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Yuan K, Zeng T, Chen L. Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study. Front Cell Dev Biol 2022; 9:720321. [PMID: 35155440 PMCID: PMC8826544 DOI: 10.3389/fcell.2021.720321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.
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Affiliation(s)
- Kai Yuan
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Guangzhou Laboratory, Guangzhou, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
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2
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Hawe JS, Wilson R, Schmid KT, Zhou L, Lakshmanan LN, Lehne BC, Kühnel B, Scott WR, Wielscher M, Yew YW, Baumbach C, Lee DP, Marouli E, Bernard M, Pfeiffer L, Matías-García PR, Autio MI, Bourgeois S, Herder C, Karhunen V, Meitinger T, Prokisch H, Rathmann W, Roden M, Sebert S, Shin J, Strauch K, Zhang W, Tan WLW, Hauck SM, Merl-Pham J, Grallert H, Barbosa EGV, Illig T, Peters A, Paus T, Pausova Z, Deloukas P, Foo RSY, Jarvelin MR, Kooner JS, Loh M, Heinig M, Gieger C, Waldenberger M, Chambers JC. Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function. Nat Genet 2022; 54:18-29. [PMID: 34980917 DOI: 10.1038/s41588-021-00969-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/18/2021] [Indexed: 02/07/2023]
Abstract
We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P < 10-14), including 467,915 meQTL that operate in trans. The meQTL are enriched for functionally relevant characteristics, including shared chromatin state, High-throuhgput chromosome conformation interaction, and association with gene expression, metabolic variation and clinical traits. We use molecular interaction and colocalization analyses to identify multiple nuclear regulatory pathways linking meQTL loci to phenotypic variation, including UBASH3B (body mass index), NFKBIE (rheumatoid arthritis), MGA (blood pressure) and COMMD7 (white cell counts). For rs6511961 , chromatin immunoprecipitation followed by sequencing (ChIP-seq) validates zinc finger protein (ZNF)333 as the likely trans acting effector protein. Finally, we used interaction analyses to identify population- and lineage-specific meQTL, including rs174548 in FADS1, with the strongest effect in CD8+ T cells, thus linking fatty acid metabolism with immune dysregulation and asthma. Our study advances understanding of the potential pathways linking genetic variation to human phenotype.
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Affiliation(s)
- Johann S Hawe
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Informatics, Technical University of Munich, Garching bei München, Germany
| | - Rory Wilson
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - Katharina T Schmid
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Informatics, Technical University of Munich, Garching bei München, Germany
| | - Li Zhou
- Lee Kong Chian School of Medicine, Singapore, Singapore
| | | | - Benjamin C Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Brigitte Kühnel
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - William R Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Matthias Wielscher
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Yik Weng Yew
- Lee Kong Chian School of Medicine, Singapore, Singapore
| | - Clemens Baumbach
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | | | - Eirini Marouli
- Centre for Genomic Health, Queen Mary University of London, London, UK
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Manon Bernard
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Liliane Pfeiffer
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - Pamela R Matías-García
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
| | - Matias I Autio
- Genome Institute of Singapore, Singapore, Singapore
- Cardiovascular Research Institute, National University Health Systems, National University of Singapore, Singapore, Singapore
| | - Stephane Bourgeois
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Christian Herder
- German Center for Diabetes Research (DZD), partner site Düsseldorf, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), partner site Düsseldorf, Düsseldorf, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), partner site Düsseldorf, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Sylvain Sebert
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department for Genomics of Common Diseases, School of Public Health, Imperial College London, London, UK
| | - Jean Shin
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Konstantin Strauch
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Southall, UK
| | | | - Stefanie M Hauck
- Research Unit Protein Science, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich, Germany
| | - Juliane Merl-Pham
- Research Unit Protein Science, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich, Germany
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - Eudes G V Barbosa
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Neuherberg, Germany
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Institute for Human Genetics, Hannover Medical School, Hannover, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Hannover, Germany
| | - Tomas Paus
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Canada
| | - Zdenka Pausova
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Panos Deloukas
- Centre for Genomic Health, Queen Mary University of London, London, UK
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Roger S Y Foo
- Genome Institute of Singapore, Singapore, Singapore
- Cardiovascular Research Institute, National University Health Systems, National University of Singapore, Singapore, Singapore
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Jaspal S Kooner
- National Heart and Lung Institute, Imperial College London, London, UK.
| | - Marie Loh
- Lee Kong Chian School of Medicine, Singapore, Singapore.
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
| | - Matthias Heinig
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, Neuherberg, Germany.
- Department of Informatics, Technical University of Munich, Garching bei München, Germany.
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany.
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg, Germany.
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Hannover, Germany.
| | - John C Chambers
- Lee Kong Chian School of Medicine, Singapore, Singapore.
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
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3
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Yılmaz S, Ayati M, Schlatzer D, Çiçek AE, Chance MR, Koyutürk M. Robust inference of kinase activity using functional networks. Nat Commun 2021; 12:1177. [PMID: 33608514 PMCID: PMC7895941 DOI: 10.1038/s41467-021-21211-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 01/15/2021] [Indexed: 12/17/2022] Open
Abstract
Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io. Kinases drive fundamental changes in cell state, but predicting kinase activity based on substrate-level changes can be challenging. Here the authors introduce a computational framework that utilizes similarities between substrates to robustly infer kinase activity.
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Affiliation(s)
- Serhan Yılmaz
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Marzieh Ayati
- Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - Daniela Schlatzer
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
| | - A Ercüment Çiçek
- Department of Computer Engineering, Bilkent University, Ankara, Turkey.,Department of Computational Biology, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Mark R Chance
- Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA.,Department of Nutrition, Case Western Reserve University, Cleveland, OH, USA
| | - Mehmet Koyutürk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.,Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA
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4
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Basha O, Mauer O, Simonovsky E, Shpringer R, Yeger-Lotem E. ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues. Nucleic Acids Res 2020; 47:W242-W247. [PMID: 31114913 PMCID: PMC6602570 DOI: 10.1093/nar/gkz421] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/23/2019] [Accepted: 05/06/2019] [Indexed: 12/13/2022] Open
Abstract
ResponseNet v.3 is an enhanced version of ResponseNet, a web server that is designed to highlight signaling and regulatory pathways connecting user-defined proteins and genes by using the ResponseNet network optimization approach (http://netbio.bgu.ac.il/respnet). Users run ResponseNet by defining source and target sets of proteins, genes and/or microRNAs, and by specifying a molecular interaction network (interactome). The output of ResponseNet is a sparse, high-probability interactome subnetwork that connects the two sets, thereby revealing additional molecules and interactions that are involved in the studied condition. In recent years, massive efforts were invested in profiling the transcriptomes of human tissues, enabling the inference of human tissue interactomes. ResponseNet v.3 expands ResponseNet2.0 by harnessing ∼11,600 RNA-sequenced human tissue profiles made available by the Genotype-Tissue Expression consortium, to support context-specific analysis of 44 human tissues. Thus, ResponseNet v.3 allows users to illuminate the signaling and regulatory pathways potentially active in the context of a specific tissue, and to compare them with active pathways in other tissues. In the era of precision medicine, such analyses open the door for tissue- and patient-specific analyses of pathways and diseases.
