501
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Barah P, Jayavelu ND, Mundy J, Bones AM. Genome scale transcriptional response diversity among ten ecotypes of Arabidopsis thaliana during heat stress. FRONTIERS IN PLANT SCIENCE 2013; 4:532. [PMID: 24409190 PMCID: PMC3872818 DOI: 10.3389/fpls.2013.00532] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Accepted: 12/10/2013] [Indexed: 05/08/2023]
Abstract
In the scenario of global warming and climate change, heat stress is a serious threat to crop production worldwide. Being sessile, plants cannot escape from heat. Plants have developed various adaptive mechanisms to survive heat stress. Several studies have focused on diversity of heat tolerance levels in divergent Arabidopsis thaliana (A. thaliana) ecotypes, but comprehensive genome scale understanding of heat stress response in plants is still lacking. Here we report the genome scale transcript responses to heat stress of 10 A. thaliana ecotypes (Col, Ler, C24, Cvi, Kas1, An1, Sha, Kyo2, Eri, and Kond) originated from different geographical locations. During the experiment, A. thaliana plants were subjected to heat stress (38°C) and transcript responses were monitored using Arabidopsis NimbleGen ATH6 microarrays. The responses of A. thaliana ecotypes exhibited considerable variation in the transcript abundance levels. In total, 3644 transcripts were significantly heat regulated (p < 0.01) in the 10 ecotypes, including 244 transcription factors and 203 transposable elements. By employing a systems genetics approach- Network Component Analysis (NCA), we have constructed an in silico transcript regulatory network model for 35 heat responsive transcription factors during cellular responses to heat stress in A. thaliana. The computed activities of the 35 transcription factors showed ecotype specific responses to the heat treatment.
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Affiliation(s)
- Pankaj Barah
- Cell Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and TechnologyTrondheim, Norway
| | - Naresh D. Jayavelu
- Department of Chemical Engineering, Norwegian University of Science and TechnologyTrondheim, Norway
| | - John Mundy
- Department of Biology, University of CopenhagenCopenhagen, Denmark
| | - Atle M. Bones
- Cell Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and TechnologyTrondheim, Norway
- *Correspondence: Atle M. Bones, Cell Molecular Biology and Genomics Group, Department of Biology, Norwegian University of Science and Technology, Hoegskoleringen 5, N-7491 Trondheim, Norway e-mail:
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502
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Zybailov BL, Glazko GV, Jaiswal M, Raney KD. Large Scale Chemical Cross-linking Mass Spectrometry Perspectives. ACTA ACUST UNITED AC 2013; 6:001. [PMID: 25045217 PMCID: PMC4101816 DOI: 10.4172/jpb.s2-001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The spectacular heterogeneity of a complex protein mixture from biological samples becomes even more difficult to tackle when one’s attention is shifted towards different protein complex topologies, transient interactions, or localization of PPIs. Meticulous protein-by-protein affinity pull-downs and yeast-two-hybrid screens are the two approaches currently used to decipher proteome-wide interaction networks. Another method is to employ chemical cross-linking, which gives not only identities of interactors, but could also provide information on the sites of interactions and interaction interfaces. Despite significant advances in mass spectrometry instrumentation over the last decade, mapping Protein-Protein Interactions (PPIs) using chemical cross-linking remains time consuming and requires substantial expertise, even in the simplest of systems. While robust methodologies and software exist for the analysis of binary PPIs and also for the single protein structure refinement using cross-linking-derived constraints, undertaking a proteome-wide cross-linking study is highly complex. Difficulties include i) identifying cross-linkers of the right length and selectivity that could capture interactions of interest; ii) enrichment of the cross-linked species; iii) identification and validation of the cross-linked peptides and cross-linked sites. In this review we examine existing literature aimed at the large-scale protein cross-linking and discuss possible paths for improvement. We also discuss short-length cross-linkers of broad specificity such as formaldehyde and diazirine-based photo-cross-linkers. These cross-linkers could potentially capture many types of interactions, without strict requirement for a particular amino-acid to be present at a given protein-protein interface. How these shortlength, broad specificity cross-linkers be applied to proteome-wide studies? We will suggest specific advances in methodology, instrumentation and software that are needed to make such a leap.
