2751
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Abstract
Cells respond to external stimuli by transducing signals through a series of intracellular molecules and eliciting an appropriate response. The cascade of events through which the signals are transduced include post-translational modifications such as phosphorylation and ubiquitylation in addition to formation of multi-protein complexes. Improvements in biological mass spectrometry and protein/peptide microarray technology have tremendously improved our ability to probe proteins, protein complexes, and signaling pathways in a high-throughput fashion. Today, a single mass spectrometry-based investigation of a signaling pathway has the potential to uncover the large majority of known signaling intermediates painstakingly characterized over decades in addition to discovering a number of novel ones. Here, we discuss various proteomic strategies to characterize signaling pathways and provide protocols for phosphoproteomic analysis.
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Affiliation(s)
- H C Harsha
- Institute of Bioinformatics, International Technology Park, Bangalore, India
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2752
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Mosca R, Céol A, Aloy P. Interactome3D: adding structural details to protein networks. Nat Methods 2013; 10:47-53. [DOI: 10.1038/nmeth.2289] [Citation(s) in RCA: 339] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 10/30/2012] [Indexed: 01/13/2023]
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2753
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Abstract
Most biological processes including growth, proliferation, differentiation, and apoptosis are coordinated by tightly regulated signaling pathways, which also involve secreted proteins acting in an autocrine and/or paracrine manner. In addition, extracellular signaling molecules affect local niche biology and influence the cross-talking with the surrounding tissues. The understanding of this molecular language may provide an integrated and broader view of cellular regulatory networks under physiological and pathological conditions. In this context, the profiling at a global level of cell secretomes (i.e., the subpopulations of a proteome secreted from the cell) has become an active area of research. The current interest in secretome research also deals with its high potential for the biomarker discovery and the identification of new targets for therapeutic strategies. Several proteomic and mass spectrometry platforms and methodologies have been applied to secretome profiling of conditioned media of cultured cell lines and primary cells. Nevertheless, the analysis of secreted proteins is still a very challenging task, because of the technical difficulties that may hamper the subsequent mass spectrometry analysis. This chapter describes a typical workflow for the analysis of proteins secreted by cultured cells. Crucial issues related to cell culture conditions for the collection of conditioned media, secretome preparation, and mass spectrometry analysis are discussed. Furthermore, an overview of quantitative LC-MS-based approaches, computational tools for data analysis, and strategies for validation of potential secretome biomarkers is also presented.
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2754
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Durmuş Tekir SD, Ülgen KÖ. Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era. Biotechnol J 2013; 8:85-96. [PMID: 23193100 PMCID: PMC7161785 DOI: 10.1002/biot.201200110] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Revised: 09/17/2012] [Accepted: 10/11/2012] [Indexed: 12/13/2022]
Abstract
Infectious diseases comprise some of the leading causes of death and disability worldwide. Interactions between pathogen and host proteins underlie the process of infection. Improved understanding of pathogen-host molecular interactions will increase our knowledge of the mechanisms involved in infection, and allow novel therapeutic solutions to be devised. Complete genome sequences for a number of pathogenic microorganisms, as well as the human host, has led to the revelation of their protein-protein interaction (PPI) networks. In this post-genomic era, pathogen-host interactions (PHIs) operating during infection can also be mapped. Detailed systematic analyses of PPI and PHI data together are required for a complete understanding of pathogenesis of infections. Here we review the striking results recently obtained during the construction and investigation of these networks. Emphasis is placed on studies producing large-scale interaction data by high-throughput experimental techniques.
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Affiliation(s)
| | - Kutlu Ö. Ülgen
- Department of Chemical Engineering, Boǧaziçi University, Istanbul, Turkey
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2755
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Vera J, Lai X, Schmitz U, Wolkenhauer O. MicroRNA-Regulated Networks: The Perfect Storm for Classical Molecular Biology, the Ideal Scenario for Systems Biology. Advances in Experimental Medicine and Biology 2013. [DOI: 10.1007/978-94-007-5590-1_4] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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2756
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Laenen G, Thorrez L, Börnigen D, Moreau Y. Finding the targets of a drug by integration of gene expression data with a protein interaction network. Mol BioSyst 2013; 9:1676-85. [DOI: 10.1039/c3mb25438k] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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2757
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Lapolla A, Porcu S, Roverso M, Desoye G, Cosma C, Nardelli GB, Bogana G, Carrozzini M, Traldi P. A preliminary investigation on placenta protein profile reveals only modest changes in well controlled gestational diabetes mellitus. Eur J Mass Spectrom (Chichester) 2013; 19:211-223. [PMID: 24308201 DOI: 10.1255/ejms.1225] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Gestational diabetes mellitus (GDM) is associated with a wide range of tissue-specific changes depending on the quality of glycemic control of the mothers. Here we tested the hypothesis that GDM is associated with alterations in the human term placenta proteome. For this aim, two different approacheswere employed. The placenta homogenates from 20 healthy subjects and those from 20 GDM pregnant women were pooled. The two samples thus obtained were analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and the proteins detected were tentatively identified by comparison of their molecular weight with the Human Protein Reference Database, restricting the search to the species expressed in the placenta tissue. However this approach led to misleading results: in fact, an in deep analysis of the spectra and tandem mass spectrometry (MS/MS) measurements of the digestion products from the protein detected, unequivocally proved that the species observed are maternal and fetal globins. Consequently, the two pools were analyzed by 1D sodium dodecyl sulphate polyacrylamide gel electrophoresis; the different bands obtained were digested by trypsin and the digestion products were analyzed by MALDI-MS; the protein identification was carried out by comparison of the peptide mass fingerprint with databases. Only modest quantitative differences were observed between the placenta protein profiles of healthy and GDM subjects, indicating that GDM, if well controlled, induces only minor changes in the placental proteome. One example of differently expressed proteins in the placenta homogenate pool from GDM and the controls was the SRRM1 protein, a member of the serine-arginine protein kinase family; for GDM samples, the MALDI spectrum of its digestion products showed the presence of molecular species attributable to glycation and glyco-oxidation processes.
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2758
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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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2759
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Kayhan FE, Koldemir M, Cagatay P, Ciftci C, Susleyici-Duman B. Prevalence of endothelial nitric oxide synthase E298D polymorphism in Turkish patients with essential hypertension. Diabetes Metab Syndr 2013; 7:12-16. [PMID: 23517789 DOI: 10.1016/j.dsx.2013.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AIMS Our aim was to evaluate the effects of endothelial nitric oxide synthase (eNOS) E298D polymorphism in obesity variables and essential hypertension (eHT) development risk. The genotype frequencies of E298D polymorphism in eHT patients and non-hypertensive (non-HT) controls (proven to have normal coronaries angiographically) were analyzed for their association with demographic and obesity related data of the eHT patients and controls. MATERIALS AND METHODS eNOS gene E298D genotypes were determined with qPCR. RESULTS The eNOS E298D polymorphism frequencies for 298E/E, 298E/D and 298D/D genotypes were respectively as 41.1%, 44.6%, 14.3% in subjects eHT and 52.8%, 38.9%, 8.3% in the non-HT groups. The combined E298D homozygous polymorphic and heterozygous genotypes were found to have a decreasing effect on serum total-cholesterol levels in comparison to wild-type genotypes in eHT patients but not controls. CONCLUSIONS Our results support the idea that, the eNOS E298D polymorphism, which is not associated with hypertension, may increase the risk of hypertension when associated with high serum total-cholesterol levels.
