51
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Drew K, Lee C, Huizar RL, Tu F, Borgeson B, McWhite CD, Ma Y, Wallingford JB, Marcotte EM. Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes. Mol Syst Biol 2017; 13:932. [PMID: 28596423 PMCID: PMC5488662 DOI: 10.15252/msb.20167490] [Citation(s) in RCA: 146] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k‐cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease‐associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.
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Affiliation(s)
- Kevin Drew
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA
| | - Chanjae Lee
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Ryan L Huizar
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Fan Tu
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Blake Borgeson
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Claire D McWhite
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Yun Ma
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.,The Otolaryngology Hospital, The First Affiliated Hospital of Sun Yat-sen University Sun Yat-sen University, Guangzhou, China
| | - John B Wallingford
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
| | - Edward M Marcotte
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX, USA .,Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA
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52
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Computational modeling of protein assemblies. Curr Opin Struct Biol 2017; 44:179-189. [DOI: 10.1016/j.sbi.2017.04.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
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53
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Zhang W, Xu J, Li Y, Zou X. A new two-stage method for revealing missing parts of edges in protein-protein interaction networks. PLoS One 2017; 12:e0177029. [PMID: 28493910 PMCID: PMC5426645 DOI: 10.1371/journal.pone.0177029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2016] [Accepted: 04/20/2017] [Indexed: 12/24/2022] Open
Abstract
With the increasing availability of high-throughput data, various computational methods have recently been developed for understanding the cell through protein-protein interaction (PPI) networks at a systems level. However, due to the incompleteness of the original PPI networks those efforts have been significantly hindered. In this paper, we propose a two stage method to predict underlying links between two originally unlinked protein pairs. First, we measure gene expression and gene functional similarly between unlinked protein pairs on Saccharomyces cerevisiae benchmark network and obtain new constructed networks. Then, we select the significant part of the new predicted links by analyzing the difference between essential proteins that have been identified based on the new constructed networks and the original network. Furthermore, we validate the performance of the new method by using the reliable and comprehensive PPI dataset obtained from the STRING database and compare the new proposed method with four other random walk-based methods. Comparing the results indicates that the new proposed strategy performs well in predicting underlying links. This study provides a general paradigm for predicting new interactions between protein pairs and offers new insights into identifying essential proteins.
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Affiliation(s)
- Wei Zhang
- School of Science, East China Jiaotong University, Nanchang 330013, China
- * E-mail: (WZ); (XFZ)
| | - Jia Xu
- School of Mechatronic Engineering, East China Jiaotong University, Nanchang 330013, China
| | - Yuanyuan Li
- School of Mathematics and Statistics, Wuhan Institute of Technology in Wuhan, Wuhan, 430072, China
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
- * E-mail: (WZ); (XFZ)
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54
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Luck K, Sheynkman GM, Zhang I, Vidal M. Proteome-Scale Human Interactomics. Trends Biochem Sci 2017; 42:342-354. [PMID: 28284537 DOI: 10.1016/j.tibs.2017.02.006] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 02/10/2017] [Accepted: 02/16/2017] [Indexed: 01/28/2023]
Abstract
Cellular functions are mediated by complex interactome networks of physical, biochemical, and functional interactions between DNA sequences, RNA molecules, proteins, lipids, and small metabolites. A thorough understanding of cellular organization requires accurate and relatively complete models of interactome networks at proteome scale. The recent publication of four human protein-protein interaction (PPI) maps represents a technological breakthrough and an unprecedented resource for the scientific community, heralding a new era of proteome-scale human interactomics. Our knowledge gained from these and complementary studies provides fresh insights into the opportunities and challenges when analyzing systematically generated interactome data, defines a clear roadmap towards the generation of a first reference interactome, and reveals new perspectives on the organization of cellular life.
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Affiliation(s)
- Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | - Gloria M Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
| | - Ivy Zhang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
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55
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Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome. Artif Intell Med 2017; 77:53-63. [DOI: 10.1016/j.artmed.2017.03.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Revised: 01/06/2017] [Accepted: 03/17/2017] [Indexed: 01/16/2023]
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56
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Singh P, Madhaiyan K, Duong-Thi MD, Dymock BW, Ohlson S. Analysis of Protein Target Interactions of Synthetic Mixtures by Affinity-LC/MS. SLAS DISCOVERY 2017; 22:440-446. [PMID: 28328315 DOI: 10.1177/2472555216687964] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Analysis of interactions between molecules is of fundamental importance in life science research. In this study, we applied weak affinity chromatography, based on high-performance liquid chromatography and mass spectrometry, as a powerful tool for direct analysis of the components of a chemical reaction mixture for their binding to a target protein. As a demonstration of the potential of this method, we analyzed the binding of the compounds of the reaction mixture to the chaperone heat shock protein 90 (Hsp90). It was possible to analyze quantitatively the binding of the components of the mixture to the target independently from each other without any preceding process such as purification. This feature has wide implications in biological sciences as crude mixtures, either natural or synthetic, can be analyzed directly for their possible binding to a target. This method could lead to savings in costs and labor through shortening chemical research project development time.