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Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Omry Mauer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Eyal Simonovsky
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Rotem Shpringer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences.,National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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5
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Borgmann-Winter KE, Wang K, Bandyopadhyay S, Torshizi AD, Blair IA, Hahn CG. The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia. Schizophr Res 2020; 217:148-161. [PMID: 31416743 PMCID: PMC7500806 DOI: 10.1016/j.schres.2019.07.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 07/10/2019] [Accepted: 07/13/2019] [Indexed: 01/08/2023]
Abstract
The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.
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Affiliation(s)
- Karin E Borgmann-Winter
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America; Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States of America
| | - Kai Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States of America
| | - Sabyasachi Bandyopadhyay
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America
| | - Abolfazl Doostparast Torshizi
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States of America
| | - Ian A Blair
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Chang-Gyu Hahn
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104-3403, United States of America.
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6
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Bersanelli M, Mosca E, Milanesi L, Bazzani A, Castellani G. Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach. Sci Rep 2020; 10:2643. [PMID: 32060296 PMCID: PMC7021762 DOI: 10.1038/s41598-020-59036-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 11/22/2019] [Indexed: 11/13/2022] Open
Abstract
In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.
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Affiliation(s)
- Matteo Bersanelli
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy. .,National Institute for Nuclear Physics (INFN), Bologna, 40127, Italy.
| | - Ettore Mosca
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, 20090, Italy
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, 20090, Italy
| | - Armando Bazzani
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
| | - Gastone Castellani
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
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7
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Zolotareva O, Kleine M. A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2018-0069/jib-2018-0069.xml. [PMID: 31494632 PMCID: PMC7074139 DOI: 10.1515/jib-2018-0069] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 07/12/2019] [Indexed: 12/16/2022] Open
Abstract
Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.
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Affiliation(s)
- Olga Zolotareva
- Bielefeld University, Faculty of Technology and Center for Biotechnology, International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes" and Genome Informatics, Universitätsstraße 25, Bielefeld, Germany
| | - Maren Kleine
- Bielefeld University, Faculty of Technology, Bioinformatics/Medical Informatics Department, Universitätsstraße 25, Bielefeld, Germany
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8
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Park J, Hescott BJ, Slonim DK. Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease. Sci Rep 2019; 9:5863. [PMID: 30971743 PMCID: PMC6458310 DOI: 10.1038/s41598-019-42299-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 03/13/2019] [Indexed: 12/17/2022] Open
Abstract
Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We define the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. Using pathway centrality, we identify mediating pathways in three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)). We systematically evaluate the significance of all identified central pathways using genetic interactions. Mediating pathways shared by all three pulmonary disorders favor innate immune and inflammation-related processes, including toll-like receptor (TLR) signaling, PDGF- and angiotensin-regulated airway remodeling, the JAK-STAT signaling pathway, and interferon gamma. Disease-specific mediators, such as neurodevelopmental processes in BPD or adhesion molecules in COPD, are also highlighted. Some of our findings implicate pathways already in development as drug targets, while others may suggest new therapeutic approaches.
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Affiliation(s)
- Jisoo Park
- School of Medicine, University of California, San Diego, CA, 92093, USA.
| | - Benjamin J Hescott
- College of Computer and Information Science, Northeastern University, Boston, MA, 02115, USA
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA.
- Department of Immunology, Tufts University School of Medicine, Boston, MA, 02111, USA.
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9
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Seal A, Wild DJ. Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links. BMC Bioinformatics 2018; 19:265. [PMID: 30012095 PMCID: PMC6047136 DOI: 10.1186/s12859-018-2254-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/18/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download. RESULTS We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis. CONCLUSION The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.
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Affiliation(s)
- Abhik Seal
- School of Informatics and Computing, Indiana University Bloomington, Informatics West, Bloomington, 47408, Indiana, USA
| | - David J Wild
- School of Informatics and Computing, Indiana University Bloomington, Informatics West, Bloomington, 47408, Indiana, USA.
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10
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11
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Reconstructing the Molecular Function of Genetic Variation in Regulatory Networks. Genetics 2017; 207:1699-1709. [PMID: 29046401 DOI: 10.1534/genetics.117.300381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/11/2017] [Indexed: 11/18/2022] Open
Abstract
Over the past decade, genetic studies have recognized hundreds of polymorphic DNA loci called response QTLs (reQTLs) as potential contributors to interindividual variation in transcriptional responses to stimulations. Such reQTLs commonly affect the transduction of signals along the regulatory network that controls gene transcription. Identifying the pathways through which reQTLs perturb the underlying network has been a major challenge. Here, we present GEVIN ("Genome-wide Embedding of Variation In Networks"), a methodology that simultaneously identifies a reQTL and the particular pathway in which the reQTL affects downstream signal transduction along the network. Using synthetic data, we show that this algorithm outperforms existing pathway identification and reQTL identification methods. We applied GEVIN to the analysis of murine and human dendritic cells in response to pathogenic components. These analyses revealed significant reQTLs together with their perturbed Toll-like receptor signaling pathways. GEVIN thus offers a powerful framework that renders a comprehensive picture of disease-related DNA loci and their molecular functions within regulatory networks.
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12
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Shrestha R, Hodzic E, Sauerwald T, Dao P, Wang K, Yeung J, Anderson S, Vandin F, Haffari G, Collins CC, Sahinalp SC. HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology. Genome Res 2017; 27:1573-1588. [PMID: 28768687 PMCID: PMC5580716 DOI: 10.1101/gr.221218.117] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 07/06/2017] [Indexed: 12/12/2022]
Abstract
Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.