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2760
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Distler M, Pilarsky E, Kersting S, Grützmann R. Preoperative CEA and CA 19-9 are prognostic markers for survival after curative resection for ductal adenocarcinoma of the pancreas - a retrospective tumor marker prognostic study. Int J Surg 2013; 11:1067-72. [PMID: 24161419 DOI: 10.1016/j.ijsu.2013.10.005] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2013] [Revised: 09/30/2013] [Accepted: 10/11/2013] [Indexed: 12/30/2022]
Abstract
BACKGROUND The prognosis for patients with ductal adenocarcinoma of the pancreas (PDAC) remains poor even after curative resection. Carbohydrate antigen 19-9 (CA 19-9) and the carcinoembryonic antigen (CEA) are the most widely used serum-based tumor markers for the diagnosis and follow up of pancreatic cancer. In our analysis we aim to assess the prognostic value of a combination of both tumor markers in patients with pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS Between 01/1995 and 08/2012 we performed a total of 264 pancreatic resections due to PDAC. Patients were stratified into 3 groups in regard to their preoperative tumor marker levels. Survival was compared between the groups using Kaplan Meier analysis and log rank test. Univariate subgroup analysis and multivariate analysis were performed. RESULTS For 259 cases complete follow up could be obtained. In patients with low preoperative CEA and CA 19-9 levels (group 1 n = 91) the mean survival was 33.3 month (CI 95% 25.1-41.5). If one of the analyzed tumor markers (CEA/CA19-9) was preoperatively elevated above the cut-off level (group 2 n = 106) mean survival was 28.5 month (CI 95% 22.1-35.1). 62 patients showed preoperative elevation of both, CEA and CA 19-9 (group 3); mean survival in this group was 23.9 month (CI 95% 13.9-33.9), p > 0.01. Multivariate analysis confirmed preoperative CEA/CA 19-9 level as independent prognostic factor (HR 1.299). CONCLUSION Preoperative CEA and CA 19-9 levels correlate with patient prognosis after curative pancreatic resection due to PDAC. This is especially true for the most frequently pT 3/4 stages of PDAC. Even if CEA and CA 19-9 might not be appropriate for screening, its serum levels should therefore be determined prior to operation and taken into account when resectability or operability is doubtful.
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Affiliation(s)
- Marius Distler
- Department of General, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, TU Dresden, Germany.
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2761
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Reimand J, Bader GD. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol 2013; 9:637. [PMID: 23340843 PMCID: PMC3564258 DOI: 10.1038/msb.2012.68] [Citation(s) in RCA: 192] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2012] [Accepted: 12/06/2012] [Indexed: 12/20/2022] Open
Abstract
Large-scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single-nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer-related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation-associated SNVs (pSNVs), phospho-mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR, and an immune-related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates (FLNB, GRM1, POU2F1), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research.
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Affiliation(s)
- Jüri Reimand
- The Donnelly Centre, University of Toronto, Toronto, Canada
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Canada
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2762
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Villar EL, Cho WC. Proteomics and Cancer Research. In: Wang X, editor. Bioinformatics of Human Proteomics. Dordrecht: Springer Netherlands; 2013. pp. 75-101. [DOI: 10.1007/978-94-007-5811-7_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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2763
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Abstract
This community page describes the database and associated Web application that comprise the Allen Human Brain Atlas, an open online resource that integrates genomic and anatomic human brain data.
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2764
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Abstract
Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.
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Affiliation(s)
- Mileidy W. Gonzalez
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Maricel G. Kann
- Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland, United States of America
- * E-mail:
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2765
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Vanburen V, Chen H. Managing biological complexity across orthologs with a visual knowledgebase of documented biomolecular interactions. Sci Rep 2012; 2:1011. [PMID: 23264875 PMCID: PMC3527363 DOI: 10.1038/srep01011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Accepted: 12/04/2012] [Indexed: 01/11/2023] Open
Abstract
The complexity of biomolecular interactions and influences is a major obstacle to their comprehension and elucidation. Visualizing knowledge of biomolecular interactions increases comprehension and facilitates the development of new hypotheses. The rapidly changing landscape of high-content experimental results also presents a challenge for the maintenance of comprehensive knowledgebases. Distributing the responsibility for maintenance of a knowledgebase to a community of subject matter experts is an effective strategy for large, complex and rapidly changing knowledgebases. Cognoscente serves these needs by building visualizations for queries of biomolecular interactions on demand, by managing the complexity of those visualizations, and by crowdsourcing to promote the incorporation of current knowledge from the literature.
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Affiliation(s)
- Vincent Vanburen
- Systems Biology and Translational Medicine, Texas A&M HSC College of Medicine, TX, USA.
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2766
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Klenke C, Janowski S, Borck D, Widera D, Ebmeyer J, Kalinowski J, Leichtle A, Hofestädt R, Upile T, Kaltschmidt C, Kaltschmidt B, Sudhoff H. Identification of novel cholesteatoma-related gene expression signatures using full-genome microarrays. PLoS One 2012; 7:e52718. [PMID: 23285167 PMCID: PMC3527606 DOI: 10.1371/journal.pone.0052718] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2012] [Accepted: 11/20/2012] [Indexed: 01/30/2023] Open
Abstract
Background Cholesteatoma is a gradually expanding destructive epithelial lesion within the middle ear. It can cause extensive local tissue destruction in the temporal bone and can initially lead to the development of conductive hearing loss via ossicular erosion. As the disease progresses, sensorineural hearing loss, vertigo or facial palsy may occur. Cholesteatoma may promote the spread of infection through the tegmen of the middle ear and cause meningitis or intracranial infections with abscess formation. It must, therefore, be considered as a potentially life-threatening middle ear disease. Methods and Findings In this study, we investigated differentially expressed genes in human cholesteatomas in comparison to regular auditory canal skin using Whole Human Genome Microarrays containing 19,596 human genes. In addition to already described up-regulated mRNAs in cholesteatoma, such as MMP9, DEFB2 and KRT19, we identified 3558 new cholesteatoma-related transcripts. 811 genes appear to be significantly differentially up-regulated in cholesteatoma. 334 genes were down-regulated more than 2-fold. Significantly regulated genes with protein metabolism activity include matrix metalloproteinases as well as PI3, SERPINB3 and SERPINB4. Genes like SPP1, KRT6B, PRPH, SPRR1B and LAMC2 are known as genes with cell growth and/or maintenance activity. Transport activity genes and signal transduction genes are LCN2, GJB2 and CEACAM6. Three cell communication genes were identified; one CDH19 and two from the S100 family. Conclusions This study demonstrates that the expression profile of cholesteatoma is similar to a metastatic tumour and chronically inflamed tissue. Based on the investigated profiles we present novel protein-protein interaction and signal transduction networks, which include cholesteatoma-regulated transcripts and may be of great value for drug targeting and therapy development.
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Affiliation(s)
- Christin Klenke
- Department of Otolaryngology and Head and Neck Surgery, Klinikum Bielefeld, Bielefeld, Germany.
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2767
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Abstract
Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.
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2768
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Kalra H, Simpson RJ, Ji H, Aikawa E, Altevogt P, Askenase P, Bond VC, Borràs FE, Breakefield X, Budnik V, Buzas E, Camussi G, Clayton A, Cocucci E, Falcon-Perez JM, Gabrielsson S, Gho YS, Gupta D, Harsha HC, Hendrix A, Hill AF, Inal JM, Jenster G, Krämer-Albers EM, Lim SK, Llorente A, Lötvall J, Marcilla A, Mincheva-Nilsson L, Nazarenko I, Nieuwland R, Nolte-'t Hoen ENM, Pandey A, Patel T, Piper MG, Pluchino S, Prasad TSK, Rajendran L, Raposo G, Record M, Reid GE, Sánchez-Madrid F, Schiffelers RM, Siljander P, Stensballe A, Stoorvogel W, Taylor D, Thery C, Valadi H, van Balkom BWM, Vázquez J, Vidal M, Wauben MHM, Yáñez-Mó M, Zoeller M, Mathivanan S. Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation. PLoS Biol 2012; 10:e1001450. [PMID: 23271954 PMCID: PMC3525526 DOI: 10.1371/journal.pbio.1001450] [Citation(s) in RCA: 915] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Extracellular vesicles (EVs) are membraneous vesicles released by a variety of cells into their microenvironment. Recent studies have elucidated the role of EVs in intercellular communication, pathogenesis, drug, vaccine and gene-vector delivery, and as possible reservoirs of biomarkers. These findings have generated immense interest, along with an exponential increase in molecular data pertaining to EVs. Here, we describe Vesiclepedia, a manually curated compendium of molecular data (lipid, RNA, and protein) identified in different classes of EVs from more than 300 independent studies published over the past several years. Even though databases are indispensable resources for the scientific community, recent studies have shown that more than 50% of the databases are not regularly updated. In addition, more than 20% of the database links are inactive. To prevent such database and link decay, we have initiated a continuous community annotation project with the active involvement of EV researchers. The EV research community can set a gold standard in data sharing with Vesiclepedia, which could evolve as a primary resource for the field.
