
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Affiliation(s)
- Chai-Ling Ho
- />Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM-Serdang, Selangor Malaysia
| | - Yingzhou Wu
- />Department of Cell & Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2 Canada
| | - Hong-bin Shen
- />Department of Automation, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240 China
| | - Nicholas J Provart
- />Department of Cell & Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2 Canada
| | - Matt Geisler
- />Department of Plant Biology, Southern Illinois University Carbondale, 1125 Lincoln Ave., Life Science II, Carbondale, IL 62901-6509 USA
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142
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Jiang JQ, Wu M. Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study. BMC Bioinformatics 2012; 13 Suppl 10:S20. [PMID: 22759426 PMCID: PMC3314587 DOI: 10.1186/1471-2105-13-s10-s20] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard. RESULTS We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies. CONCLUSIONS Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.
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Affiliation(s)
- Jonathan Q Jiang
- School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China
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143
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Affiliation(s)
- Ian W. Taylor
- Samuel Lunenfeld Research Institute; Mount Sinai Hospital; Toronto Ontario Canada
- Department of Molecular Genetics; University of Toronto; Toronto Ontario Canada
| | - Jeffrey L. Wrana
- Samuel Lunenfeld Research Institute; Mount Sinai Hospital; Toronto Ontario Canada
- Department of Molecular Genetics; University of Toronto; Toronto Ontario Canada
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144
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Eronen L, Toivonen H. Biomine: predicting links between biological entities using network models of heterogeneous databases. BMC Bioinformatics 2012; 13:119. [PMID: 22672646 PMCID: PMC3505483 DOI: 10.1186/1471-2105-13-119] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2011] [Accepted: 04/17/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. RESULTS Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. CONCLUSIONS The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.
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Affiliation(s)
- Lauri Eronen
- Biocomputing Platforms Ltd, Innopoli 2, Tekniikantie 14, , FI-02150 Espoo, Finland.
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Hallock P, Thomas MA. Integrating the Alzheimer's disease proteome and transcriptome: a comprehensive network model of a complex disease. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2012; 16:37-49. [PMID: 22321014 DOI: 10.1089/omi.2011.0054] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Network models combined with gene expression studies have become useful tools for studying complex diseases like Alzheimer's disease. We constructed a "Core" Alzheimer's disease protein interaction network by human curation of the primary literature. The Core network consisted of 775 nodes and 2,204 interactions. To our knowledge, this is the most comprehensive and accurate protein interaction network yet constructed for Alzheimer's disease. An "Expanded" network was computationally constructed by adding additional proteins that interacted with Core network proteins, and consisted of 4,945 nodes and 26,064 interactions. We then mapped existing gene expression studies to the Core network. This combined data model identified the MAPK/ERK pathway and clathrin-mediated receptor endocytosis as key pathways in Alzheimer's disease. Important proteins in the MAPK/ERK pathway that interacted in the Core network formed a downregulated cluster of nodes, whereas clathrin and several clathrin accessory proteins that interacted in the Core network formed an upregulated cluster of nodes. The MAPK/ERK pathway is a key component in synaptic plasticity and learning, processes disrupted in Alzheimer's. Clathrin and clathrin adaptor proteins are involved in the endocytosis of the APP protein that can lead to increased intracellular levels of amyloid beta peptide, contributing to the progression of Alzheimer's.
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Affiliation(s)
- Peter Hallock
- Department of Biological Sciences, Idaho State University, Pocatello, USA
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146
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Jaeger S, Aloy P. From protein interaction networks to novel therapeutic strategies. IUBMB Life 2012; 64:529-37. [DOI: 10.1002/iub.1040] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2012] [Accepted: 03/14/2012] [Indexed: 01/18/2023]
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147
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Schleker S, Garcia-Garcia J, Klein-Seetharaman J, Oliva B. Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes. Chem Biodivers 2012; 9:991-1018. [PMID: 22589098 PMCID: PMC3407687 DOI: 10.1002/cbdv.201100392] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Salmonellosis caused by Salmonella bacteria is a food-borne disease and a worldwide health threat causing millions of infections and thousands of deaths every year. This pathogen infects an unusually broad range of host organisms including human and plants. A better understanding of the mechanisms of communication between Salmonella and its hosts requires identifying the interactions between Salmonella and host proteins. Protein-protein interactions (PPIs) are the fundamental building blocks of communication. Here, we utilize the prediction platform BIANA to obtain the putative Salmonella-human and Salmonella-Arabidopsis interactomes based on sequence and domain similarity to known PPIs. A gold standard list of Salmonella-host PPIs served to validate the quality of the human model. 24,726 and 10,926 PPIs comprising interactions between 38 and 33 Salmonella effectors and virulence factors with 9,740 human and 4,676 Arabidopsis proteins, respectively, were predicted. Putative hub proteins could be identified, and parallels between the two interactomes were discovered. This approach can provide insight into possible biological functions of so far uncharacterized proteins. The predicted interactions are available via a web interface which allows filtering of the database according to parameters provided by the user to narrow down the list of suspected interactions. The interactions are available via a web interface at http://sbi.imim.es/web/SHIPREC.php.