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Affiliation(s)
- Boris L Zybailov
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Galina V Glazko
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Mihir Jaiswal
- UALR/UAMS Joint Bioinformatics Program, University of Arkansas Little Rock, Little Rock, AR, USA
| | - Kevin D Raney
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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503
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Sridharan GV, Yi M, Hassoun S, Lee K. Metabolic flux-based modularity using shortest retroactive distances. BMC SYSTEMS BIOLOGY 2012; 6:155. [PMID: 23270532 PMCID: PMC3556310 DOI: 10.1186/1752-0509-6-155] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2012] [Accepted: 11/27/2012] [Indexed: 01/07/2023]
Abstract
Background Graph-based modularity analysis has emerged as an important tool to study the functional organization of biological networks. However, few methods are available to study state-dependent changes in network modularity using biological activity data. We develop a weighting scheme, based on metabolic flux data, to adjust the interaction distances in a reaction-centric graph model of a metabolic network. The weighting scheme was combined with a hierarchical module assignment algorithm featuring the preservation of metabolic cycles to examine the effects of cellular differentiation and enzyme inhibitions on the functional organization of adipocyte metabolism. Results Our analysis found that the differences between various metabolic states primarily involved the assignment of two specific reactions in fatty acid synthesis and glycerogenesis. Our analysis also identified cyclical interactions between reactions that are robust with respect to metabolic state, suggesting possible co-regulation. Comparisons based on cyclical interaction distances between reaction pairs suggest that the modular organization of adipocyte metabolism is stable with respect to the inhibition of an enzyme, whereas a major physiological change such as cellular differentiation leads to a more substantial reorganization. Conclusion Taken together, our results support the notion that network modularity is influenced by both the connectivity of the network’s components as well as the relative engagements of the connections.
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Affiliation(s)
- Gautham Vivek Sridharan
- Department of Chemical and Biological Engineering, Tufts University, 4 Colby Street, Room 150, Medford, MA 02155, USA
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504
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Fukushima A. DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 2012; 518:209-14. [PMID: 23246976 DOI: 10.1016/j.gene.2012.11.028] [Citation(s) in RCA: 113] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2012] [Accepted: 11/27/2012] [Indexed: 10/27/2022]
Abstract
Large-scale "omics" data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable. We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based "eigen-molecules" in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test. We illustrated its utility by demonstrating biologically relevant, differentially correlated molecules in transcriptome coexpression and metabolite-to-metabolite correlation networks. DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates. The package can be downloaded from the following website: http://diffcorr.sourceforge.net/.
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505
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Toubiana D, Fernie AR, Nikoloski Z, Fait A. Network analysis: tackling complex data to study plant metabolism. Trends Biotechnol 2012; 31:29-36. [PMID: 23245943 DOI: 10.1016/j.tibtech.2012.10.011] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2012] [Revised: 10/18/2012] [Accepted: 10/24/2012] [Indexed: 11/18/2022]
Abstract
Incomplete knowledge of biochemical pathways makes the holistic description of plant metabolism a non-trivial undertaking. Sensitive analytical platforms, which are capable of accurately quantifying the levels of the various molecular entities of the cell, can assist in tackling this task. However, the ever-increasing amount of high-throughput data, often from multiple technologies, requires significant computational efforts for integrative analysis. Here we introduce the application of network analysis to study plant metabolism and describe the construction and analysis of correlation-based networks from (time-resolved) metabolomics data. By investigating the interactions between metabolites, network analysis can help to interpret complex datasets through the identification of key network components. The relationship between structural and biological roles of network components can be evaluated and employed to aid metabolic engineering.
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Affiliation(s)
- David Toubiana
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
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506
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Altelaar AFM, Munoz J, Heck AJR. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet 2012. [PMID: 23207911 DOI: 10.1038/nrg3356] [Citation(s) in RCA: 515] [Impact Index Per Article: 42.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.
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Affiliation(s)
- A F Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
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507
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Ku CS, Roukos DH. De novo mutations, protein-protein interactions and functional regulatory networks toward novel diagnostics in autism. Expert Rev Proteomics 2012. [PMID: 23194261 DOI: 10.1586/epr.12.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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508
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Abstract
Current limitation in cancer genomic studies is a lack of the integration of various omics data generated through next generation sequencing technologies, as well as a lack of the sounding and comprehensive epigenomic and genomic information about a particular cancer cell type. In this review, we will discuss main aspects of current genomics research with its application in cancer topics. We will first overview the next-generation sequencing technologies, then outline the major computational approaches, particularly focusing on ChIP-based omics data, and list several remaining open questions facing computational biologists, further present regulatory network analysis inferred from the ChIP-based omics data; finally implicate the clinical outcomes from the network and pathway analysis.