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Affiliation(s)
- Hina Kalra
- Department of Biochemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia
| | - Richard J. Simpson
- Department of Biochemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia
| | - Hong Ji
- Department of Biochemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia
| | - Elena Aikawa
- Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Peter Altevogt
- Tumor Immunology Programme, German Cancer Research Center, Heidelberg, Germany
| | - Philip Askenase
- Department of Medicine, Yale Medical School, New Haven, Connecticut, United States of America
| | - Vincent C. Bond
- Department of Microbiology, Biochemistry, and Immunology, Morehouse School of Medicine, Atlanta, Georgia, United States of America
| | - Francesc E. Borràs
- IVECAT, LIRAD-BST, Institut d'Investigació Germans Trias i Pujol, Dept de Biologia Cellular, Fisiologia i Immunologia, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Xandra Breakefield
- Department of Neurology, Massachusetts General Hospital, and Neuroscience Program, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Vivian Budnik
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Edit Buzas
- Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Giovanni Camussi
- Department of Internal Medicine, Centre for Molecular Biotechnology and Centre for Research in Experimental Medicine, Torino, Italy
| | - Aled Clayton
- Institute of Cancer & Genetics, School of Medicine, Cardiff University, Velindre Cancer Centre, Whitchurch, Cardiff, United Kingdom
| | - Emanuele Cocucci
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- Immune Disease Institute and Program in Cellular and Molecular Medicine at Boston Children's Hospital, Boston, Massachusetts, United States of America
| | - Juan M. Falcon-Perez
- Metabolomics Unit, CIC bioGUNE, CIBERehd, Technology Park of Bizkaia, Derio, Bizkaia, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Susanne Gabrielsson
- Translational Immunology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Yong Song Gho
- Department of Life Science, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Dwijendra Gupta
- Center of Bioinformatics, Institute of Interdisciplinary Studies, University of Allahabad, Allahabad, India
| | | | - An Hendrix
- Laboratory of Experimental Cancer Research, Department of Radiation Oncology and Experimental Cancer Research, Ghent University Hospital, Ghent, Belgium
| | - Andrew F. Hill
- Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Australia
| | - Jameel M. Inal
- Cellular and Molecular Immunology Research Centre, Faculty of Life Sciences, London Metropolitan University, London, United Kingdom
| | - Guido Jenster
- Department of Urology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | | | - Sai Kiang Lim
- A*STAR Institute of Medical Biology and Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alicia Llorente
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital-The Norwegian Radium Hospital, Oslo, Norway
| | - Jan Lötvall
- Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Antonio Marcilla
- Área de Parasitología, Departamento de Biología Celular y Parasitología, Universitat de València, Burjassot (Valencia), Spain
| | | | - Irina Nazarenko
- Department of Environmental Health Sciences, University Medical Center Freiburg, Freiburg, Germany
| | - Rienk Nieuwland
- Department of Clinical Chemistry, Academic Medical Center, Amsterdam, The Netherlands
| | - Esther N. M. Nolte-'t Hoen
- Department of Biochemistry & Cell Biology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Akhilesh Pandey
- Institute of Bioinformatics, Bangalore, India
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology and Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Tushar Patel
- Mayo Clinic, Jacksonville, Florida, United States of America
| | - Melissa G. Piper
- Department of Internal Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Davis Heart & Lung Research Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Stefano Pluchino
- Center for Brain Repair and Wellcome Trust-MRC Stem Cell Institute, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | | | - Lawrence Rajendran
- Systems and Cell Biology of Neurodegeneration, Division of Psychiatry Research, University of Zurich, Zurich, Switzerland
| | | | | | - Gavin E. Reid
- Department of Chemistry, Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
| | | | - Raymond M. Schiffelers
- Laboratory of Clinical Chemistry and Haematology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pia Siljander
- Department of Biosciences, Division of Biochemistry and Biotechnology, University of Helsinki, Finland
| | | | - Willem Stoorvogel
- Department of Biochemistry and Cell Biology, Faculty of Veterinary Medicine and Institute of Biomembranes, Utrecht University, Utrecht, The Netherlands
| | - Douglas Taylor
- Department of Obstetrics, Gynecology and Women's Health and James Graham Brown Cancer Center, University of Louisville School of Medicine, Louisville, Kentucky, United States of America
| | - Clotilde Thery
- Institut Curie Centre de Recherche, Paris, France
- INSERM U932, Paris, France
| | - Hadi Valadi
- Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Bas W. M. van Balkom
- Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jesús Vázquez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Michel Vidal
- UMR 5235 CNRS-University Montpellier II, Montpellier, France
| | - Marca H. M. Wauben
- Department of Biochemistry & Cell Biology, Faculty of Veterinary Medicine, Life Sciences, Utrecht University, Utrecht, The Netherlands
| | - María Yáñez-Mó
- Unidad de Investigación, Hospital Santa Cristina, Instituto de Investigación Sanitaria Princesa, Madrid, Spain
| | - Margot Zoeller
- Department of Tumor Cell Biology, University Hospital of Surgery, Heidelberg, Germany
| | - Suresh Mathivanan
- Department of Biochemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia
- * E-mail:
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2769
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Younesi E, Toldo L, Müller B, Friedrich CM, Novac N, Scheer A, Hofmann-Apitius M, Fluck J. Mining biomarker information in biomedical literature. BMC Med Inform Decis Mak 2012; 12:148. [PMID: 23249606 PMCID: PMC3541249 DOI: 10.1186/1472-6947-12-148] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2012] [Accepted: 12/10/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND For selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives. METHODS A biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases. RESULTS The current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html. CONCLUSIONS The approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.
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Affiliation(s)
- Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53754, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | - Luca Toldo
- Knowledge Management, Operational Excellence & Site Coordination, Merck Serono, Merck KGaA, Darmstadt, Germany
| | - Bernd Müller
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53754, Germany
| | - Christoph M Friedrich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53754, Germany
- Department of Computer Science, University of Applied Science and Arts, Dortmund, Germany
| | - Natalia Novac
- Knowledge Management, Operational Excellence & Site Coordination, Merck Serono, Merck KGaA, Darmstadt, Germany
| | - Alexander Scheer
- Informatics & Knowledge Management, Merck Serono, Merck KGaA, Geneva, Switzerland
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53754, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | - Juliane Fluck
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, 53754, Germany
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2770
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Abstract
Background Protein-protein interaction (PPI) networks carry vital information about proteins' functions. Analysis of PPI networks associated with specific disease systems including cancer helps us in the understanding of the complex biology of diseases. Specifically, identification of similar and frequently occurring patterns (network motifs) across PPI networks will provide useful clues to better understand the biology of the diseases. Results In this study, we developed a novel pattern-mining algorithm that detects cancer associated functional subgraphs occurring in multiple cancer PPI networks. We constructed nine cancer PPI networks using differentially expressed genes from the Oncomine dataset. From these networks we discovered frequent patterns that occur in all networks and at different size levels. Patterns are abstracted subgraphs with their nodes replaced by node cluster IDs. By using effective canonical labeling and adopting weighted adjacency matrices, we are able to perform graph isomorphism test in polynomial running time. We use a bottom-up pattern growth approach to search for patterns, which allows us to effectively reduce the search space as pattern sizes grow. Validation of the frequent common patterns using GO semantic similarity showed that the discovered subgraphs scored consistently higher than the randomly generated subgraphs at each size level. We further investigated the cancer relevance of a select set of subgraphs using literature-based evidences. Conclusion Frequent common patterns exist in cancer PPI networks, which can be found through effective pattern mining algorithms. We believe that this work would allow us to identify functionally relevant and coherent subgraphs in cancer networks, which can be advanced to experimental validation to further our understanding of the complex biology of cancer.
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Affiliation(s)
- Ru Shen
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA
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2771
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Han B, Chen XW, Talebizadeh Z, Xu H. Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks. BMC Syst Biol 2012; 6 Suppl 3:S14. [PMID: 23281790 PMCID: PMC3524021 DOI: 10.1186/1752-0509-6-s3-s14] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype. RESULTS To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simulation data show that our method outperforms some other commonly-used methods in terms of power and sample-efficiency, and is especially suitable for detecting epistatic interactions with weak or no marginal effects. Furthermore, our method is scalable to real disease data. CONCLUSIONS We propose a Bayesian network-based method, EpiBN, to detect epistatic interactions. In EpiBN, we develop a new scoring function, which can reflect higher-order epistatic interactions by estimating the model complexity from data, and apply a fast Branch-and-Bound algorithm to learn the structure of a two-layer Bayesian network containing only one target node. To make our method scalable to real data, we propose the use of a Markov chain Monte Carlo (MCMC) method to perform the screening process. Applications of the proposed method to some real GWAS (genome-wide association studies) datasets may provide helpful insights into understanding the genetic basis of Age-related Macular Degeneration, late-onset Alzheimer's disease, and autism.