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Affiliation(s)
- Raunak Shrestha
- Bioinformatics Training Program, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4.,Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Ermin Hodzic
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
| | - Thomas Sauerwald
- Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| | - Phuong Dao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Kendric Wang
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Jake Yeung
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Shawn Anderson
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Gholamreza Haffari
- Faculty of Information Technology, Monash University, Melbourne 3800, Australia
| | - Colin C Collins
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6.,Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada V5Z 1M9
| | - S Cenk Sahinalp
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6.,School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6.,School of Informatics and Computing, Indiana University, Bloomington, Indiana 47408, USA
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13
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Chung MK, Hanson JL, Adluru N, Alexander AL, Davidson RJ, Pollak SD. Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging. Brain Connect 2017; 7:331-346. [PMID: 28657774 PMCID: PMC5567603 DOI: 10.1089/brain.2016.0481] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
In diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length, and FA values into the connectivity model. Using various node-degree-based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in postinstitutional settings before being adopted by families in the United States.
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Affiliation(s)
- Moo K Chung
- 1 Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin , Madison, Wisconsin.,2 Department of Biostatistics and Medical Informatics, University of Wisconsin , Madison, Wisconsin
| | - Jamie L Hanson
- 3 Department of Psychology, University of Pittsburgh , Pittsburgh, Pennsylvania.,4 Learning Research and Development Center, University of Pittsburgh , Pittsburgh, Pennsylvania
| | - Nagesh Adluru
- 1 Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin , Madison, Wisconsin
| | - Andrew L Alexander
- 1 Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin , Madison, Wisconsin.,5 Department of Medical Physics, University of Wisconsin , Madison, Wisconsin.,6 Department of Psychiatry, University of Wisconsin , Madison, Wisconsin
| | - Richard J Davidson
- 1 Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin , Madison, Wisconsin.,6 Department of Psychiatry, University of Wisconsin , Madison, Wisconsin.,7 Department of Psychology, University of Wisconsin , Madison, Wisconsin
| | - Seth D Pollak
- 6 Department of Psychiatry, University of Wisconsin , Madison, Wisconsin.,7 Department of Psychology, University of Wisconsin , Madison, Wisconsin.,8 Waisman Laboratory, University of Wisconsin , Madison, Wisconsin
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14
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Abstract
Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery - network propagation - suggesting that it is a powerful data transformation method of broad utility in genetic research.
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15
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Murthy MN, Blauwendraat C, Guelfi S, Hardy J, Lewis PA, Trabzuni D. Increased brain expression of GPNMB is associated with genome wide significant risk for Parkinson's disease on chromosome 7p15.3. Neurogenetics 2017; 18:121-133. [PMID: 28391543 PMCID: PMC5522530 DOI: 10.1007/s10048-017-0514-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 03/20/2017] [Indexed: 12/14/2022]
Abstract
Genome wide association studies (GWAS) for Parkinson’s disease (PD) have previously revealed a significant association with a locus on chromosome 7p15.3, initially designated as the glycoprotein non-metastatic melanoma protein B (GPNMB) locus. In this study, the functional consequences of this association on expression were explored in depth by integrating different expression quantitative trait locus (eQTL) datasets (Braineac, CAGEseq, GTEx, and Phenotype-Genotype Integrator (PheGenI)). Top risk SNP rs199347 eQTLs demonstrated increased expressions of GPNMB, KLHL7, and NUPL2 with the major allele (AA) in brain, with most significant eQTLs in cortical regions, followed by putamen. In addition, decreased expression of the antisense RNA KLHL7-AS1 was observed in GTEx. Furthermore, rs199347 is an eQTL with long non-coding RNA (AC005082.12) in human tissues other than brain. Interestingly, transcript-specific eQTLs in immune-related tissues (spleen and lymphoblastoid cells) for NUPL2 and KLHL7-AS1 were observed, which suggests a complex functional role of this eQTL in specific tissues, cell types at specific time points. Significantly increased expression of GPNMB linked to rs199347 was consistent across all datasets, and taken in combination with the risk SNP being located within the GPNMB gene, these results suggest that increased expression of GPNMB is the causative link explaining the association of this locus with PD. However, other transcript eQTLs and subsequent functional roles cannot be excluded. This highlights the importance of further investigations to understand the functional interactions between the coding genes, antisense, and non-coding RNA species considering the tissue and cell-type specificity to understand the underlying biological mechanisms in PD.
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Affiliation(s)
- Megha N Murthy
- Genetics and Genomics Laboratory, DOS in Genetics and Genomics, University of Mysore, Mysore, Karnataka, 570006, India
- School of Pharmacy, University of Reading, Whiteknights, Reading, RG6 6AP, UK
| | - Cornelis Blauwendraat
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Sebastian Guelfi
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - John Hardy
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Patrick A Lewis
- School of Pharmacy, University of Reading, Whiteknights, Reading, RG6 6AP, UK
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Daniah Trabzuni
- Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, 11211, Saudi Arabia.
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16
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Abstract
PathLinker is a graph-theoretic algorithm for reconstructing the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. We originally developed PathLinker to complement manual curation of signaling pathways, which is slow and painstaking. The method can be used in general to connect any set of sources to any set of targets in an interaction network. The app presented here makes the PathLinker functionality available to Cytoscape users. We present an example where we used PathLinker to compute and analyze the network of interactions connecting proteins that are perturbed by the drug lovastatin.
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Affiliation(s)
- Daniel P Gil
- Department of Computer Science, Virginia Tech, Blacksburg, USA
| | - Jeffrey N Law
- Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, USA.,ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, USA
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17
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Dubovenko A, Nikolsky Y, Rakhmatulin E, Nikolskaya T. Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform. Methods Mol Biol 2017; 1613:101-124. [PMID: 28849560 DOI: 10.1007/978-1-4939-7027-8_6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).
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Affiliation(s)
- Alexey Dubovenko
- Clarivate Analytics, 1500 Spring Garden Street, Fourth Floor, Philadelphia, PA, 19130, USA.
| | - Yuri Nikolsky
- Prosapia Genetics, Solana Beach, CA, 92075, USA.,School of Systems Biology, George Mason University, Fairfax, VA, USA
| | - Eugene Rakhmatulin
- Clarivate Analytics, 1500 Spring Garden Street, Fourth Floor, Philadelphia, PA, 19130, USA
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18
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Abstract
Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar disease phenotypes. Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying disease subtypes, and identifying drug targets. In this review, we discuss how networks can be used to help understand patient-to-patient variations and how one can leverage this variability to elucidate interactions between cancer drivers.
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Affiliation(s)
- Yoo-Ah Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Dong-Yeon Cho
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Teresa M. Przytycka
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America
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19
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Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C, Castellani G, Milanesi L. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 2016; 17 Suppl 2:15. [PMID: 26821531 PMCID: PMC4959355 DOI: 10.1186/s12859-015-0857-9] [Citation(s) in RCA: 221] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem. RESULTS AND CONCLUSIONS We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.