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Affiliation(s)
- Binhua Tang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
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509
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Cho W, Ziogas DE, Katsios C, Roukos DH. Emerging personalized oncology: sequencing and systems strategies. Future Oncol 2012; 8:637-41. [PMID: 22764759 DOI: 10.2217/fon.12.44] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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510
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Roukos DH, Ziogas DE, Papaloukas C, Baltogiannis G. Novel Wnt signaling and other pathway inhibitors in the colorectal cancer genomic landscape era. Future Oncol 2012; 8:1373-6. [PMID: 23148609 DOI: 10.2217/fon.12.140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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511
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Differential network entropy reveals cancer system hallmarks. Sci Rep 2012; 2:802. [PMID: 23150773 PMCID: PMC3496163 DOI: 10.1038/srep00802] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Accepted: 10/12/2012] [Indexed: 01/14/2023] Open
Abstract
The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network we here demonstrate that cancer cells are characterised by an increase in network entropy. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local network entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local correlation patterns. In particular, we find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in network entropy. These findings may have potential implications for identifying novel drug targets.
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512
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Affiliation(s)
- Nagasuma Chandra
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India ,
| | - Jyothi Padiadpu
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India
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513
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Ziogas DE, Baltogiannis G, Spiliotis J, Tzaphlidou M, Roukos DH. Genome-based diagnostics and predictive tools: a new epoch for breast cancer management. Future Oncol 2012; 8:1211-4. [PMID: 23130921 DOI: 10.2217/fon.12.115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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514
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Kiel C, Serrano L. Structural Data in Synthetic Biology Approaches for Studying General Design Principles of Cellular Signaling Networks. Structure 2012; 20:1806-13. [DOI: 10.1016/j.str.2012.10.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2012] [Revised: 10/09/2012] [Accepted: 10/10/2012] [Indexed: 12/13/2022]
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515
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Freschi L, Torres-Quiroz F, Dubé AK, Landry CR. qPCA: a scalable assay to measure the perturbation of protein-protein interactions in living cells. MOLECULAR BIOSYSTEMS 2012; 9:36-43. [PMID: 23099892 DOI: 10.1039/c2mb25265a] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
One of the most important challenges in systems biology is to understand how cells respond to genetic and environmental perturbations. Here we show that the yeast DHFR-PCA, coupled with high-resolution growth profiling (DHFR-qPCA), is a straightforward assay to study the modulation of protein-protein interactions (PPIs) in vivo as a response to genetic, metabolic and drug perturbations. Using the canonical Protein Kinase A (PKA) pathway as a test system, we show that changes in PKA activity can be measured in living cells as a modulation of the interaction between its regulatory (Bcy1) and catalytic (Tpk1 and Tpk2) subunits in response to changes in carbon metabolism, caffeine and methyl methanesulfonate (MMS) treatments and to modifications in the dosage of its enzymatic regulators, the phosphodiesterases. Our results show that the DHFR-qPCA is easily implementable and amenable to high-throughput. The DHFR-qPCA will pave the way to the study of the effects of drug, genetic and environmental perturbations on in vivo PPI networks, thus allowing the exploration of new spaces of the eukaryotic interactome.
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Affiliation(s)
- Luca Freschi
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Canada
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516
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GIST: advances in tyrosine kinase inhibitors enhance laparoscopic resection even in advanced disease. Surg Endosc 2012; 27:1446-7. [PMID: 23093238 DOI: 10.1007/s00464-012-2599-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 09/10/2012] [Indexed: 10/27/2022]
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517
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Pang CNI, Goel A, Li SS, Wilkins MR. A multidimensional matrix for systems biology research and its application to interaction networks. J Proteome Res 2012; 11:5204-20. [PMID: 22979997 DOI: 10.1021/pr300405y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.
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Affiliation(s)
- Chi Nam Ignatius Pang
- Systems Biology Initiative and School of Biotechnology and Biomolecular Sciences, The University of New South Wales, New South Wales, Australia
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518
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Affiliation(s)
- Enrico Capobianco
- Institute of Clinical Physiology (IFC), Laboratory for Integrative Systems Medicine (LISM), National Research Council (CNR) Pisa, Italy ; Center for Computational Science (CCS), University of Miami Miami, FL, USA
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519
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Capobianco E. Dynamic networks in systems medicine. Front Genet 2012; 3:185. [PMID: 23049537 PMCID: PMC3445994 DOI: 10.3389/fgene.2012.00185] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Accepted: 09/01/2012] [Indexed: 12/01/2022] Open
Affiliation(s)
- Enrico Capobianco
- Center for Computational Science, University of Miami Miami, FL, USA ; Laboratory for Integrative Systems Medicine Unit, Institute of Clinical Physiology, National Research Council Pisa, Italy
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520
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Klein C, Marino A, Sagot MF, Vieira Milreu P, Brilli M. Structural and dynamical analysis of biological networks. Brief Funct Genomics 2012; 11:420-33. [PMID: 22908211 DOI: 10.1093/bfgp/els030] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Biological networks are currently being studied with approaches derived from the mathematical and physical sciences. Their structural analysis enables to highlight nodes with special properties that have sometimes been correlated with the biological importance of a gene or a protein. However, biological networks are dynamic both on the evolutionary time-scale, and on the much shorter time-scale of physiological processes. There is therefore no unique network for a given cellular process, but potentially many realizations, each with different properties as a consequence of regulatory mechanisms. Such realizations provide snapshots of a same network in different conditions, enabling the study of condition-dependent structural properties. True dynamical analysis can be obtained through detailed mathematical modeling techniques that are not easily scalable to full network models.