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Affiliation(s)
- Bing Han
- Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA
| | - Xue-wen Chen
- Department of Computer Science Wayne State University Detroit, MI 48202
| | - Zohreh Talebizadeh
- Children's Mercy Hospital and University of Missouri-Kansas City School of Medicine, 2401 Gillham Road, Kansas City, MO 64108, USA
| | - Hua Xu
- School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston, TX 77030
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2772
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Abstract
Background Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Studies have correlated risk of CRC development with dietary habits and environmental conditions. Gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. Such processes can be used to evaluate potential drug targets. Though recognition of CRC gene-signatures across populations is crucial to better understanding potential novel treatment options for CRC, it remains a challenging task. Results We developed a topological and biological feature-based network approach for identifying the gene signatures across populations. In this work, we propose a novel approach of using cliques to understand the variability within population. Cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. Our study was based on four publicly available expression datasets belonging to four different populations across the world. We identified cliques of various sizes (0 to 7) across the four population networks. Cliques of size seven were further analyzed across populations for their commonality and uniqueness. Forty-nine common cliques of size seven were identified. These cliques were further analyzed based on their connectivity profiles. We found associations between the cliques and their connectivity profiles across networks. With these clique connectivity profiles (CCPs), we were able to identify the divergence among the populations, important biological processes (cell cycle, signal transduction, and cell differentiation), and related gene pathways. Therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. In this work we demonstrate the power and effectiveness of cliques to study CRC across populations. Conclusions We developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in CRC gene profiles across four populations with unique dietary habits.
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Affiliation(s)
- Meeta P Pradhan
- School of Informatics, Indiana University Purdue University Indianapolis, IN, USA
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2773
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Abstract
BACKGROUND Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses. RESULTS We applied our novel approach to address the Predictive Signaling Network Modeling challenge of the fourth (2009) Dialog for Reverse Engineering Assessment's and Methods (DREAM4) competition. The challenge results showed that our method accurately captured signal transduction of a network of protein kinases and phosphoproteins in that the predicted protein phosphorylation levels under all experimental conditions were highly correlated (R2 = 0.93) with the observed results. Based on the evaluation of the DREAM4 organizer, our team was ranked as one of the top five best performers in predicting network structure and protein phosphorylation activity under test conditions. CONCLUSIONS Bayesian network can be used to simulate the propagation of signals in cellular systems. Incorporating the Ontology Fingerprint as prior biological knowledge allows us to efficiently infer concise signaling network structure and to accurately predict cellular responses.
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Affiliation(s)
- Tingting Qin
- Bioinformatics Graduate Program, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Lam C Tsoi
- Bioinformatics Graduate Program, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Kellie J Sims
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15232, USA
| | - W Jim Zheng
- Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA
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2774
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Levy ED, Michnick SW, Landry CR. Protein abundance is key to distinguish promiscuous from functional phosphorylation based on evolutionary information. Philos Trans R Soc Lond B Biol Sci 2012; 367:2594-606. [PMID: 22889910 DOI: 10.1098/rstb.2012.0078] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
In eukaryotic cells, protein phosphorylation is an important and widespread mechanism used to regulate protein function. Yet, of the thousands of phosphosites identified to date, only a few hundred at best have a characterized function. It was recently shown that these functional sites are significantly more conserved than phosphosites of unknown function, stressing the importance of considering evolutionary conservation in assessing the global functional landscape of phosphosites. This leads us to review studies that examined the impact of phosphorylation on evolutionary conservation. While all these studies have shown that conservation is greater among phosphorylated sites compared with non-phosphorylated ones, the magnitude of this difference varies greatly. Further, not all studies have considered key factors that may influence the rate of phosphosite evolution. Such key factors are their localization in ordered or disordered regions, their stoichiometry or the abundance of their corresponding protein. Here we take into account all of these factors simultaneously, which reveals remarkable evolutionary patterns. First, while it is well established that protein conservation increases with abundance, we show that phosphosites partly follow an opposite trend. More precisely, Saccharomyces cerevisiae phosphosites present among abundant proteins are 1.5 times more likely to diverge in the closely related species Saccharomyces bayanus when compared with phosphosites present in the 5 per cent least abundant proteins. Second, we show that conservation is coupled to stoichiometry, whereby sites frequently phosphorylated are more conserved than those rarely phosphorylated. Finally, we provide a model of functional and noisy or 'accidental' phosphorylation that explains these observations.
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Affiliation(s)
- Emmanuel D Levy
- Département de Biochimie, Université de Montréal, Montréal, Québec, Canada.
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2775
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Abstract
Background In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations. Results This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis. Conclusions This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.
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Affiliation(s)
- Chia-Lang Hsu
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
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2776
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Lei C, Ruan J. A novel link prediction algorithm for reconstructing protein-protein interaction networks by topological similarity. ACTA ACUST UNITED AC 2012; 29:355-64. [PMID: 23235927 DOI: 10.1093/bioinformatics/bts688] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
MOTIVATION Recent advances in technology have dramatically increased the availability of protein-protein interaction (PPI) data and stimulated the development of many methods for improving the systems level understanding the cell. However, those efforts have been significantly hindered by the high level of noise, sparseness and highly skewed degree distribution of PPI networks. Here, we present a novel algorithm to reduce the noise present in PPI networks. The key idea of our algorithm is that two proteins sharing some higher-order topological similarities, measured by a novel random walk-based procedure, are likely interacting with each other and may belong to the same protein complex. RESULTS Applying our algorithm to a yeast PPI network, we found that the edges in the reconstructed network have higher biological relevance than in the original network, assessed by multiple types of information, including gene ontology, gene expression, essentiality, conservation between species and known protein complexes. Comparison with existing methods shows that the network reconstructed by our method has the highest quality. Using two independent graph clustering algorithms, we found that the reconstructed network has resulted in significantly improved prediction accuracy of protein complexes. Furthermore, our method is applicable to PPI networks obtained with different experimental systems, such as affinity purification, yeast two-hybrid (Y2H) and protein-fragment complementation assay (PCA), and evidence shows that the predicted edges are likely bona fide physical interactions. Finally, an application to a human PPI network increased the coverage of the network by at least 100%. AVAILABILITY www.cs.utsa.edu/∼jruan/RWS/.
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Affiliation(s)
- Chengwei Lei
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA
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2777
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Abstract
Hypertension is a major health problem worldwide. Individuals with hypertension are at an increased risk for stroke, heart disease, and kidney failure. Although the etiology of essential hypertension has a genetic component, lifestyle factors such as diet play an important role. Insulin resistance is a common feature of hypertension in both humans and animal models affecting glucose and lipid metabolism producing excess aldehydes including methylglyoxal. These aldehydes react with proteins to form conjugates called advanced glycation end products (AGEs). This alters protein structure and function and can affect vascular and immune cells leading to their activation and secretion of inflammatory cytokines. AGEs also act via receptors for advanced glycation end products on these cells altering the function of antioxidant and metabolic enzymes, and ion channels. This results in an increase in cytosolic free calcium, decrease in nitric oxide, endothelial dysfunction, oxidative stress, peripheral vascular resistance, and infiltration of vascular and kidney tissue with inflammatory cells leading to hypertension. Supplementation with dietary antioxidants including vitamins C, E, or B(6), thiols such as cysteine and lipoic acid, have been shown to lower blood pressure and plasma inflammatory cytokines in animal models and humans with essential hypertension. A well-balanced diet rich in antioxidants that includes vegetables, fruits, low fat dairy products, low salt, and includes whole grains, poultry, fish and nuts, lowers blood pressure and vascular inflammation. These antioxidants may achieve their antihypertensive and anti-inflammatory/immunomodulatory effects by reducing AGEs and improving insulin resistance and associated alterations. Dietary supplementation with antioxidants may be a beneficial, inexpensive, front-line alterative treatment modality for hypertension.