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Affiliation(s)
- Prachi Singh
- 1 School of Biological Sciences, Nanyang Technological University (NTU), Singapore
| | | | - Minh-Dao Duong-Thi
- 1 School of Biological Sciences, Nanyang Technological University (NTU), Singapore
| | - Brian W Dymock
- 2 Department of Pharmacy, National University of Singapore (NUS), Singapore
| | - Sten Ohlson
- 1 School of Biological Sciences, Nanyang Technological University (NTU), Singapore
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57
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Scott NE, Rogers LD, Prudova A, Brown NF, Fortelny N, Overall CM, Foster LJ. Interactome disassembly during apoptosis occurs independent of caspase cleavage. Mol Syst Biol 2017; 13:906. [PMID: 28082348 PMCID: PMC5293159 DOI: 10.15252/msb.20167067] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Protein-protein interaction networks (interactomes) define the functionality of all biological systems. In apoptosis, proteolysis by caspases is thought to initiate disassembly of protein complexes and cell death. Here we used a quantitative proteomics approach, protein correlation profiling (PCP), to explore changes in cytoplasmic and mitochondrial interactomes in response to apoptosis initiation as a function of caspase activity. We measured the response to initiation of Fas-mediated apoptosis in 17,991 interactions among 2,779 proteins, comprising the largest dynamic interactome to date. The majority of interactions were unaffected early in apoptosis, but multiple complexes containing known caspase targets were disassembled. Nonetheless, proteome-wide analysis of proteolytic processing by terminal amine isotopic labeling of substrates (TAILS) revealed little correlation between proteolytic and interactome changes. Our findings show that, in apoptosis, significant interactome alterations occur before and independently of caspase activity. Thus, apoptosis initiation includes a tight program of interactome rearrangement, leading to disassembly of relatively few, select complexes. These early interactome alterations occur independently of cleavage of these protein by caspases.
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Affiliation(s)
- Nichollas E Scott
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Lindsay D Rogers
- Department of Oral Biological and Medical Sciences, University of British Columbia, Vancouver, BC, Canada.,Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Anna Prudova
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Nat F Brown
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Nikolaus Fortelny
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.,Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Christopher M Overall
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.,Department of Oral Biological and Medical Sciences, University of British Columbia, Vancouver, BC, Canada.,Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada .,Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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58
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Yu L, Wang B, Ma X, Gao L. The extraction of drug-disease correlations based on module distance in incomplete human interactome. BMC SYSTEMS BIOLOGY 2016; 10:111. [PMID: 28155709 PMCID: PMC5260043 DOI: 10.1186/s12918-016-0364-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND Extracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete. RESULTS We present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results. CONCLUSION The overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China.
| | - Bingbo Wang
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, People's Republic of China
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59
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Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res 2016; 45:D408-D414. [PMID: 27794551 PMCID: PMC5210659 DOI: 10.1093/nar/gkw985] [Citation(s) in RCA: 280] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 09/28/2016] [Accepted: 10/14/2016] [Indexed: 01/01/2023] Open
Abstract
The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein–protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein–Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.
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Affiliation(s)
- Gregorio Alanis-Lobato
- Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany
- Institute of Molecular Biology, Mainz, Germany
| | - Miguel A Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany
- Institute of Molecular Biology, Mainz, Germany
| | - Martin H Schaefer
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
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60
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Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B. A computational interactome and functional annotation for the human proteome. eLife 2016; 5. [PMID: 27770567 PMCID: PMC5115866 DOI: 10.7554/elife.18715] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 10/19/2016] [Indexed: 12/14/2022] Open
Abstract
We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.
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Affiliation(s)
- José Ignacio Garzón
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
| | - Lei Deng
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,School of Software, Central South University, Changsha, China
| | - Diana Murray
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
| | - Sagi Shapira
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,Department of Microbiology and Immunology, Columbia University, New York, United States
| | - Donald Petrey
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,Howard Hughes Medical Institute, Columbia University, New York, United States
| | - Barry Honig
- Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States.,Howard Hughes Medical Institute, Columbia University, New York, United States.,Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States.,Department of Medicine, Columbia University, New York, United States.,Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States
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61
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Abstract
Genes carrying mutations associated with genetic diseases are present in all human cells; yet, clinical manifestations of genetic diseases are usually highly tissue-specific. Although some disease genes are expressed only in selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue specificity of human diseases. Here we hypothesize that for a disease to manifest itself in a particular tissue, a whole functional subnetwork of genes (disease module) needs to be expressed in that tissue. Driven by this hypothesis, we conducted a systematic study of the expression patterns of disease genes within the human interactome. We find that genes expressed in a specific tissue tend to be localized in the same neighborhood of the interactome. By contrast, genes expressed in different tissues are segregated in distinct network neighborhoods. Most important, we show that it is the integrity and the completeness of the expression of the disease module that determines disease manifestation in selected tissues. This approach allows us to construct a disease-tissue network that confirms known and predicts unexpected disease-tissue associations.
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62
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Tonddast-Navaei S, Skolnick J. Are protein-protein interfaces special regions on a protein's surface? J Chem Phys 2016; 143:243149. [PMID: 26723634 DOI: 10.1063/1.4937428] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Protein-protein interactions (PPIs) are involved in many cellular processes. Experimentally obtained protein quaternary structures provide the location of protein-protein interfaces, the surface region of a given protein that interacts with another. These regions are termed half-interfaces (HIs). Canonical HIs cover roughly one third of a protein's surface and were found to have more hydrophobic residues than the non-interface surface region. In addition, the classical view of protein HIs was that there are a few (if not one) HIs per protein that are structurally and chemically unique. However, on average, a given protein interacts with at least a dozen others. This raises the question of whether they use the same or other HIs. By copying HIs from monomers with the same folds in solved quaternary structures, we introduce the concept of geometric HIs (HIs whose geometry has a significant match to other known interfaces) and show that on average they cover three quarters of a protein's surface. We then demonstrate that in some cases, these geometric HI could result in real physical interactions (which may or may not be biologically relevant). The composition of the new HIs is on average more charged compared to most known ones, suggesting that the current protein interface database is biased towards more hydrophobic, possibly more obligate, complexes. Finally, our results provide evidence for interface fuzziness and PPI promiscuity. Thus, the classical view of unique, well defined HIs needs to be revisited as HIs are another example of coarse-graining that is used by nature.