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Affiliation(s)
- Matteo Bersanelli
- Department of Physics and Astronomy, Universita' di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy. .,Institute of Biomedical Technologies - CNR, Via Fratelli Cervi 93, Segrate MI, 20090, Italy.
| | - Ettore Mosca
- Institute of Biomedical Technologies - CNR, Via Fratelli Cervi 93, Segrate MI, 20090, Italy.
| | - Daniel Remondini
- Department of Physics and Astronomy, Universita' di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy.
| | - Enrico Giampieri
- Department of Physics and Astronomy, Universita' di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy.
| | - Claudia Sala
- Department of Physics and Astronomy, Universita' di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy.
| | - Gastone Castellani
- Department of Physics and Astronomy, Universita' di Bologna, Via B. Pichat 6/2, Bologna, 40127, Italy.
| | - Luciano Milanesi
- Institute of Biomedical Technologies - CNR, Via Fratelli Cervi 93, Segrate MI, 20090, Italy.
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20
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Simões SN, Martins DC, Pereira CAB, Hashimoto RF, Brentani H. NERI: network-medicine based integrative approach for disease gene prioritization by relative importance. BMC Bioinformatics 2015; 16 Suppl 19:S9. [PMID: 26696568 PMCID: PMC4686785 DOI: 10.1186/1471-2105-16-s19-s9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease. Results The goal of this work is to develop a methodology that integrates PPI networks with disease specific data sources, such as GWAS and gene expression, to find genes more specific of a given complex disease. After the integration of PPI networks and gene expression data, the resulting network is used to connect genes related to the disease through the shortest paths that have the greatest concordance between their gene expressions. Both case and control expression data are used separately and, at the end, the most altered genes between the two conditions are selected. To evaluate the method, schizophrenia was adopted as case study. Conclusion Results show that the proposed method successfully retrieves differentially coexpressed genes in two conditions, while avoiding the bias from literature. Moreover we were able to achieve a greater concordance in the selection of important genes from different microarray studies of the same disease and to produce a more specific gene set related to the studied disease.
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21
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Valba OV, Nechaev SK, Sterken MG, Snoek LB, Kammenga JE, Vasieva OO. On predicting regulatory genes by analysis of functional networks in C. elegans. BioData Min 2015; 8:33. [PMID: 26535058 PMCID: PMC4631084 DOI: 10.1186/s13040-015-0066-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Accepted: 10/20/2015] [Indexed: 12/14/2022] Open
Abstract
Background Connectivity networks, which reflect multiple interactions between genes and proteins, possess not only a descriptive but also a predictive value, as new connections can be extrapolated and tested by means of computational analysis. Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit ‘guilt by association’ analysis. However predictive values of connectives of different types, that had their specific functional meaning and topological characteristics were not obvious, and have been addressed in this analysis. Methods eQTL data for 3 experimental C.elegans age groups were retrieved from WormQTL. WormNet has been used to obtain pair-wise gene interactions. The Shortest Path Function (SPF) has been adopted for statistical validation of the co-expressed gene clusters and for computational prediction of their potential gene expression regulators from a network context. A new SPF-based algorithm has been applied to genetic interactions sub-networks adjacent to the clusters of co-expressed genes for ranking the most likely gene expression regulators causal to eQTLs. Results We have demonstrated that known co-expression and genetic interactions between C. elegans genes can be complementary in predicting gene expression regulators. Several algorithms were compared in respect to their predictive potential in different network connectivity contexts. We found that genes associated with eQTLs are highly clustered in a C. elegans co-expression sub-network, and their adjacent genetic interactions provide the optimal functional connectivity environment for application of the new SPF-based algorithm. It was successfully tested in the reverse-prediction analysis on groups of genes with known regulators and applied to co-expressed genes and experimentally observed expression quantitative trait loci (eQTLs). Conclusions This analysis demonstrates differences in topology and connectivity of co-expression and genetic interactions sub-networks in WormNet. The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions. However the genetic interaction network can be used much more efficiently with the SPF method in prediction of potential regulators of gene expression. The developed method can be used for validation of functional significance of suggested eQTLs and a discovery of new regulatory modules.
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Affiliation(s)
- Olga V Valba
- Laboratory of Nematology, Wageningen University, Wageninge, Netherlands
| | - Sergei K Nechaev
- LPTMS, Université Paris Sud, Orsay Cedex, France ; National Research University, Higher School of Economics, Moscow, Russia
| | - Mark G Sterken
- LPTMS, Université Paris Sud, Orsay Cedex, France ; National Research University, Higher School of Economics, Moscow, Russia ; P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia
| | - L Basten Snoek
- Laboratory of Nematology, Wageningen University, Wageninge, Netherlands
| | - Jan E Kammenga
- Laboratory of Nematology, Wageningen University, Wageninge, Netherlands
| | - Olga O Vasieva
- Laboratory of Nematology, Wageningen University, Wageninge, Netherlands
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22
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Verbeke LPC, Van den Eynden J, Fierro AC, Demeester P, Fostier J, Marchal K. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration. PLoS One 2015. [PMID: 26217958 PMCID: PMC4517887 DOI: 10.1371/journal.pone.0133503] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival.
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Affiliation(s)
- Lieven P. C. Verbeke
- Department of Information Technology, Ghent University—iMinds, Ghent, Belgium
- * E-mail: (LPCV); (JF); (KM)
| | - Jimmy Van den Eynden
- Department of Information Technology, Ghent University—iMinds, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Ana Carolina Fierro
- Department of Information Technology, Ghent University—iMinds, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Piet Demeester
- Department of Information Technology, Ghent University—iMinds, Ghent, Belgium
| | - Jan Fostier
- Department of Information Technology, Ghent University—iMinds, Ghent, Belgium
- * E-mail: (LPCV); (JF); (KM)
| | - Kathleen Marchal
- Department of Information Technology, Ghent University—iMinds, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- * E-mail: (LPCV); (JF); (KM)
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23
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De Maeyer D, Weytjens B, Renkens J, De Raedt L, Marchal K. PheNetic: network-based interpretation of molecular profiling data. Nucleic Acids Res 2015; 43:W244-50. [PMID: 25878035 PMCID: PMC4489255 DOI: 10.1093/nar/gkv347] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 04/03/2015] [Indexed: 12/17/2022] Open
Abstract
Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.