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Affiliation(s)
- Sudesh Vasdev
- Discipline of Medicine, Health Sciences Centre, Memorial University, St. John's, Newfoundland, Canada
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2778
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Yilmaz OH, Deshpande V. Pathology and Genetics of Pancreatic Neoplasms. Surg Pathol Clin 2012; 5:941-59. [PMID: 26838509 DOI: 10.1016/j.path.2012.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
This is a state-of-the-art review of the molecular genetics of pancreatic neoplasms. Although understanding of the molecular features underlying pancreatic neoplasms is still in its infancy, a strong emphasis on the relevance of these findings for the practicing surgical pathologist is provided. The application of molecular techniques has yielded a wealth of information that may soon enhance diagnostics, and will also lead to the development of safer, more effective targeted therapies. The pathologist will play a key role in integrating the current pathologic classification system with newly validated molecular markers.
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Affiliation(s)
- Omer H Yilmaz
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Warren 2, Boston, MA 02478, USA
| | - Vikram Deshpande
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Warren 2, Boston, MA 02478, USA.
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2779
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Han Y, Han D, Yan Z, Boyd-Kirkup JD, Green CD, Khaitovich P, Han JDJ. Stress-associated H3K4 methylation accumulates during postnatal development and aging of rhesus macaque brain. Aging Cell 2012; 11:1055-64. [PMID: 22978322 DOI: 10.1111/acel.12007] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/09/2012] [Indexed: 12/31/2022] Open
Abstract
Epigenetic modifications are critical determinants of cellular and developmental states. Epigenetic changes, such as decreased H3K27me3 histone methylation on insulin/IGF1 genes, have been previously shown to modulate lifespan through gene expression regulation. However, global epigenetic changes during aging and their biological functions, if any, remain elusive. Here, we examined the histone modification H3K4 dimethylation (H3K4me2) in the prefrontal cortex of individual rhesus macaques at different ages by chromatin immunoprecipitation, followed by deep sequencing (ChIP-seq) at the whole genome level. Through integrative analysis of the ChIP-seq profiles with gene expression data, we found that H3K4me2 increased at promoters and enhancers globally during postnatal development and aging, and those that correspond to gene expression changes in cis are enriched for stress responses, such as the DNA damage response. This suggests that metabolic and environmental stresses experienced by an organism are associated with the progressive opening of chromatin. In support of this, we also observed increased expression of two H3K4 methyltransferases, SETD7 and DPY30, in aged macaque brain.
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Affiliation(s)
| | | | - Zheng Yan
- Chinese Academy of Sciences Key laboratory for Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences; 320 Yue Yang Road; Shanghai; 200031; China
| | - Jerome D. Boyd-Kirkup
- Chinese Academy of Sciences Key laboratory for Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences; 320 Yue Yang Road; Shanghai; 200031; China
| | - Christopher D. Green
- Chinese Academy of Sciences Key laboratory for Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences; 320 Yue Yang Road; Shanghai; 200031; China
| | | | - Jing-Dong J. Han
- Chinese Academy of Sciences Key laboratory for Computational Biology; Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology; Shanghai Institutes for Biological Sciences; Chinese Academy of Sciences; 320 Yue Yang Road; Shanghai; 200031; China
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2780
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2781
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Kumar GSS, Venugopal AK, Mahadevan A, Renuse S, Harsha HC, Sahasrabuddhe NA, Pawar H, Sharma R, Kumar P, Rajagopalan S, Waddell K, Ramachandra YL, Satishchandra P, Chaerkady R, Prasad TSK, Shankar K, Pandey A. Quantitative proteomics for identifying biomarkers for tuberculous meningitis. Clin Proteomics 2012. [PMID: 23198679 DOI: 10.1186/1559-0275-9-12] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
UNLABELLED INTRODUCTION Tuberculous meningitis is a frequent extrapulmonary disease caused by Mycobacterium tuberculosis and is associated with high mortality rates and severe neurological sequelae. In an earlier study employing DNA microarrays, we had identified genes that were differentially expressed at the transcript level in human brain tissue from cases of tuberculous meningitis. In the current study, we used a quantitative proteomics approach to discover protein biomarkers for tuberculous meningitis. METHODS To compare brain tissues from confirmed cased of tuberculous meningitis with uninfected brain tissue, we carried out quantitative protein expression profiling using iTRAQ labeling and LC-MS/MS analysis of SCX fractionated peptides on Agilent's accurate mass QTOF mass spectrometer. RESULTS AND CONCLUSIONS Through this approach, we identified both known and novel differentially regulated molecules. Those described previously included signal-regulatory protein alpha (SIRPA) and protein disulfide isomerase family A, member 6 (PDIA6), which have been shown to be overexpressed at the mRNA level in tuberculous meningitis. The novel overexpressed proteins identified in our study included amphiphysin (AMPH) and neurofascin (NFASC) while ferritin light chain (FTL) was found to be downregulated in TBM. We validated amphiphysin, neurofascin and ferritin light chain using immunohistochemistry which confirmed their differential expression in tuberculous meningitis. Overall, our data provides insights into the host response in tuberculous meningitis at the molecular level in addition to providing candidate diagnostic biomarkers for tuberculous meningitis.
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2782
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Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A, Stark C, Nixon J, Ramage L, Kolas N, O'Donnell L, Reguly T, Breitkreutz A, Sellam A, Chen D, Chang C, Rust J, Livstone M, Oughtred R, Dolinski K, Tyers M. The BioGRID interaction database: 2013 update. Nucleic Acids Res 2012. [PMID: 23203989 PMCID: PMC3531226 DOI: 10.1093/nar/gks1158] [Citation(s) in RCA: 502] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.
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Affiliation(s)
- Andrew Chatr-Aryamontri
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec, Canada H3C 3J7
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2783
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Obayashi T, Okamura Y, Ito S, Tadaka S, Motoike IN, Kinoshita K. COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals. Nucleic Acids Res 2012. [PMID: 23203868 PMCID: PMC3531062 DOI: 10.1093/nar/gks1014] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.
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Affiliation(s)
- Takeshi Obayashi
- Graduate School of Information Sciences, Tohoku University, Sendai 980-8679, Japan
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2784
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Abstract
As an alternative to modern western medicine, Traditional Chinese Medicine (TCM) is receiving increasingly attention worldwide. Great efforts have been paid to TCM’s modernization, which tries to bridge the gap between TCM and modern western medicine. As TCM and modern western medicine share a common aspect at molecular level that the compound(s) perturb human’s dysfunction network and restore human normal physiological condition, the relationship between compounds (in herb, refer to ingredients) and their targets (proteins) should be the key factor to connect TCM and modern medicine. Accordingly, we construct this Traditional Chinese Medicine Integrated Database (TCMID, http://www.megabionet.org/tcmid/), which records TCM-related information collected from different resources and through text-mining method. To enlarge the scope of the TCMID, the data have been linked to common drug and disease databases, including Drugbank, OMIM and PubChem. Currently, our TCMID contains ∼47 000 prescriptions, 8159 herbs, 25 210 compounds, 6828 drugs, 3791 diseases and 17 521 related targets, which is the largest data set for related field. Our web-based software displays a network for integrative relationships between herbs and their treated diseases, the active ingredients and their targets, which will facilitate the study of combination therapy and understanding of the underlying mechanisms for TCM at molecular level.
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Affiliation(s)
- Ruichao Xue
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Science, East China Normal University, Shanghai 200241, China
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2785
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Minguez P, Letunic I, Parca L, Bork P. PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins. Nucleic Acids Res 2012. [PMID: 23193284 PMCID: PMC3531129 DOI: 10.1093/nar/gks1230] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Post-translational modifications (PTMs) are involved in the regulation and structural stabilization of eukaryotic proteins. The combination of individual PTM states is a key to modulate cellular functions as became evident in a few well-studied proteins. This combinatorial setting, dubbed the PTM code, has been proposed to be extended to whole proteomes in eukaryotes. Although we are still far from deciphering such a complex language, thousands of protein PTM sites are being mapped by high-throughput technologies, thus providing sufficient data for comparative analysis. PTMcode (http://ptmcode.embl.de) aims to compile known and predicted PTM associations to provide a framework that would enable hypothesis-driven experimental or computational analysis of various scales. In its first release, PTMcode provides PTM functional associations of 13 different PTM types within proteins in 8 eukaryotes. They are based on five evidence channels: a literature survey, residue co-evolution, structural proximity, PTMs at the same residue and location within PTM highly enriched protein regions (hotspots). PTMcode is presented as a protein-based searchable database with an interactive web interface providing the context of the co-regulation of nearly 75 000 residues in >10 000 proteins.