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Affiliation(s)
- Sam Tonddast-Navaei
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street N.W., Atlanta, Georgia 30318, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 250 14th Street N.W., Atlanta, Georgia 30318, USA
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63
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Combining Design and Selection to Create Novel Protein-Peptide Interactions. Methods Enzymol 2016. [PMID: 27586335 DOI: 10.1016/bs.mie.2016.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
The ability to design new protein-protein interactions (PPIs) has many applications in biotechnology and medicine. The goal of designed PPIs is to achieve both high affinity and specificity for the target protein. A great challenge in protein design is to identify such proteins from an enormous number of potential sequences. Many computational and experimental methods have been developed to contend with this challenge. Here we describe one particularly powerful approach-semirational design-that combines design and selection. This approach has been applied to generate new PPIs for many applications, including novel affinity reagents for protein detection/purification and bioorthogonal modules for synthetic biology (Jackrel, Valverde, & Regan, 2009; Sawyer et al., 2014; Speltz, Brown, Hajare, Schlieker, & Regan, 2015; Speltz, Nathan, & Regan, 2015).
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64
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Cheng F, Liu C, Shen B, Zhao Z. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 3:65. [PMID: 27585651 PMCID: PMC5009528 DOI: 10.1186/s12918-016-0309-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. RESULTS In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and further contribute to tyrosine kinase inhibitor (e.g., gefitinib) resistance. CONCLUSION In summary, we demonstrated that network properties such as network entropy and unbalanced motifs associated with tumor initiation, progression, and anticancer drug responses, suggesting new potential network-based prognostic and predictive measure in cancer.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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65
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Zhou J. Effect of Protein Repetitiveness on Protein-Protein Interaction Prediction Results Using Support Vector Machines. J Comput Biol 2016; 24:183-192. [PMID: 27529135 DOI: 10.1089/cmb.2015.0233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND There are many computational approaches to predict the protein-protein interactions using support vector machines (SVMs) with high performance. In fact, performance of currently reported methods are significantly over-estimated and affected by the object repetitiveness in the datasets used. OBJECTIVE To study the effect of object repetitiveness of datasets on predicting results. METHOD We present novel methods to construct different positive datasets with or without repeating proteins using graph maximum matching in the protein-protein interaction datasets and corresponding series of negative datasets with different proteins repetitiveness are constructed using graph adjacency matrix. The relationship between the SVM prediction results and the repeated proteins (repeat numbers and repeat rates) and the distributions of repeated proteins in the datasets are analyzed. RESULTS Protein repetitiveness of positive and negative datasets can affect the prediction result: high protein repetitiveness of positive or negative datasets yield high performance prediction result. CONCLUSION This indicate that dealing with object repetitiveness of datasets is a key issue in protein-protein interactions prediction using SVMs since real world data contain certain degrees of repeat proteins.
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Affiliation(s)
- Jie Zhou
- Guangdong Province Key Laboratory of Computer Network, School of Computer Science and Engineering, South China University of Technology , Guangzhou, China
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66
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Xing S, Wallmeroth N, Berendzen KW, Grefen C. Techniques for the Analysis of Protein-Protein Interactions in Vivo. PLANT PHYSIOLOGY 2016; 171:727-58. [PMID: 27208310 PMCID: PMC4902627 DOI: 10.1104/pp.16.00470] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 04/19/2016] [Indexed: 05/20/2023]
Abstract
Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.
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Affiliation(s)
- Shuping Xing
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| | - Niklas Wallmeroth
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| | - Kenneth W Berendzen
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
| | - Christopher Grefen
- University of Tübingen, ZMBP Developmental Genetics (S.X., N.W., C.G.) and ZMBP Central Facilities (K.W.B.), D-72076 Tuebingen, Germany
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67
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Schizophrenia interactome with 504 novel protein-protein interactions. NPJ SCHIZOPHRENIA 2016; 2:16012. [PMID: 27336055 PMCID: PMC4898894 DOI: 10.1038/npjschz.2016.12] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 02/17/2016] [Accepted: 02/23/2016] [Indexed: 11/29/2022]
Abstract
Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein–protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.
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68
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Keskin O, Tuncbag N, Gursoy A. Predicting Protein–Protein Interactions from the Molecular to the Proteome Level. Chem Rev 2016; 116:4884-909. [DOI: 10.1021/acs.chemrev.5b00683] [Citation(s) in RCA: 207] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Nurcan Tuncbag
- Graduate
School of Informatics, Department of Health Informatics, Middle East Technical University, 06800 Ankara, Turkey
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69
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Rigid-Docking Approaches to Explore Protein-Protein Interaction Space. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:33-55. [PMID: 27830312 DOI: 10.1007/10_2016_41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.
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70
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Simões SN, Martins DC, Pereira CAB, Hashimoto RF, Brentani H. NERI: network-medicine based integrative approach for disease gene prioritization by relative importance. BMC Bioinformatics 2015; 16 Suppl 19:S9. [PMID: 26696568 PMCID: PMC4686785 DOI: 10.1186/1471-2105-16-s19-s9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease. Results The goal of this work is to develop a methodology that integrates PPI networks with disease specific data sources, such as GWAS and gene expression, to find genes more specific of a given complex disease. After the integration of PPI networks and gene expression data, the resulting network is used to connect genes related to the disease through the shortest paths that have the greatest concordance between their gene expressions. Both case and control expression data are used separately and, at the end, the most altered genes between the two conditions are selected. To evaluate the method, schizophrenia was adopted as case study. Conclusion Results show that the proposed method successfully retrieves differentially coexpressed genes in two conditions, while avoiding the bias from literature. Moreover we were able to achieve a greater concordance in the selection of important genes from different microarray studies of the same disease and to produce a more specific gene set related to the studied disease.
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71
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Abstract
The properties of living cells are mediated by a huge number of ever-changing interactions of their component macromolecules forming living machines; collectively, these are termed the interactome. Pathogenic alterations in interactomes mechanistically underlie diseases. Therefore, there exists an essential need for much better tools to reveal and dissect interactomes. This need is only now beginning to be met.