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Affiliation(s)
- Dries De Maeyer
- Dept. of Microbial and Molecular Systems, KULeuven, Leuven, 3000, Belgium Dept. of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, 9052, Belgium
| | - Bram Weytjens
- Dept. of Microbial and Molecular Systems, KULeuven, Leuven, 3000, Belgium Dept. of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, 9052, Belgium
| | - Joris Renkens
- Dept. of Computer Science, KULeuven, Leuven, 3000, Belgium
| | - Luc De Raedt
- Dept. of Computer Science, KULeuven, Leuven, 3000, Belgium
| | - Kathleen Marchal
- Dept. of Microbial and Molecular Systems, KULeuven, Leuven, 3000, Belgium Dept. of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, 9052, Belgium Dept. of Plant Biotechnology and Bioinformatics, U.Ghent, Ghent, 9052, Belgium
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24
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Wilentzik R, Gat-Viks I. A statistical framework for revealing signaling pathways perturbed by DNA variants. Nucleic Acids Res 2015; 43:e74. [PMID: 25765646 PMCID: PMC4477643 DOI: 10.1093/nar/gkv203] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 02/28/2015] [Indexed: 12/03/2022] Open
Abstract
Much of the inter-individual variation in gene expression is triggered via perturbations of signaling networks by DNA variants. We present a novel probabilistic approach for identifying the particular pathways by which DNA variants perturb the signaling network. Our procedure, called PINE, relies on a systematic integration of established biological knowledge of signaling networks with data on transcriptional responses to various experimental conditions. Unlike previous approaches, PINE provides statistical aspects that are critical for prioritizing hypotheses for followup experiments. Using simulated data, we show that higher accuracy is attained with PINE than with existing methods. We used PINE to analyze transcriptional responses of immune dendritic cells to several pathogenic stimulations. PINE identified statistically significant genetic perturbations in the pathogen-sensing signaling network, suggesting previously uncharacterized regulatory mechanisms for functional DNA variants.
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Affiliation(s)
- Roni Wilentzik
- Department of Cell Research and Immunology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, 6997801 Tel Aviv, Israel
| | - Irit Gat-Viks
- Department of Cell Research and Immunology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, 6997801 Tel Aviv, Israel
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25
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Clément-Ziza M, Marsellach FX, Codlin S, Papadakis MA, Reinhardt S, Rodríguez-López M, Martin S, Marguerat S, Schmidt A, Lee E, Workman CT, Bähler J, Beyer A. Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast. Mol Syst Biol 2014; 10:764. [PMID: 25432776 PMCID: PMC4299605 DOI: 10.15252/msb.20145123] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Our current understanding of how natural genetic variation affects gene expression beyond
well-annotated coding genes is still limited. The use of deep sequencing technologies for the study
of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we
generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL
study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal
effects (trans-eQTLs) greatly exceeds the number of local effects
(cis-eQTLs) and that non-coding RNAs are as likely to be affected by eQTLs as
protein-coding RNAs. We identified a genetic variation of swc5 that modifies the
levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this
effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains,
methods, and datasets generated here provide a rich resource for future studies.
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Affiliation(s)
- Mathieu Clément-Ziza
- Biotechnology Centre, Technische Universität Dresden, Dresden, Germany Cologne Cluster of Excellence in Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Francesc X Marsellach
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | - Sandra Codlin
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | - Manos A Papadakis
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
| | - Susanne Reinhardt
- Biotechnology Centre, Technische Universität Dresden, Dresden, Germany
| | - María Rodríguez-López
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | - Stuart Martin
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | - Samuel Marguerat
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | | | - Eunhye Lee
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | - Christopher T Workman
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
| | - Jürg Bähler
- Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
| | - Andreas Beyer
- Biotechnology Centre, Technische Universität Dresden, Dresden, Germany Cologne Cluster of Excellence in Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
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26
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Chasman D, Ho YH, Berry DB, Nemec CM, MacGilvray ME, Hose J, Merrill AE, Lee MV, Will JL, Coon JJ, Ansari AZ, Craven M, Gasch AP. Pathway connectivity and signaling coordination in the yeast stress-activated signaling network. Mol Syst Biol 2014; 10:759. [PMID: 25411400 PMCID: PMC4299600 DOI: 10.15252/msb.20145120] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet
knowledge of the complete stress-activated regulatory network as well as design principles for
signal integration remains incomplete. We developed an experimental and computational approach to
integrate available protein interaction data with gene fitness contributions, mutant transcriptome
profiles, and phospho-proteome changes in cells responding to salt stress, to infer the
salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions
by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and
pointing to previously unknown ‘hubs’ of signal integration. We exploited these
predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of
RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related
transcripts. We find that the orthologous human network is enriched for cancer-causing genes,
underscoring the importance of the subnetwork's predictions in understanding stress
biology.
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Affiliation(s)
- Deborah Chasman
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Yi-Hsuan Ho
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - David B Berry
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Corey M Nemec
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - James Hose
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna E Merrill
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - M Violet Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jessica L Will
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Department of Biological Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Aseem Z Ansari
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark Craven
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Audrey P Gasch
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
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27
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Chasman D, Gancarz B, Hao L, Ferris M, Ahlquist P, Craven M. Inferring host gene subnetworks involved in viral replication. PLoS Comput Biol 2014; 10:e1003626. [PMID: 24874113 PMCID: PMC4038467 DOI: 10.1371/journal.pcbi.1003626] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 02/06/2014] [Indexed: 12/16/2022] Open
Abstract
Systematic, genome-wide loss-of-function experiments can be used to identify host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell. We present an approach that combines an integer linear program and a diffusion kernel method to infer the pathways through which those host factors modulate viral replication. The inputs to the method are a set of viral phenotypes observed in single-host-gene mutants and a background network consisting of a variety of host intracellular interactions. The output is an ensemble of subnetworks that provides a consistent explanation for the measured phenotypes, predicts which unassayed host factors modulate the virus, and predicts which host factors are the most direct interfaces with the virus. We infer host-virus interaction subnetworks using data from experiments screening the yeast genome for genes modulating the replication of two RNA viruses. Because a gold-standard network is unavailable, we assess the predicted subnetworks using both computational and qualitative analyses. We conduct a cross-validation experiment in which we predict whether held-aside test genes have an effect on viral replication. Our approach is able to make high-confidence predictions more accurately than several baselines, and about as well as the best baseline, which does not infer mechanistic pathways. We also examine two kinds of predictions made by our method: which host factors are nearest to a direct interaction with a viral component, and which unassayed host genes are likely to be involved in viral replication. Multiple predictions are supported by recent independent experimental data, or are components or functional partners of confirmed relevant complexes or pathways. Integer program code, background network data, and inferred host-virus subnetworks are available at http://www.biostat.wisc.edu/~craven/chasman_host_virus/.