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Affiliation(s)
- Pablo Minguez
- European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany
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2786
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Nelson TH, Jung JY, DeLuca TF, Hinebaugh BK, St Gabriel KC, Wall DP. Autworks: a cross-disease network biology application for Autism and related disorders. BMC Med Genomics 2012; 5:56. [PMID: 23190929 PMCID: PMC3533944 DOI: 10.1186/1755-8794-5-56] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2012] [Accepted: 11/22/2012] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The genetic etiology of autism is heterogeneous. Multiple disorders share genotypic and phenotypic traits with autism. Network based cross-disorder analysis can aid in the understanding and characterization of the molecular pathology of autism, but there are few tools that enable us to conduct cross-disorder analysis and to visualize the results. DESCRIPTION We have designed Autworks as a web portal to bring together gene interaction and gene-disease association data on autism to enable network construction, visualization, network comparisons with numerous other related neurological conditions and disorders. Users may examine the structure of gene interactions within a set of disorder-associated genes, compare networks of disorder/disease genes with those of other disorders/diseases, and upload their own sets for comparative analysis. CONCLUSIONS Autworks is a web application that provides an easy-to-use resource for researchers of varied backgrounds to analyze the autism gene network structure within and between disorders. AVAILABILITY http://autworks.hms.harvard.edu/
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Affiliation(s)
- Tristan H Nelson
- The Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Jae-Yoon Jung
- The Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Todd F DeLuca
- The Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Byron K Hinebaugh
- The Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | | | - Dennis P Wall
- The Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
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2787
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Zhang Y, Zhong L, Xu B, Yang Y, Ban R, Zhu J, Cooke HJ, Hao Q, Shi Q. SpermatogenesisOnline 1.0: a resource for spermatogenesis based on manual literature curation and genome-wide data mining. Nucleic Acids Res 2012. [PMID: 23193286 PMCID: PMC3531227 DOI: 10.1093/nar/gks1186] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Human infertility affects 10-15% of couples, half of which is attributed to the male partner. Abnormal spermatogenesis is a major cause of male infertility. Characterizing the genes involved in spermatogenesis is fundamental to understand the mechanisms underlying this biological process and in developing treatments for male infertility. Although many genes have been implicated in spermatogenesis, no dedicated bioinformatic resource for spermatogenesis is available. We have developed such a database, SpermatogenesisOnline 1.0 (http://mcg.ustc.edu.cn/sdap1/spermgenes/), using manual curation from 30 233 articles published before 1 May 2012. It provides detailed information for 1666 genes reported to participate in spermatogenesis in 37 organisms. Based on the analysis of these genes, we developed an algorithm, Greed AUC Stepwise (GAS) model, which predicted 762 genes to participate in spermatogenesis (GAS probability >0.5) based on genome-wide transcriptional data in Mus musculus testis from the ArrayExpress database. These predicted and experimentally verified genes were annotated, with several identical spermatogenesis-related GO terms being enriched for both classes. Furthermore, protein-protein interaction analysis indicates direct interactions of predicted genes with the experimentally verified ones, which supports the reliability of GAS. The strategy (manual curation and data mining) used to develop SpermatogenesisOnline 1.0 can be easily extended to other biological processes.
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Affiliation(s)
- Yuanwei Zhang
- Hefei National Laboratory for Physical Sciences at Microscale and Department of Life Sciences, University of Science and Technology of China, Hefei 230027, China
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2788
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Lu CT, Huang KY, Su MG, Lee TY, Bretaña NA, Chang WC, Chen YJ, Chen YJ, Huang HD. DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications. Nucleic Acids Res 2012. [PMID: 23193290 PMCID: PMC3531199 DOI: 10.1093/nar/gks1229] [Citation(s) in RCA: 165] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Protein modification is an extremely important post-translational regulation that adjusts the physical and chemical properties, conformation, stability and activity of a protein; thus altering protein function. Due to the high throughput of mass spectrometry (MS)-based methods in identifying site-specific post-translational modifications (PTMs), dbPTM (http://dbPTM.mbc.nctu.edu.tw/) is updated to integrate experimental PTMs obtained from public resources as well as manually curated MS/MS peptides associated with PTMs from research articles. Version 3.0 of dbPTM aims to be an informative resource for investigating the substrate specificity of PTM sites and functional association of PTMs between substrates and their interacting proteins. In order to investigate the substrate specificity for modification sites, a newly developed statistical method has been applied to identify the significant substrate motifs for each type of PTMs containing sufficient experimental data. According to the data statistics in dbPTM, >60% of PTM sites are located in the functional domains of proteins. It is known that most PTMs can create binding sites for specific protein-interaction domains that work together for cellular function. Thus, this update integrates protein–protein interaction and domain–domain interaction to determine the functional association of PTM sites located in protein-interacting domains. Additionally, the information of structural topologies on transmembrane (TM) proteins is integrated in dbPTM in order to delineate the structural correlation between the reported PTM sites and TM topologies. To facilitate the investigation of PTMs on TM proteins, the PTM substrate sites and the structural topology are graphically represented. Also, literature information related to PTMs, orthologous conservations and substrate motifs of PTMs are also provided in the resource. Finally, this version features an improved web interface to facilitate convenient access to the resource.
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Affiliation(s)
- Cheng-Tsung Lu
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 320, Taiwan
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2789
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Abstract
RNA interference (RNAi) represents a powerful method to systematically study loss-of-function phenotypes on a large scale with a wide variety of biological assays, constituting a rich source for the assignment of gene function. The GenomeRNAi database (http://www.genomernai.org) makes available RNAi phenotype data extracted from the literature for human and Drosophila. It also provides RNAi reagent information, along with an assessment as to their efficiency and specificity. This manuscript describes an update of the database previously featured in the NAR Database Issue. The new version has undergone a complete re-design of the user interface, providing an intuitive, flexible framework for additional functionalities. Screen information and gene-reagent-phenotype associations are now available for download. The integration with other resources has been improved by allowing in-links via GenomeRNAi screen IDs, or external gene or reagent identifiers. A distributed annotation system (DAS) server enables the visualization of the phenotypes and reagents in the context of a genome browser. We have added a page listing ‘frequent hitters’, i.e. genes that show a phenotype in many screens, which might guide on-going RNAi studies. Structured annotation guidelines have been established to facilitate consistent curation, and a submission template for direct submission by data producers is available for download.