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Affiliation(s)
- John D Aitchison
- Center for Infectious Disease Research (formerly Seattle Biomedical Research Institute), Seattle, WA 98109 Institute for Systems Biology, Seattle, WA 98109
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065
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72
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Yao J, Guo H, Yang X. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction. Int J Genomics 2015; 2015:608042. [PMID: 26539460 PMCID: PMC4619929 DOI: 10.1155/2015/608042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Revised: 07/22/2015] [Accepted: 07/26/2015] [Indexed: 12/15/2022] Open
Abstract
Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.
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Affiliation(s)
- Jianzhuang Yao
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - Hong Guo
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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73
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Jacunski A, Dixon SJ, Tatonetti NP. Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality. PLoS Comput Biol 2015; 11:e1004506. [PMID: 26451775 PMCID: PMC4599967 DOI: 10.1371/journal.pcbi.1004506] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 08/11/2015] [Indexed: 12/22/2022] Open
Abstract
Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies. Synthetic lethality is a genetic interaction that has promising implications for informing novel cancer therapies. Over 200 million pairwise tests would be required to identify all pairwise synthetic lethal interactions in humans–currently, an impossibly large experimental burden. To simplify the process, we have developed a method to predict human synthetic lethal pairs in translation from a well-studied species to one in which synthetic lethality is understudied using both species’ protein-protein interaction networks. Here, we explore the model’s success in translation from S. cerevisiae to S. pombe. We then predict human synthetic lethality and suggest novel areas of inquiry for cancer therapies.
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Affiliation(s)
- Alexandra Jacunski
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Scott J. Dixon
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Nicholas P. Tatonetti
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
- Department of Medicine, Columbia University, New York, New York, United States of America
- * E-mail:
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74
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Zhao S, Shojaie A. A significance test for graph-constrained estimation. Biometrics 2015; 72:484-93. [PMID: 26393533 DOI: 10.1111/biom.12418] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2015] [Revised: 06/01/2015] [Accepted: 08/01/2015] [Indexed: 11/28/2022]
Abstract
Graph-constrained estimation methods encourage similarities among neighboring covariates presented as nodes of a graph, and can result in more accurate estimates, especially in high-dimensional settings. Variable selection approaches can then be utilized to select a subset of variables that are associated with the response. However, existing procedures do not provide measures of uncertainty of estimates. Further, the vast majority of existing approaches assume that available graph accurately captures the association among covariates; violations to this assumption could severely hurt the reliability of the resulting estimates. In this article, we present a new inference framework, called the Grace test, which produces coefficient estimates and corresponding p-values by incorporating the external graph information. We show, both theoretically and via numerical studies, that the proposed method asymptotically controls the type-I error rate regardless of the choice of the graph. We also show that when the underlying graph is informative, the Grace test is asymptotically more powerful than similar tests that ignore the external information. We study the power properties of the proposed test when the graph is not fully informative and develop a more powerful Grace-ridge test for such settings. Our numerical studies show that as long as the graph is reasonably informative, the proposed inference procedures deliver improved statistical power over existing methods that ignore external information.
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Affiliation(s)
- Sen Zhao
- Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A
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75
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Wan C, Borgeson B, Phanse S, Tu F, Drew K, Clark G, Xiong X, Kagan O, Kwan J, Bezginov A, Chessman K, Pal S, Cromar G, Papoulas O, Ni Z, Boutz DR, Stoilova S, Havugimana PC, Guo X, Malty RH, Sarov M, Greenblatt J, Babu M, Derry WB, Tillier ER, Wallingford JB, Parkinson J, Marcotte EM, Emili A. Panorama of ancient metazoan macromolecular complexes. Nature 2015; 525:339-44. [PMID: 26344197 PMCID: PMC5036527 DOI: 10.1038/nature14877] [Citation(s) in RCA: 364] [Impact Index Per Article: 40.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 06/30/2015] [Indexed: 12/21/2022]
Abstract
Macromolecular complexes are essential to conserved biological processes, but their prevalence across animals is unclear. By combining extensive biochemical fractionation with quantitative mass spectrometry, we directly examined the composition of soluble multiprotein complexes among diverse metazoan models. Using an integrative approach, we then generated a draft conservation map consisting of >1 million putative high-confidence co-complex interactions for species with fully sequenced genomes that encompasses functional modules present broadly across all extant animals. Clustering revealed a spectrum of conservation, ranging from ancient Eukaryal assemblies likely serving cellular housekeeping roles for at least 1 billion years, ancestral complexes that have accrued contemporary components, and rarer metazoan innovations linked to multicellularity. We validated these projections by independent co-fractionation experiments in evolutionarily distant species, by affinity-purification and by functional analyses. The comprehensiveness, centrality and modularity of these reconstructed interactomes reflect their fundamental mechanistic significance and adaptive value to animal cell systems.
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Affiliation(s)
- Cuihong Wan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA
| | - Blake Borgeson
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA
| | - Sadhna Phanse
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Fan Tu
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA
| | - Kevin Drew
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA
| | - Greg Clark
- Department of Medical Biophysics, Toronto, Ontario M5G 1L7, Canada
| | - Xuejian Xiong
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Olga Kagan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Julian Kwan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | | | - Kyle Chessman
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Swati Pal
- Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Graham Cromar
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Ophelia Papoulas
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA
| | - Zuyao Ni
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Daniel R Boutz
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA
| | - Snejana Stoilova
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Pierre C Havugimana
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Xinghua Guo
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Ramy H Malty
- Department of Biochemistry, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
| | - Mihail Sarov
- Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
| | - Jack Greenblatt
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
| | - W Brent Derry
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | | | - John B Wallingford
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712, USA
| | - John Parkinson
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Edward M Marcotte
- Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, Texas 78712, USA.,Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712, USA
| | - Andrew Emili
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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76
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Yu F, Yang Z, Hu X, Sun Y, Lin H, Wang J. Protein complex detection in PPI networks based on data integration and supervised learning method. BMC Bioinformatics 2015; 16 Suppl 12:S3. [PMID: 26329886 PMCID: PMC4705505 DOI: 10.1186/1471-2105-16-s12-s3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. Methods The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. Results The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. Conclusions The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.