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Affiliation(s)
- Deborah Chasman
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Brandi Gancarz
- Luminex Corporation, Madison, Wisconsin, United States of America
- Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Linhui Hao
- Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Howard Hughes Medical Institute, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Michael Ferris
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Paul Ahlquist
- Institute for Molecular Virology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Howard Hughes Medical Institute, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Morgridge Institute for Research, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Mark Craven
- Department of Computer Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
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28
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Teschendorff AE, Sollich P, Kuehn R. Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 2014; 67:282-93. [PMID: 24675401 DOI: 10.1016/j.ymeth.2014.03.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Revised: 01/03/2014] [Accepted: 03/13/2014] [Indexed: 12/27/2022] Open
Abstract
A key challenge in systems biology is the elucidation of the underlying principles, or fundamental laws, which determine the cellular phenotype. Understanding how these fundamental principles are altered in diseases like cancer is important for translating basic scientific knowledge into clinical advances. While significant progress is being made, with the identification of novel drug targets and treatments by means of systems biological methods, our fundamental systems level understanding of why certain treatments succeed and others fail is still lacking. We here advocate a novel methodological framework for systems analysis and interpretation of molecular omic data, which is based on statistical mechanical principles. Specifically, we propose the notion of cellular signalling entropy (or uncertainty), as a novel means of analysing and interpreting omic data, and more fundamentally, as a means of elucidating systems-level principles underlying basic biology and disease. We describe the power of signalling entropy to discriminate cells according to differentiation potential and cancer status. We further argue the case for an empirical cellular entropy-robustness correlation theorem and demonstrate its existence in cancer cell line drug sensitivity data. Specifically, we find that high signalling entropy correlates with drug resistance and further describe how entropy could be used to identify the achilles heels of cancer cells. In summary, signalling entropy is a deep and powerful concept, based on rigorous statistical mechanical principles, which, with improved data quality and coverage, will allow a much deeper understanding of the systems biological principles underlying normal and disease physiology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, China; Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK.
| | - Peter Sollich
- Department of Mathematics, King's College London, London WC2R 2LS, UK
| | - Reimer Kuehn
- Department of Mathematics, King's College London, London WC2R 2LS, UK
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29
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Yang TH, Wang CC, Wang YC, Wu WS. YTRP: a repository for yeast transcriptional regulatory pathways. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau014. [PMID: 24608172 PMCID: PMC3948430 DOI: 10.1093/database/bau014] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Regulatory targets of transcription factors (TFs) can be identified by the TF perturbation experiments, which reveal the expression changes owing to the perturbation (deletion or overexpression) of TFs. But the identified targets of a given TF consist of both direct and indirect regulatory targets. It has been shown that most of the TFPE-identified regulatory targets are indirect, indicating that TF-gene regulation is mainly through transcriptional regulatory pathways (TRPs) consisting of intermediate TFs. Without identification of these TRPs, it is not easy to understand how a TF regulates its indirect targets. Because there is no such database depositing the potential TRPs for Saccharomyces cerevisiae now, this motivates us to construct the YTRP (Yeast Transcriptional Regulatory Pathway) database. For each TF-gene regulatory pair under different experimental conditions, all possible TRPs in two underlying networks (constructed using experimentally verified TF-gene binding pairs and TF-gene regulatory pairs from the literature) for the specified experimental conditions were automatically enumerated by TRP mining procedures developed from the graph theory. The enumerated TRPs of a TF-gene regulatory pair provide experimentally testable hypotheses for the molecular mechanisms behind a TF and its regulatory target. YTRP is available online at http://cosbi3.ee.ncku.edu.tw/YTRP/. We believe that the TRPs deposited in this database will greatly improve the usefulness of TFPE data for yeast biologists to study the regulatory mechanisms between a TF and its knocked-out targets. Database URL: http://cosbi3.ee.ncku.edu.tw/YTRP/
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan and Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
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30
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Lan A, Ziv-Ukelson M, Yeger-Lotem E. A context-sensitive framework for the analysis of human signalling pathways in molecular interaction networks. Bioinformatics 2013; 29:i210-6. [PMID: 23812986 PMCID: PMC3694656 DOI: 10.1093/bioinformatics/btt240] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
MOTIVATION A major challenge in systems biology is to reveal the cellular pathways that give rise to specific phenotypes and behaviours. Current techniques often rely on a network representation of molecular interactions, where each node represents a protein or a gene and each interaction is assigned a single static score. However, the use of single interaction scores fails to capture the tendency of proteins to favour different partners under distinct cellular conditions. RESULTS Here, we propose a novel context-sensitive network model, in which genes and protein nodes are assigned multiple contexts based on their gene ontology annotations, and their interactions are associated with multiple context-sensitive scores. Using this model, we developed a new approach and a corresponding tool, ContextNet, based on a dynamic programming algorithm for identifying signalling paths linking proteins to their downstream target genes. ContextNet finds high-ranking context-sensitive paths in the interactome, thereby revealing the intermediate proteins in the path and their path-specific contexts. We validated the model using 18 348 manually curated cellular paths derived from the SPIKE database. We next applied our framework to elucidate the responses of human primary lung cells to influenza infection. Top-ranking paths were much more likely to contain infection-related proteins, and this likelihood was highly correlated with path score. Moreover, the contexts assigned by the algorithm pointed to putative, as well as previously known responses to viral infection. Thus, context sensitivity is an important extension to current network biology models and can be efficiently used to elucidate cellular response mechanisms. AVAILABILITY ContextNet is publicly available at http://netbio.bgu.ac.il/ContextNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander Lan
- Department of Computer Science, National Center for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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31
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Leiserson MDM, Eldridge JV, Ramachandran S, Raphael BJ. Network analysis of GWAS data. Curr Opin Genet Dev 2013; 23:602-10. [PMID: 24287332 PMCID: PMC3867794 DOI: 10.1016/j.gde.2013.09.003] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2013] [Revised: 09/19/2013] [Accepted: 09/23/2013] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) identify genetic variants that distinguish a control population from a population with a specific trait. Two challenges in GWAS are: (1) identification of the causal variant within a longer haplotype that is associated with the trait; (2) identification of causal variants for polygenic traits that are caused by variants in multiple genes within a pathway. We review recent methods that use information in protein-protein and protein-DNA interaction networks to address these two challenges.