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Affiliation(s)
- Esther E Schmidt
- Division Signaling and Functional Genomics, German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany
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2790
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Raad M, El Tal T, Gul R, Mondello S, Zhang Z, Boustany RM, Guingab J, Wang KK, Kobeissy F. Neuroproteomics approach and neurosystems biology analysis: ROCK inhibitors as promising therapeutic targets in neurodegeneration and neurotrauma. Electrophoresis 2012; 33:3659-68. [DOI: 10.1002/elps.201200470] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2012] [Revised: 10/07/2012] [Accepted: 10/08/2012] [Indexed: 12/14/2022]
Affiliation(s)
- Mohamad Raad
- Department of Biochemistry and Molecular Genetics; Faculty of Medicine; American University of Beirut; Beirut; Lebanon
| | - Tala El Tal
- Faculty of Medicine; American University of Beirut; Beirut; Lebanon
| | - Rukhsana Gul
- Department of Internal Medicine; Harry S. Truman Veterans Affairs Medical Center; University of Missouri; Columbia; MO; USA
| | - Stefania Mondello
- Center of Innovative Research Banyan Biomarkers Inc.; Alachua; FL; USA
| | - Zhiqun Zhang
- Department of Psychiatry; University of Florida; Gainesville; FL; USA
| | | | - Joy Guingab
- Center of Innovative Research Banyan Biomarkers Inc.; Alachua; FL; USA
| | - Kevin K. Wang
- Department of Psychiatry; University of Florida; Gainesville; FL; USA
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2791
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Kanakasabai S, Pestereva E, Chearwae W, Gupta SK, Ansari S, Bright JJ. PPARγ agonists promote oligodendrocyte differentiation of neural stem cells by modulating stemness and differentiation genes. PLoS One 2012. [PMID: 23185633 PMCID: PMC3503969 DOI: 10.1371/journal.pone.0050500] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Neural stem cells (NSCs) are a small population of resident cells that can grow, migrate and differentiate into neuro-glial cells in the central nervous system (CNS). Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor transcription factor that regulates cell growth and differentiation. In this study we analyzed the influence of PPARγ agonists on neural stem cell growth and differentiation in culture. We found that in vitro culture of mouse NSCs in neurobasal medium with B27 in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF) induced their growth and expansion as neurospheres. Addition of all-trans retinoic acid (ATRA) and PPARγ agonist ciglitazone or 15-Deoxy-Δ12,14-Prostaglandin J2 (15d-PGJ2) resulted in a dose-dependent inhibition of cell viability and proliferation of NSCs in culture. Interestingly, NSCs cultured with PPARγ agonists, but not ATRA, showed significant increase in oligodendrocyte precursor-specific O4 and NG2 reactivity with a reduction in NSC marker nestin, in 3–7 days. In vitro treatment with PPARγ agonists and ATRA also induced modest increase in the expression of neuronal β-III tubulin and astrocyte-specific GFAP in NSCs in 3–7 days. Further analyses showed that PPARγ agonists and ATRA induced significant alterations in the expression of many stemness and differentiation genes associated with neuro-glial differentiation in NSCs. These findings highlight the influence of PPARγ agonists in promoting neuro-glial differentiation of NSCs and its significance in the treatment of neurodegenerative diseases.
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Affiliation(s)
- Saravanan Kanakasabai
- Neuroscience Research Laboratory, Methodist Research Institute, Indiana University Health, Indianapolis, Indiana, United States of America
| | - Ecaterina Pestereva
- Neuroscience Research Laboratory, Methodist Research Institute, Indiana University Health, Indianapolis, Indiana, United States of America
| | - Wanida Chearwae
- Neuroscience Research Laboratory, Methodist Research Institute, Indiana University Health, Indianapolis, Indiana, United States of America
| | - Sushil K. Gupta
- Neuroscience Research Laboratory, Methodist Research Institute, Indiana University Health, Indianapolis, Indiana, United States of America
| | - Saif Ansari
- Neuroscience Research Laboratory, Methodist Research Institute, Indiana University Health, Indianapolis, Indiana, United States of America
| | - John J. Bright
- Neuroscience Research Laboratory, Methodist Research Institute, Indiana University Health, Indianapolis, Indiana, United States of America
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- * E-mail:
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2792
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Suo SB, Qiu JD, Shi SP, Sun XY, Huang SY, Chen X, Liang RP. Position-specific analysis and prediction for protein lysine acetylation based on multiple features. PLoS One 2012; 7:e49108. [PMID: 23173045 PMCID: PMC3500252 DOI: 10.1371/journal.pone.0049108] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2012] [Accepted: 10/04/2012] [Indexed: 11/17/2022] Open
Abstract
Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.
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Affiliation(s)
- Sheng-Bao Suo
- Department of Chemistry, Nanchang University, Nanchang, China
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2793
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Shah SN, Kerr C, Cope L, Zambidis E, Liu C, Hillion J, Belton A, Huso DL, Resar LMS. HMGA1 reprograms somatic cells into pluripotent stem cells by inducing stem cell transcriptional networks. PLoS One 2012; 7:e48533. [PMID: 23166588 PMCID: PMC3499526 DOI: 10.1371/journal.pone.0048533] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Accepted: 09/26/2012] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Although recent studies have identified genes expressed in human embryonic stem cells (hESCs) that induce pluripotency, the molecular underpinnings of normal stem cell function remain poorly understood. The high mobility group A1 (HMGA1) gene is highly expressed in hESCs and poorly differentiated, stem-like cancers; however, its role in these settings has been unclear. METHODS/PRINCIPAL FINDINGS We show that HMGA1 is highly expressed in fully reprogrammed iPSCs and hESCs, with intermediate levels in ECCs and low levels in fibroblasts. When hESCs are induced to differentiate, HMGA1 decreases and parallels that of other pluripotency factors. Conversely, forced expression of HMGA1 blocks differentiation of hESCs. We also discovered that HMGA1 enhances cellular reprogramming of somatic cells to iPSCs together with the Yamanaka factors (OCT4, SOX2, KLF4, cMYC - OSKM). HMGA1 increases the number and size of iPSC colonies compared to OSKM controls. Surprisingly, there was normal differentiation in vitro and benign teratoma formation in vivo of the HMGA1-derived iPSCs. During the reprogramming process, HMGA1 induces the expression of pluripotency genes, including SOX2, LIN28, and cMYC, while knockdown of HMGA1 in hESCs results in the repression of these genes. Chromatin immunoprecipitation shows that HMGA1 binds to the promoters of these pluripotency genes in vivo. In addition, interfering with HMGA1 function using a short hairpin RNA or a dominant-negative construct blocks cellular reprogramming to a pluripotent state. CONCLUSIONS Our findings demonstrate for the first time that HMGA1 enhances cellular reprogramming from a somatic cell to a fully pluripotent stem cell. These findings identify a novel role for HMGA1 as a key regulator of the stem cell state by inducing transcriptional networks that drive pluripotency. Although further studies are needed, these HMGA1 pathways could be exploited in regenerative medicine or as novel therapeutic targets for poorly differentiated, stem-like cancers.
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Affiliation(s)
- Sandeep N. Shah
- Hematology Division, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Medicine, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Candace Kerr
- Obstetrics & Gynecology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Leslie Cope
- Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Biostatistics, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Elias Zambidis
- Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Comparative Molecular & Pathobiology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Cyndi Liu
- Obstetrics & Gynecology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Joelle Hillion
- Hematology Division, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Amy Belton
- Hematology Division, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Medicine, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - David L. Huso
- Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Comparative Molecular & Pathobiology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Pathology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Linda M. S. Resar
- Hematology Division, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Medicine, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Oncology, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Pediatrics, the Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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2794
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Thahir M, Sharma T, Ganapathiraju MK. An efficient heuristic method for active feature acquisition and its application to protein-protein interaction prediction. BMC Proc 2012; 6 Suppl 7:S2. [PMID: 23173746 PMCID: PMC3504800 DOI: 10.1186/1753-6561-6-s7-s2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Machine learning approaches for classification learn the pattern of the feature space of different classes, or learn a boundary that separates the feature space into different classes. The features of the data instances are usually available, and it is only the class-labels of the instances that are unavailable. For example, to classify text documents into different topic categories, the words in the documents are features and they are readily available, whereas the topic is what is predicted. However, in some domains obtaining features may be resource-intensive because of which not all features may be available. An example is that of protein-protein interaction prediction, where not only are the labels ('interacting' or 'non-interacting') unavailable, but so are some of the features. It may be possible to obtain at least some of the missing features by carrying out a few experiments as permitted by the available resources. If only a few experiments can be carried out to acquire missing features, which proteins should be studied and which features of those proteins should be determined? From the perspective of machine learning for PPI prediction, it would be desirable that those features be acquired which when used in training the classifier, the accuracy of the classifier is improved the most. That is, the utility of the feature-acquisition is measured in terms of how much acquired features contribute to improving the accuracy of the classifier. Active feature acquisition (AFA) is a strategy to preselect such instance-feature combinations (i.e. protein and experiment combinations) for maximum utility. The goal of AFA is the creation of optimal training set that would result in the best classifier, and not in determining the best classification model itself. RESULTS We present a heuristic method for active feature acquisition to calculate the utility of acquiring a missing feature. This heuristic takes into account the change in belief of the classification model induced by the acquisition of the feature under consideration. As compared to random selection of proteins on which the experiments are performed and the type of experiment that is performed, the heuristic method reduces the number of experiments to as few as 40%. Most notable characteristic of this method is that it does not require re-training of the classification model on every possible combination of instance, feature and feature-value tuples. For this reason, our method is far less computationally expensive as compared with previous AFA strategies. CONCLUSIONS The results show that our heuristic method for AFA creates an optimal training set with far less features acquired as compared to random acquisition. This shows the value of active feature acquisition to aid in protein-protein interaction prediction where feature acquisition is costly. Compared to previous methods, the proposed method reduces computational cost while also achieving a better F-score. The proposed method is valuable as it presents a direction to AFA with a far lesser computational expense by removing the need for the first time, of training a classifier for every combination of instance, feature and feature-value tuples which would be impractical for several domains.