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77
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Cheng F, Zhao J, Zhao Z. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform 2015; 17:642-56. [PMID: 26307061 DOI: 10.1093/bib/bbv068] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Indexed: 12/27/2022] Open
Abstract
Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
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78
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Pan A, Lahiri C, Rajendiran A, Shanmugham B. Computational analysis of protein interaction networks for infectious diseases. Brief Bioinform 2015; 17:517-26. [PMID: 26261187 PMCID: PMC7110031 DOI: 10.1093/bib/bbv059] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Indexed: 12/13/2022] Open
Abstract
Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.
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79
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Parikshak NN, Gandal MJ, Geschwind DH. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet 2015; 16:441-58. [PMID: 26149713 PMCID: PMC4699316 DOI: 10.1038/nrg3934] [Citation(s) in RCA: 287] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Genetic and genomic approaches have implicated hundreds of genetic loci in neurodevelopmental disorders and neurodegeneration, but mechanistic understanding continues to lag behind the pace of gene discovery. Understanding the role of specific genetic variants in the brain involves dissecting a functional hierarchy that encompasses molecular pathways, diverse cell types, neural circuits and, ultimately, cognition and behaviour. With a focus on transcriptomics, this Review discusses how high-throughput molecular, integrative and network approaches inform disease biology by placing human genetics in a molecular systems and neurobiological context. We provide a framework for interpreting network biology studies and leveraging big genomics data sets in neurobiology.
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Affiliation(s)
- Neelroop N Parikshak
- 1] Program in Neurobehavioral Genetics, Semel Institute, and Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA. [2] Interdepartmental Program in Neuroscience, University of California, Los Angeles, California 90095, USA
| | - Michael J Gandal
- 1] Program in Neurobehavioral Genetics, Semel Institute, and Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA. [2] Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
| | - Daniel H Geschwind
- 1] Program in Neurobehavioral Genetics, Semel Institute, and Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA. [2] Interdepartmental Program in Neuroscience, University of California, Los Angeles, California 90095, USA. [3] Center for Autism Treatment and Research, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA. [4] Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
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80
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Zhu L, Deng SP, Huang DS. A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks. IEEE Trans Nanobioscience 2015; 14:528-34. [PMID: 25861086 DOI: 10.1109/tnb.2015.2420754] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Protein-protein interactions (PPIs) play essential roles for determining the outcomes of most of the cellular functions of the cell. Although the experimentally detected high-throughput PPI data promise new opportunities for the study of many biological mechanisms including cellular metabolism and protein functions, experimentally detected PPIs have high levels of false positive rate. Therefore, it is of high practical value to develop novel computational tools for pruning low-confidence PPIs. In this paper, we propose a new geometric approach called Leave-One-Out Logistic Metric Embedding (LOO-LME) for assessing the reliability of interactions. Unlike previous approaches which mainly seek to preserve the noisy topological information of the PPI networks in the embedding space, LOO-LME first transforms the learning task into an equivalent discriminant form, then directly deals with the uncertainty in PPI networks using a leave-one-out-style approach. The experimental results show that LOO-LME substantially outperforms previous methods on PPI assessment problems. LOO-LME could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge.
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81
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Ghiassian SD, Menche J, Barabási AL. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput Biol 2015; 11:e1004120. [PMID: 25853560 PMCID: PMC4390154 DOI: 10.1371/journal.pcbi.1004120] [Citation(s) in RCA: 222] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2014] [Accepted: 01/09/2015] [Indexed: 01/08/2023] Open
Abstract
The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases. Diseases are rarely the result of an abnormality in a single gene, but involve a whole cascade of interactions between several cellular processes. To disentangle these complex interactions it is necessary to study genotype-phenotype relationships in the context of protein-protein interaction networks. Our analysis of 70 diseases shows that disease proteins are not randomly scattered within these networks, but agglomerate in specific regions, suggesting the existence of specific disease modules for each disease. The identification of these modules is the first step towards elucidating the biological mechanisms of a disease or for a targeted search of drug targets. We present a systematic analysis of the connectivity patterns of disease proteins and determine the most predictive topological property for their identification. This allows us to rationally design a reliable and efficient Disease Module Detection algorithm (DIAMOnD).
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Affiliation(s)
- Susan Dina Ghiassian
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Jörg Menche
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Center for Network Science, Central European University, Budapest, Hungary
| | - Albert-László Barabási
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Center for Network Science, Central European University, Budapest, Hungary
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
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82
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Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, Barabási AL. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 2015; 347:1257601. [PMID: 25700523 PMCID: PMC4435741 DOI: 10.1126/science.1257601] [Citation(s) in RCA: 875] [Impact Index Per Article: 97.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
According to the disease module hypothesis, the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data have sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant coexpression patterns, symptom similarity, and comorbidity, whereas diseases residing in separated network neighborhoods are phenotypically distinct. These tools represent an interactome-based platform to predict molecular commonalities between phenotypically related diseases, even if they do not share primary disease genes.