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Affiliation(s)
- Mark D M Leiserson
- Department of Computer Science, Brown University, Providence, RI 02912, United States; Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States
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32
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Halldórsson BV, Sharan R. Network-based interpretation of genomic variation data. J Mol Biol 2013; 425:3964-9. [PMID: 23886866 DOI: 10.1016/j.jmb.2013.07.026] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 07/02/2013] [Accepted: 07/16/2013] [Indexed: 02/02/2023]
Abstract
Advances in sequencing technologies are allowing genome-wide association studies at an ever-growing scale. The interpretation of these studies requires dealing with statistical and combinatorial challenges, owing to the multi-factorial nature of human diseases and the huge space of genomic markers that are being monitored. Recently, it was proposed that using protein-protein interaction network information could help in tackling these challenges by restricting attention to markers or combinations of markers that map to close proteins in the network. In this review, we survey techniques for integrating genomic variation data with network information to improve our understanding of complex diseases and reveal meaningful associations.
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Affiliation(s)
- Bjarni V Halldórsson
- School of Science and Engineering, Reykjavík University, 101 Reykjavík, Iceland.
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33
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Tuncbag N, Braunstein A, Pagnani A, Huang SSC, Chayes J, Borgs C, Zecchina R, Fraenkel E. Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J Comput Biol 2013; 20:124-36. [PMID: 23383998 DOI: 10.1089/cmb.2012.0092] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Signaling and regulatory networks are essential for cells to control processes such as growth, differentiation, and response to stimuli. Although many "omic" data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases and to propose new therapeutic strategies. We overcome these problems and use "omic" data to reconstruct simultaneously multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees, each of which is rooted in a different cell-surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies. Although the algorithm was not provided with any information about the phosphorylation status of receptors, it identifies a small set of clinically relevant receptors among hundreds present in the interactome.
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Affiliation(s)
- Nurcan Tuncbag
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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34
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Rajagopalan P, Kasif S, Murali T. Systems Biology Characterization of Engineered Tissues. Annu Rev Biomed Eng 2013; 15:55-70. [DOI: 10.1146/annurev-bioeng-071811-150120] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Padmavathy Rajagopalan
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24060;
- School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, Virginia 24060
- ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, Virginia 24060
| | - Simon Kasif
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
| | - T.M. Murali
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24060
- ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, Virginia 24060
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35
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Abstract
High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
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Affiliation(s)
- Bonnie Berger
- Department of Mathematics and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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36
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Huang Y, Wuchty S, Przytycka TM. eQTL Epistasis - Challenges and Computational Approaches. Front Genet 2013; 4:51. [PMID: 23755066 PMCID: PMC3668133 DOI: 10.3389/fgene.2013.00051] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2012] [Accepted: 03/19/2013] [Indexed: 01/18/2023] Open
Abstract
The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods.
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Affiliation(s)
- Yang Huang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD, USA
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37
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Kim YA, Przytycka TM. Bridging the Gap between Genotype and Phenotype via Network Approaches. Front Genet 2013; 3:227. [PMID: 23755063 PMCID: PMC3668153 DOI: 10.3389/fgene.2012.00227] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Accepted: 10/10/2012] [Indexed: 11/15/2022] Open
Abstract
In the last few years we have witnessed tremendous progress in detecting associations between genetic variations and complex traits. While genome-wide association studies have been able to discover genomic regions that may influence many common human diseases, these discoveries created an urgent need for methods that extend the knowledge of genotype-phenotype relationships to the level of the molecular mechanisms behind them. To address this emerging need, computational approaches increasingly utilize a pathway-centric perspective. These new methods often utilize known or predicted interactions between genes and/or gene products. In this review, we survey recently developed network based methods that attempt to bridge the genotype-phenotype gap. We note that although these methods help narrow the gap between genotype and phenotype relationships, these approaches alone cannot provide the precise details of underlying mechanisms and current research is still far from closing the gap.
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Affiliation(s)
- Yoo-Ah Kim
- National Center for Biotechnology Information, National Institutes of Health, National Library of Medicine Bethesda, MD, USA
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38
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De Maeyer D, Renkens J, Cloots L, De Raedt L, Marchal K. PheNetic: network-based interpretation of unstructured gene lists in E. coli. MOLECULAR BIOSYSTEMS 2013; 9:1594-603. [PMID: 23591551 DOI: 10.1039/c3mb25551d] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network. PheNetic is available at .
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Affiliation(s)
- Dries De Maeyer
- Center of Microbial and Plant Genetics, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
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39
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Verbeke LPC, Cloots L, Demeester P, Fostier J, Marchal K. EPSILON: an eQTL prioritization framework using similarity measures derived from local networks. ACTA ACUST UNITED AC 2013; 29:1308-16. [PMID: 23595663 DOI: 10.1093/bioinformatics/btt142] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
MOTIVATION When genomic data are associated with gene expression data, the resulting expression quantitative trait loci (eQTL) will likely span multiple genes. eQTL prioritization techniques can be used to select the most likely causal gene affecting the expression of a target gene from a list of candidates. As an input, these techniques use physical interaction networks that often contain highly connected genes and unreliable or irrelevant interactions that can interfere with the prioritization process. We present EPSILON, an extendable framework for eQTL prioritization, which mitigates the effect of highly connected genes and unreliable interactions by constructing a local network before a network-based similarity measure is applied to select the true causal gene. RESULTS We tested the new method on three eQTL datasets derived from yeast data using three different association techniques. A physical interaction network was constructed, and each eQTL in each dataset was prioritized using the EPSILON approach: first, a local network was constructed using a k-trials shortest path algorithm, followed by the calculation of a network-based similarity measure. Three similarity measures were evaluated: random walks, the Laplacian Exponential Diffusion kernel and the Regularized Commute-Time kernel. The aim was to predict knockout interactions from a yeast knockout compendium. EPSILON outperformed two reference prioritization methods, random assignment and shortest path prioritization. Next, we found that using a local network significantly increased prioritization performance in terms of predicted knockout pairs when compared with using exactly the same network similarity measures on the global network, with an average increase in prioritization performance of 8 percentage points (P < 10(-5)). AVAILABILITY The physical interaction network and the source code (Matlab/C++) of our implementation can be downloaded from http://bioinformatics.intec.ugent.be/epsilon. CONTACT lieven.verbeke@intec.ugent.be, kamar@psb.ugent.be, jan.fostier@intec.ugent.be SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lieven P C Verbeke
- Department of Information Technology, Ghent University - iMinds, 9050 Gent, Belgium.