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Affiliation(s)
- Sylvia Schleker
- Forschungszentrum Jülich, Institute of Complex Systems (ICS-5), 52425 Jülich, Germany
| | - Javier Garcia-Garcia
- Structural Bioinformatics Group (GRIB-IMIM). Universitat Pompeu Fabra. Barcelona Research Park of Biomedicine (PRBB), Barcelona 08003, Catalonia, Spain (phone: +34 933 160 509; fax: +34 933 160 550
| | - Judith Klein-Seetharaman
- Forschungszentrum Jülich, Institute of Complex Systems (ICS-5), 52425 Jülich, Germany
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA (phone: +1 412 383 7325; fax: +1 412 648 8998
| | - Baldo Oliva
- Structural Bioinformatics Group (GRIB-IMIM). Universitat Pompeu Fabra. Barcelona Research Park of Biomedicine (PRBB), Barcelona 08003, Catalonia, Spain (phone: +34 933 160 509; fax: +34 933 160 550
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148
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Lai YH, Li ZC, Chen LL, Dai Z, Zou XY. Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach. J Proteomics 2012; 75:2500-13. [DOI: 10.1016/j.jprot.2012.02.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Revised: 02/21/2012] [Accepted: 02/26/2012] [Indexed: 12/31/2022]
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149
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Barberis M. Sic1 as a timer of Clb cyclin waves in the yeast cell cycle--design principle of not just an inhibitor. FEBS J 2012; 279:3386-410. [PMID: 22356687 DOI: 10.1111/j.1742-4658.2012.08542.x] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Cellular systems biology aims to uncover design principles that describe the properties of biological networks through interaction of their components in space and time. The cell cycle is a complex system regulated by molecules that are integrated into functional modules to ensure genome integrity and faithful cell division. In budding yeast, cyclin-dependent kinases (Cdk1/Clb) drive cell cycle progression, being activated and inactivated in a precise temporal sequence. In this module, which we refer to as the 'Clb module', different Cdk1/Clb complexes are regulated to generate waves of Clb activity, a functional property of cell cycle control. The inhibitor Sic1 plays a critical role in the Clb module by binding to and blocking Cdk1/Clb activity, ultimately setting the timing of DNA replication and mitosis. Fifteen years of research subsequent to the identification of Sic1 have lead to the development of an integrative approach that addresses its role in regulating the Clb module. Sic1 is an intrinsically disordered protein and achieves its inhibitory function by cooperative binding, where different structural regions stretch on the Cdk1/Clb surface. Moreover, Sic1 promotes S phase entry, facilitating Cdk1/Clb5 nuclear transport, and therefore revealing a double function of inhibitor/activator that rationalizes a mechanism to prevent precocious DNA replication. Interestingly, the investigation of Clb temporal dynamics by mathematical modelling and experimental validation provides evidence that Sic1 acts as a timer to coordinate oscillations of Clb cyclin waves. Here we review these findings, focusing on the design principle underlying the Clb module, which highlights the role of Sic1 in regulating phase-specific Cdk1/Clb activities.
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Affiliation(s)
- Matteo Barberis
- Institute for Biology, Theoretical Biophysics, Humboldt University Berlin, Germany.
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150
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Planas-Iglesias J, Guney E, García-García J, Robertson KA, Raza S, Freeman TC, Ghazal P, Oliva B. Extending signaling pathways with protein-interaction networks. Application to apoptosis. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2012; 16:245-56. [PMID: 22385281 DOI: 10.1089/omi.2011.0130] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.
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Affiliation(s)
- Joan Planas-Iglesias
- Structural Bioinformatics Group, GRIB, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
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