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Affiliation(s)
- Jörg Menche
- Center for Complex Networks Research and Department of Physics, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, MA 02115, USA. Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA. Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
| | - Amitabh Sharma
- Center for Complex Networks Research and Department of Physics, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, MA 02115, USA. Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Maksim Kitsak
- Center for Complex Networks Research and Department of Physics, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, MA 02115, USA. Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Susan Dina Ghiassian
- Center for Complex Networks Research and Department of Physics, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, MA 02115, USA. Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA. Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Albert-László Barabási
- Center for Complex Networks Research and Department of Physics, Northeastern University, 110 Forsyth Street, 111 Dana Research Center, Boston, MA 02115, USA. Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA. Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary. Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
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83
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Sharma A, Menche J, Huang CC, Ort T, Zhou X, Kitsak M, Sahni N, Thibault D, Voung L, Guo F, Ghiassian SD, Gulbahce N, Baribaud F, Tocker J, Dobrin R, Barnathan E, Liu H, Panettieri RA, Tantisira KG, Qiu W, Raby BA, Silverman EK, Vidal M, Weiss ST, Barabási AL. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum Mol Genet 2015; 24:3005-20. [PMID: 25586491 DOI: 10.1093/hmg/ddv001] [Citation(s) in RCA: 120] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 01/05/2015] [Indexed: 01/24/2023] Open
Abstract
Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.
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Affiliation(s)
- Amitabh Sharma
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jörg Menche
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Theoretical Physics, Budapest University of Technology and Economics, H1111, Budapest, Hungary Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
| | - C Chris Huang
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Tatiana Ort
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Maksim Kitsak
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nidhi Sahni
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Derek Thibault
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Linh Voung
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Feng Guo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Susan Dina Ghiassian
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Natali Gulbahce
- Department of Cellular and Molecular Pharmacology, University of California 1700, 4th Street, Byers Hall 308D, San Francisco, CA 94158, USA
| | - Frédéric Baribaud
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Joel Tocker
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Radu Dobrin
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Elliot Barnathan
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Hao Liu
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Reynold A Panettieri
- Pulmonary Allergy and Critical Care Division, Department of Medicine, University of Pennsylvania, 125 South 31st Street, TRL Suite 1200, Philadelphia, PA 19104, USA
| | - Kelan G Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Weiliang Qiu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin A Raby
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Albert-László Barabási
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Theoretical Physics, Budapest University of Technology and Economics, H1111, Budapest, Hungary Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
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84
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Luo X, You Z, Zhou M, Li S, Leung H, Xia Y, Zhu Q. A highly efficient approach to protein interactome mapping based on collaborative filtering framework. Sci Rep 2015; 5:7702. [PMID: 25572661 PMCID: PMC4287731 DOI: 10.1038/srep07702] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 12/08/2014] [Indexed: 12/17/2022] Open
Abstract
The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.
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Affiliation(s)
- Xin Luo
- X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Zhuhong You
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Mengchu Zhou
- M. Zhou is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Shuai Li
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Hareton Leung
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Yunni Xia
- X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China
| | - Qingsheng Zhu
- X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China
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85
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Chen H, Zhu Z, Zhu Y, Wang J, Mei Y, Cheng Y. Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers. J Cell Mol Med 2015; 19:297-314. [PMID: 25560835 PMCID: PMC4407592 DOI: 10.1111/jcmm.12447] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Accepted: 08/22/2014] [Indexed: 01/06/2023] Open
Abstract
It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein-protein or gene-gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.
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Affiliation(s)
- Hao Chen
- Department of Cardiothoracic Surgery, Tongji Hospital, Tongji University, Shanghai, China
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86
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Abstract
Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware before adopting protein interaction databases in any single-gene or genome-wide analysis.
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87
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Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, Urban A, Hallmayer J, Snyder M. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol Syst Biol 2014; 10:774. [PMID: 25549968 PMCID: PMC4300495 DOI: 10.15252/msb.20145487] [Citation(s) in RCA: 94] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.
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Affiliation(s)
- Jingjing Li
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Minyi Shi
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Zhihai Ma
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Shuchun Zhao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ghia Euskirchen
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer Ziskin
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexander Urban
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Joachim Hallmayer
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Snyder
- Department of Genetics, Stanford Center for Genomics and Personalized Medicine Stanford University School of Medicine, Stanford, CA, USA
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88
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Yong CH, Maruyama O, Wong L. Discovery of small protein complexes from PPI networks with size-specific supervised weighting. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 5:S3. [PMID: 25559663 PMCID: PMC4305982 DOI: 10.1186/1752-0509-8-s5-s3] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The prediction of small complexes (consisting of two or three distinct proteins) is an important and challenging subtask in protein complex prediction from protein-protein interaction (PPI) networks. The prediction of small complexes is especially susceptible to noise (missing or spurious interactions) in the PPI network, while smaller groups of proteins are likelier to take on topological characteristics of real complexes by chance. We propose a two-stage approach, SSS and Extract, for discovering small complexes. First, the PPI network is weighted by size-specific supervised weighting (SSS), which integrates heterogeneous data and their topological features with an overall topological isolatedness feature. SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex. The second stage, Extract, analyzes the SSS-weighted network to extract putative small complexes and scores them by cohesiveness-weighted density, which incorporates both small-co-complex and large-co-complex weights of edges within and surrounding the complexes. We test our approach on the prediction of yeast and human small complexes, and demonstrate that our approach attains higher precision and recall than some popular complex prediction algorithms. Furthermore, our approach generates a greater number of novel predictions with higher quality in terms of functional coherence.
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89
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Hormozdiari F, Penn O, Borenstein E, Eichler EE. The discovery of integrated gene networks for autism and related disorders. Genome Res 2014; 25:142-54. [PMID: 25378250 PMCID: PMC4317170 DOI: 10.1101/gr.178855.114] [Citation(s) in RCA: 166] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Despite considerable genetic heterogeneity underlying neurodevelopmental diseases, there is compelling evidence that many disease genes will map to a much smaller number of biological subnetworks. We developed a computational method, termed MAGI (merging affected genes into integrated networks), that simultaneously integrates protein–protein interactions and RNA-seq expression profiles during brain development to discover “modules” enriched for de novo mutations in probands. We applied this method to recent exome sequencing of 1116 patients with autism and intellectual disability, discovering two distinct modules that differ in their properties and associated phenotypes. The first module consists of 80 genes associated with Wnt, Notch, SWI/SNF, and NCOR complexes and shows the highest expression early during embryonic development (8–16 post-conception weeks [pcw]). The second module consists of 24 genes associated with synaptic function, including long-term potentiation and calcium signaling with higher levels of postnatal expression. Patients with de novo mutations in these modules are more significantly intellectually impaired and carry more severe missense mutations when compared to probands with de novo mutations outside of these modules. We used our approach to define subsets of the network associated with higher functioning autism as well as greater severity with respect to IQ. Finally, we applied MAGI independently to epilepsy and schizophrenia exome sequencing cohorts and found significant overlap as well as expansion of these modules, suggesting a core set of integrated neurodevelopmental networks common to seemingly diverse human diseases.