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521
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Komorowsky CV, Brosius FC, Pennathur S, Kretzler M. Perspectives on systems biology applications in diabetic kidney disease. J Cardiovasc Transl Res 2012; 5:491-508. [PMID: 22733404 PMCID: PMC3422674 DOI: 10.1007/s12265-012-9382-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2012] [Accepted: 05/22/2012] [Indexed: 12/18/2022]
Abstract
Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.
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Affiliation(s)
- Claudiu V. Komorowsky
- Department of Internal Medicine, Division of Nephrology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Frank C. Brosius
- Department of Internal Medicine, Division of Nephrology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Subramaniam Pennathur
- Department of Internal Medicine, Division of Nephrology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Matthias Kretzler
- Department of Internal Medicine, Division of Nephrology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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522
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Roukos DH. Dynamics of genome 'iPOP': predicting disease or 'narciss-ome'? Expert Rev Mol Diagn 2012; 12:545-8. [PMID: 22845472 DOI: 10.1586/erm.12.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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523
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Roukos DH. Differential signaling transduction networks for clinical robustness. Expert Rev Proteomics 2012; 9:111-4. [PMID: 22462780 DOI: 10.1586/epr.12.14] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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524
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Integration of biological networks and pathways with genetic association studies. Hum Genet 2012; 131:1677-86. [PMID: 22777728 DOI: 10.1007/s00439-012-1198-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Accepted: 06/27/2012] [Indexed: 12/13/2022]
Abstract
Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.
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525
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Michaut M, Bader GD. Multiple genetic interaction experiments provide complementary information useful for gene function prediction. PLoS Comput Biol 2012; 8:e1002559. [PMID: 22737063 PMCID: PMC3380825 DOI: 10.1371/journal.pcbi.1002559] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Accepted: 05/01/2012] [Indexed: 11/19/2022] Open
Abstract
Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.
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Affiliation(s)
- Magali Michaut
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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526
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Kamburov A, Stelzl U, Herwig R. IntScore: a web tool for confidence scoring of biological interactions. Nucleic Acids Res 2012; 40:W140-6. [PMID: 22649056 PMCID: PMC3394291 DOI: 10.1093/nar/gks492] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Knowledge of all molecular interactions that potentially take place in the cell is a key for a detailed understanding of cellular processes. Currently available interaction data, such as protein–protein interaction maps, are known to contain false positives that inevitably diminish the accuracy of network-based inferences. Interaction confidence scoring is thus a crucial intermediate step after obtaining interaction data and before using it in an interaction network-based inference approach. It enables to weight individual interactions according to the likelihood that they actually take place in the cell, and can be used to filter out false positives. We describe a web tool called IntScore which calculates confidence scores for user-specified sets of interactions. IntScore provides six network topology- and annotation-based confidence scoring methods. It also enables the integration of scores calculated by the different methods into an aggregate score using machine learning approaches. IntScore is user-friendly and extensively documented. It is freely available at http://intscore.molgen.mpg.de.
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Affiliation(s)
- Atanas Kamburov
- Vertebrate Genomics Department and Otto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany.
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527
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Hasan S, Bonde BK, Buchan NS, Hall MD. Network analysis has diverse roles in drug discovery. Drug Discov Today 2012; 17:869-74. [PMID: 22627007 DOI: 10.1016/j.drudis.2012.05.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2011] [Revised: 04/04/2012] [Accepted: 05/11/2012] [Indexed: 01/08/2023]
Abstract
Computational biologists use network analysis to uncover relationships between various data types of interest for drug discovery. For example, signalling and metabolic pathways are commonly used to understand disease states and drug mechanisms. However, several other flavours of network analysis techniques are also applicable in a drug discovery context. Recent advances include networks that encompass relationships between diseases, molecular mechanisms and gene targets. Even social networks that mirror interactions within the scientific community are helping to foster collaborations and novel research. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges.
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Affiliation(s)
- Samiul Hasan
- GlaxoSmithKline, Computational Biology, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK.
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528
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Koh GCKW, Porras P, Aranda B, Hermjakob H, Orchard SE. Analyzing protein-protein interaction networks. J Proteome Res 2012; 11:2014-31. [PMID: 22385417 DOI: 10.1021/pr201211w] [Citation(s) in RCA: 116] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.
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Affiliation(s)
- Gavin C K W Koh
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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