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Affiliation(s)
- Mohamed Thahir
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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2795
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Mora A, Donaldson IM. Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. BMC Bioinformatics 2012; 13:294. [PMID: 23146171 PMCID: PMC3534413 DOI: 10.1186/1471-2105-13-294] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Accepted: 11/02/2012] [Indexed: 11/21/2022] Open
Abstract
Background Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. Results Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. Conclusions These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.
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Affiliation(s)
- Antonio Mora
- Department for Molecular Biosciences, University of Oslo, Norway
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2796
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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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2797
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Silva ART, Grinberg LT, Farfel JM, Diniz BS, Lima LA, Silva PJS, Ferretti REL, Rocha RM, Filho WJ, Carraro DM, Brentani H. Transcriptional alterations related to neuropathology and clinical manifestation of Alzheimer's disease. PLoS One 2012; 7:e48751. [PMID: 23144955 PMCID: PMC3492444 DOI: 10.1371/journal.pone.0048751] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Accepted: 10/01/2012] [Indexed: 11/18/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common cause of dementia in the human population, characterized by a spectrum of neuropathological abnormalities that results in memory impairment and loss of other cognitive processes as well as the presence of non-cognitive symptoms. Transcriptomic analyses provide an important approach to elucidating the pathogenesis of complex diseases like AD, helping to figure out both pre-clinical markers to identify susceptible patients and the early pathogenic mechanisms to serve as therapeutic targets. This study provides the gene expression profile of postmortem brain tissue from subjects with clinic-pathological AD (Braak IV, V, or V and CERAD B or C; and CDR ≥1), preclinical AD (Braak IV, V, or VI and CERAD B or C; and CDR = 0), and healthy older individuals (Braak ≤ II and CERAD 0 or A; and CDR = 0) in order to establish genes related to both AD neuropathology and clinical emergence of dementia. Based on differential gene expression, hierarchical clustering and network analysis, genes involved in energy metabolism, oxidative stress, DNA damage/repair, senescence, and transcriptional regulation were implicated with the neuropathology of AD; a transcriptional profile related to clinical manifestation of AD could not be detected with reliability using differential gene expression analysis, although genes involved in synaptic plasticity, and cell cycle seems to have a role revealed by gene classifier. In conclusion, the present data suggest gene expression profile changes secondary to the development of AD-related pathology and some genes that appear to be related to the clinical manifestation of dementia in subjects with significant AD pathology, making necessary further investigations to better understand these transcriptional findings on the pathogenesis and clinical emergence of AD.
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Affiliation(s)
- Aderbal R. T. Silva
- Research Center (CIPE), A. C. Camargo Hospital, Sao Paulo, Brazil
- Brazilian Brain Bank of the Aging Brain Study Group - Laboratory of Medical Investigations 22 (LIM 22), São Paulo, Brazil
| | - Lea T. Grinberg
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, California, United States of America
- Brazilian Brain Bank of the Aging Brain Study Group - Laboratory of Medical Investigations 22 (LIM 22), São Paulo, Brazil
| | - Jose M. Farfel
- Brazilian Brain Bank of the Aging Brain Study Group - Laboratory of Medical Investigations 22 (LIM 22), São Paulo, Brazil
- Division of Geriatrics, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | - Breno S. Diniz
- Laboratory of Neuroscience - Laboratory of Medical Investigations 27 (LIM 27) - Department and Institute of Psychiatry, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | - Leandro A. Lima
- Research Center (CIPE), A. C. Camargo Hospital, Sao Paulo, Brazil
| | - Paulo J. S. Silva
- Department of Computer Science, Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo, Brazil
| | - Renata E. L. Ferretti
- Brazilian Brain Bank of the Aging Brain Study Group - Laboratory of Medical Investigations 22 (LIM 22), São Paulo, Brazil
- Division of Geriatrics, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | - Rafael M. Rocha
- Research Center (CIPE), A. C. Camargo Hospital, Sao Paulo, Brazil
| | - Wilson Jacob Filho
- Brazilian Brain Bank of the Aging Brain Study Group - Laboratory of Medical Investigations 22 (LIM 22), São Paulo, Brazil
- Division of Geriatrics, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | - Dirce M. Carraro
- Research Center (CIPE), A. C. Camargo Hospital, Sao Paulo, Brazil
| | - Helena Brentani
- Laboratory of Clinical Pathology - Laboratory of Medical Investigations 23 (LIM 23), Department and Institute of Psychiatry, Medical School, University of Sao Paulo, Sao Paulo, Brazil
- * E-mail:
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2798
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Abstract
As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.
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Affiliation(s)
- Marina Bessarabova
- Thomson Reuters, IP & Science, 5901 Priestly Dr., #200, Carlsbad, CA 92008, USA
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2799
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Nabhan AR, Sarkar IN. Mining disease fingerprints from within genetic pathways. AMIA Annu Symp Proc 2012; 2012:1320-1329. [PMID: 23304411 PMCID: PMC3540421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.
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Affiliation(s)
- Ahmed Ragab Nabhan
- Center for Clinical & Translational Science, University of Vermont, Burlington, VT, USA
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2800
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Kim MS, Kuppireddy SV, Sakamuri S, Singal M, Getnet D, Harsha HC, Goel R, Balakrishnan L, Jacob HKC, Kashyap MK, Tankala SG, Maitra A, Iacobuzio-Donahue CA, Jaffee E, Goggins MG, Velculescu VE, Hruban RH, Pandey A. Rapid characterization of candidate biomarkers for pancreatic cancer using cell microarrays (CMAs). J Proteome Res 2012; 11:5556-63. [PMID: 22985314 PMCID: PMC3565537 DOI: 10.1021/pr300483r] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Tissue microarrays have become a valuable tool for high-throughput analysis using immunohistochemical labeling. However, the large majority of biochemical studies are carried out in cell lines to further characterize candidate biomarkers or therapeutic targets with subsequent studies in animals or using primary tissues. Thus, cell line-based microarrays could be a useful screening tool in some situations. Here, we constructed a cell microarray (CMA) containing a panel of 40 pancreatic cancer cell lines available from American Type Culture Collection in addition to those locally available at Johns Hopkins. As proof of principle, we performed immunocytochemical labeling of an epithelial cell adhesion molecule (Ep-CAM), a molecule generally expressed in the epithelium, on this pancreatic cancer CMA. In addition, selected molecules that have been previously shown to be differentially expressed in pancreatic cancer in the literature were validated. For example, we observed strong labeling of CA19-9 antigen, a prognostic and predictive marker for pancreatic cancer. We also carried out a bioinformatics analysis of a literature curated catalog of pancreatic cancer biomarkers developed previously by our group and identified two candidate biomarkers, HLA class I and transmembrane protease, serine 4 (TMPRSS4), and examined their expression in the cell lines represented on the pancreatic cancer CMAs. Our results demonstrate the utility of CMAs as a useful resource for rapid screening of molecules of interest and suggest that CMAs can become a universal standard platform in cancer research.
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Affiliation(s)
- Min-Sik Kim
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Sarada V. Kuppireddy
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Sruthi Sakamuri
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Mukul Singal
- Government Medical College and Hospital, Chandigarh 160030, India
| | - Derese Getnet
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - H. C. Harsha
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India
| | - Renu Goel
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India
| | - Lavanya Balakrishnan
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India
| | - Harrys K. C. Jacob
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India
| | - Manoj K. Kashyap
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India
| | | | - Anirban Maitra
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, United States
| | - Christine A. Iacobuzio-Donahue
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, United States
| | - Elizabeth Jaffee
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, United States
| | - Michael G. Goggins
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, United States
| | - Victor E. Velculescu
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Ludwig Center for Cancer Genetics and Therapeutics, Johns Hopkins Kimmel Cancer Center, Baltimore, Maryland 21231, United States
| | - Ralph H. Hruban
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, United States
| | - Akhilesh Pandey
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Pathology Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland 21231, United States
- Corresponding Author . Fax: +1-410-502-7544.
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