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Affiliation(s)
- Fereydoun Hormozdiari
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Osnat Penn
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Elhanan Borenstein
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
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90
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Yang ZH, Yu FY, Lin HF, Wang J. Integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. BMC Med Genomics 2014; 7 Suppl 2:S3. [PMID: 25350598 PMCID: PMC4243118 DOI: 10.1186/1755-8794-7-s2-s3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein-protein interactions (PPIs), making it possible to predict protein complexes from protein-protein interaction networks. On the other hand, the rapidly growing biomedical literature provides a significantly large and readily available source of interaction data, which can be integrated into the protein network for better complex detection performance. METHODS We present an approach of integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. The approach applies a sophisticated natural language processing system, PPIExtractor, to extract PPI data from biomedical literature. These data are then integrated into the PPI datasets for complex detection. RESULTS The experimental results of the state-of-the-art complex detection method, ClusterONE, on five yeast PPI datasets verify our method's effectiveness: compared with the original PPI datasets, the average improvements of 3.976 and 5.416 percentage units in the maximum matching ratio (MMR) are achieved on the new networks using the MIPS and SGD gold standards, respectively. In addition, our approach also proves to be effective for three other complex detection algorithms proposed in recent years, i.e. CMC, COACH and RRW. CONCLUSIONS The rapidly growing biomedical literature provides a significantly large, readily available and relatively accurate source of interaction data, which can be integrated into the protein network for better protein complex detection performance.
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Affiliation(s)
- Zhi Hao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Feng Ying Yu
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Hong Fei Lin
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Jian Wang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, China
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91
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A million peptide motifs for the molecular biologist. Mol Cell 2014; 55:161-9. [PMID: 25038412 DOI: 10.1016/j.molcel.2014.05.032] [Citation(s) in RCA: 359] [Impact Index Per Article: 35.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Revised: 04/24/2014] [Accepted: 05/15/2014] [Indexed: 11/22/2022]
Abstract
A molecular description of functional modules in the cell is the focus of many high-throughput studies in the postgenomic era. A large portion of biomolecular interactions in virtually all cellular processes is mediated by compact interaction modules, referred to as peptide motifs. Such motifs are typically less than ten residues in length, occur within intrinsically disordered regions, and are recognized and/or posttranslationally modified by structured domains of the interacting partner. In this review, we suggest that there might be over a million instances of peptide motifs in the human proteome. While this staggering number suggests that peptide motifs are numerous and the most understudied functional module in the cell, it also holds great opportunities for new discoveries.
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92
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Enhancing the functional content of eukaryotic protein interaction networks. PLoS One 2014; 9:e109130. [PMID: 25275489 PMCID: PMC4183583 DOI: 10.1371/journal.pone.0109130] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2013] [Accepted: 09/08/2014] [Indexed: 12/26/2022] Open
Abstract
Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.
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93
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Hopf TA, Schärfe CPI, Rodrigues JPGLM, Green AG, Kohlbacher O, Sander C, Bonvin AMJJ, Marks DS. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 2014; 3. [PMID: 25255213 PMCID: PMC4360534 DOI: 10.7554/elife.03430] [Citation(s) in RCA: 332] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 09/23/2014] [Indexed: 12/24/2022] Open
Abstract
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.
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Affiliation(s)
- Thomas A Hopf
- Department of Systems Biology, Harvard University, Boston, United States
| | | | - João P G L M Rodrigues
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Anna G Green
- Department of Systems Biology, Harvard University, Boston, United States
| | - Oliver Kohlbacher
- Applied Bioinformatics, Quantitative Biology Center, University of Tübingen, Tübingen, Germany
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands
| | - Debora S Marks
- Department of Systems Biology, Harvard University, Boston, United States
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94
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Andreani J, Guerois R. Evolution of protein interactions: From interactomes to interfaces. Arch Biochem Biophys 2014; 554:65-75. [DOI: 10.1016/j.abb.2014.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 04/28/2014] [Accepted: 05/12/2014] [Indexed: 12/16/2022]
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95
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Hart T, Brown KR, Sircoulomb F, Rottapel R, Moffat J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol Syst Biol 2014; 10:733. [PMID: 24987113 PMCID: PMC4299491 DOI: 10.15252/msb.20145216] [Citation(s) in RCA: 218] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Technological advancement has opened the door to systematic genetics in mammalian cells.
Genome-scale loss-of-function screens can assay fitness defects induced by partial gene knockdown,
using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can
reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to
cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for
the development of specific anticancer therapies. Unfortunately, progress in this field has been
hampered by off-target effects of perturbation reagents and poorly quantified error rates in
large-scale screens. To improve the quality of information derived from these screens, and to
provide a framework for understanding the capabilities and limitations of CRISPR technology, we
derive gold-standard reference sets of essential and nonessential genes, and provide a Bayesian
classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens.
Our results indicate that CRISPR technology is more sensitive than RNAi and that both techniques
have nontrivial false discovery rates that can be mitigated by rigorous analytical methods.
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Affiliation(s)
- Traver Hart
- Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada
| | - Kevin R Brown
- Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada
| | - Fabrice Sircoulomb
- Campbell Family Cancer Research Institute, Ontario Cancer Institute, Princess Margaret Hospital University Health Network, Toronto, ON, Canada
| | - Robert Rottapel
- Campbell Family Cancer Research Institute, Ontario Cancer Institute, Princess Margaret Hospital University Health Network, Toronto, ON, Canada Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada Division of Rheumatology, Department of Medicine, St. Michael's Hospital, Toronto, ON, Canada
| | - Jason Moffat
- Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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96
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Exploring function prediction in protein interaction networks via clustering methods. PLoS One 2014; 9:e99755. [PMID: 24972109 PMCID: PMC4074043 DOI: 10.1371/journal.pone.0099755] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2014] [Accepted: 05/17/2014] [Indexed: 01/06/2023] Open
Abstract
Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
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97
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Rhee SY, Mutwil M. Towards revealing the functions of all genes in plants. TRENDS IN PLANT SCIENCE 2014; 19:212-21. [PMID: 24231067 DOI: 10.1016/j.tplants.2013.10.006] [Citation(s) in RCA: 146] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Revised: 10/10/2013] [Accepted: 10/16/2013] [Indexed: 05/19/2023]
Abstract
The great recent progress made in identifying the molecular parts lists of organisms revealed the paucity of our understanding of what most of the parts do. In this review, we introduce computational and statistical approaches and omics data used for inferring gene function in plants, with an emphasis on network-based inference. We also discuss caveats associated with network-based function predictions such as performance assessment, annotation propagation, the guilt-by-association concept, and the meaning of hubs. Finally, we note the current limitations and possible future directions such as the need for gold standard data from several species, unified access to data and tools, quantitative comparison of data and tool quality, and high-throughput experimental validation platforms for systematic gene function elucidation in plants.
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Affiliation(s)
- Seung Yon Rhee
- Carnegie Institution for Science, Department of Plant Biology, 260 Panama St, Stanford, CA 94305, USA.
| | - Marek Mutwil
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany.
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98
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Talwar P, Silla Y, Grover S, Gupta M, Agarwal R, Kushwaha S, Kukreti R. Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease. BMC Genomics 2014; 15:199. [PMID: 24628925 PMCID: PMC4028079 DOI: 10.1186/1471-2164-15-199] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 02/21/2014] [Indexed: 01/28/2023] Open
Abstract
Background Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. Results Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. Conclusions With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | | | | | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Mall Road, Delhi 110 007, India.
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99
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Tran NH, Choi KP, Zhang L. Counting motifs in the human interactome. Nat Commun 2014; 4:2241. [PMID: 23917172 PMCID: PMC3741638 DOI: 10.1038/ncomms3241] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Accepted: 07/04/2013] [Indexed: 01/05/2023] Open
Abstract
Small over-represented motifs in biological networks often form essential functional units of biological processes. A natural question is to gauge whether a motif occurs abundantly or rarely in a biological network. Here we develop an accurate method to estimate the occurrences of a motif in the entire network from noisy and incomplete data, and apply it to eukaryotic interactomes and cell-specific transcription factor regulatory networks. The number of triangles in the human interactome is about 194 times that in the Saccharomyces cerevisiae interactome. A strong positive linear correlation exists between the numbers of occurrences of triad and quadriad motifs in human cell-specific transcription factor regulatory networks. Our findings show that the proposed method is general and powerful for counting motifs and can be applied to any network regardless of its topological structure. Biological network data are often incomplete, which makes it difficult to determine interaction motifs within such data sets. Here Tran et al. present a new method to count motif numbers in large networks from noisy and incomplete biological data.
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Affiliation(s)
- Ngoc Hieu Tran
- Department of Statistics and Applied Probability, National University of Singapore NUS, Singapore 117546, Singapore
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100
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Heisler J, Elvir L, Barnouti F, Charles E, Wolkow TD, Pyati R. Morphological Effects of Natural Products on Schizosaccharomyces pombe Measured by Imaging Flow Cytometry. NATURAL PRODUCTS AND BIOPROSPECTING 2014; 4:27-35. [PMID: 24660134 PMCID: PMC3956978 DOI: 10.1007/s13659-014-0004-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 01/14/2014] [Indexed: 05/11/2023]
Abstract
ABSTRACT Gaining a full understanding of the mechanisms of action of natural products as therapeutic agents includes observing the effects of natural products on cellular morphology, because abnormal cellular morphology is an important aspect of cellular transformations that occur as part of disease states. In this study a set of natural products was examined in search of small molecules that influence the cylindrical morphology of fission yeast Schizosaccharomyces pombe. Imaging flow cytometry of large populations of S. pombe exposed to natural products captured cell images and revealed changes in mean length and aspect ratio of cells. Several natural products were found to alter S. pombe's morphology relative to control, in terms of elongating cells, shrinking them, or making them more round. These results may facilitate future investigations into methods by which cells establish and maintain specific shapes. GRAPHICAL ABSTRACT Gaining a full understanding of the mechanisms of action of natural products as therapeutic agents includes observing the effects of natural products on cellular morphology, because abnormal cellular morphology is an important aspect of cellular transformations that occur as part of disease states. In this study a set of natural products was examined in search of small molecules that influence the cylindrical morphology of fission yeast Schizosaccharomyces pombe. Imaging flow cytometry of large populations of S. pombe exposed to natural products captured cell images and revealed changes in mean length and aspect ratio of cells. Several natural products were found to alter S. pombe's morphology relative to control, in terms of elongating cells, shrinking them, or making them more round. These results may facilitate future investigations into methods by which cells establish and maintain specific shapes.
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Affiliation(s)
- Joel Heisler
- University of North Florida, Jacksonville, FL USA
| | | | | | | | - Tom D. Wolkow
- University of Colorado at Colorado Springs, Colorado Springs, CO USA
| | - Radha Pyati
- University of North Florida, Jacksonville, FL USA
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