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Gosline SJC, Spencer SJ, Ursu O, Fraenkel E. SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets. Integr Biol (Camb) 2013; 4:1415-27. [PMID: 23060147 DOI: 10.1039/c2ib20072d] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein-protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT.
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Affiliation(s)
- Sara J C Gosline
- Dept. of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Shih YK, Parthasarathy S. A single source k-shortest paths algorithm to infer regulatory pathways in a gene network. ACTA ACUST UNITED AC 2013; 28:i49-58. [PMID: 22689778 PMCID: PMC3371844 DOI: 10.1093/bioinformatics/bts212] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motivation: Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene. Results: In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines. Availability: All data and codes are freely available upon request. Contact:srini@cse.ohio-state.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu-Keng Shih
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA
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Lee Y, Li H, Li J, Rebman E, Achour I, Regan KE, Gamazon ER, Chen JL, Yang XH, Cox NJ, Lussier YA. Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases. J Am Med Inform Assoc 2013; 20:619-29. [PMID: 23355459 PMCID: PMC3721168 DOI: 10.1136/amiajnl-2012-001519] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. METHODS We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. RESULTS Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p < 2.24 × 10(-12)). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland-USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). CONCLUSION Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.
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Affiliation(s)
- Younghee Lee
- Center for Biomedical Informatics, The University of Chicago, Chicago, Illinois, USA
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Abstract
Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.
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Affiliation(s)
- Dong-Yeon Cho
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland, United States of America
| | - Yoo-Ah Kim
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland, United States of America
| | - Teresa M. Przytycka
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland, United States of America
- * E-mail:
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Wang PI, Hwang S, Kincaid RP, Sullivan CS, Lee I, Marcotte EM. RIDDLE: reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network. Genome Biol 2012; 13:R125. [PMID: 23268829 PMCID: PMC4056375 DOI: 10.1186/gb-2012-13-12-r125] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 12/26/2012] [Indexed: 01/08/2023] Open
Abstract
The growing availability of large-scale functional networks has promoted the development of many successful techniques for predicting functions of genes. Here we extend these network-based principles and techniques to functionally characterize whole sets of genes. We present RIDDLE (Reflective Diffusion and Local Extension), which uses well developed guilt-by-association principles upon a human gene network to identify associations of gene sets. RIDDLE is particularly adept at characterizing sets with no annotations, a major challenge where most traditional set analyses fail. Notably, RIDDLE found microRNA-450a to be strongly implicated in ocular diseases and development. A web application is available at http://www.functionalnet.org/RIDDLE.
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Tu Z, Keller MP, Zhang C, Rabaglia ME, Greenawalt DM, Yang X, Wang IM, Dai H, Bruss MD, Lum PY, Zhou YP, Kemp DM, Kendziorski C, Yandell BS, Attie AD, Schadt EE, Zhu J. Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets. PLoS Genet 2012; 8:e1003107. [PMID: 23236292 PMCID: PMC3516550 DOI: 10.1371/journal.pgen.1003107] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2012] [Accepted: 10/04/2012] [Indexed: 01/20/2023] Open
Abstract
Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptinob/ob mutation (Lepob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes. Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.
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Affiliation(s)
- Zhidong Tu
- Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, New York, New York, United States of America
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, United States of America
| | - Mark P. Keller
- Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Chunsheng Zhang
- Merck Research Laboratories, Boston, Massachusetts, United States of America
| | - Mary E. Rabaglia
- Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | | | - Xia Yang
- Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, California, United States of America
| | - I-Ming Wang
- Merck Research Laboratories, Rahway, New Jersey, United States of America
| | - Hongyue Dai
- Merck Research Laboratories, Boston, Massachusetts, United States of America
| | - Matthew D. Bruss
- Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Pek Y. Lum
- Department of Genetics, Rosetta Inpharmatics, Merck, Seattle, Washington, United States of America
| | - Yun-Ping Zhou
- Merck Research Laboratories, Rahway, New Jersey, United States of America
| | - Daniel M. Kemp
- Merck Research Laboratories, Rahway, New Jersey, United States of America
| | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Brian S. Yandell
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Alan D. Attie
- Department of Biochemistry, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Eric E. Schadt
- Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, New York, New York, United States of America
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, United States of America
- Graduate School of Biological Sciences, Mount Sinai School of Medicine, New York, New York, United States of America
- Pacific Biosciences, Menlo Park, California, United States of America
| | - Jun Zhu
- Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, New York, New York, United States of America
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, United States of America
- Graduate School of Biological Sciences, Mount Sinai School of Medicine, New York, New York, United States of America
- * E-mail:
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Abo R, Jenkins GD, Wang L, Fridley BL. Identifying the genetic variation of gene expression using gene sets: application of novel gene Set eQTL approach to PharmGKB and KEGG. PLoS One 2012; 7:e43301. [PMID: 22905253 PMCID: PMC3419168 DOI: 10.1371/journal.pone.0043301] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2012] [Accepted: 07/19/2012] [Indexed: 11/18/2022] Open
Abstract
Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set - expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.
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Affiliation(s)
- Ryan Abo
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Gregory D. Jenkins
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Liewei Wang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Brooke L. Fridley
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- * E-mail:
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Ackermann M, Clément-Ziza M, Michaelson JJ, Beyer A. Teamwork: improved eQTL mapping using combinations of machine learning methods. PLoS One 2012; 7:e40916. [PMID: 22911718 PMCID: PMC3404069 DOI: 10.1371/journal.pone.0040916] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Accepted: 06/14/2012] [Indexed: 12/30/2022] Open
Abstract
Expression quantitative trait loci (eQTL) mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee). Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.
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Affiliation(s)
- Marit Ackermann
- Biotechnology Center, Technical University Dresden, Dresden, Germany
| | | | | | - Andreas Beyer
- Biotechnology Center, Technical University Dresden, Dresden, Germany
- Center for Regenerative Therapy Dresden, Dresden, Germany
- * E-mail:
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Doncheva NT, Kacprowski T, Albrecht M. Recent approaches to the prioritization of candidate disease genes. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012; 4:429-42. [PMID: 22689539 DOI: 10.1002/wsbm.1177] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High-throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time-consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow-up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene-disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods.
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Tuncbag N, McCallum S, Huang SSC, Fraenkel E. SteinerNet: a web server for integrating 'omic' data to discover hidden components of response pathways. Nucleic Acids Res 2012; 40:W505-9. [PMID: 22638579 PMCID: PMC3394335 DOI: 10.1093/nar/gks445] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
High-throughput technologies including transcriptional profiling, proteomics and reverse genetics screens provide detailed molecular descriptions of cellular responses to perturbations. However, it is difficult to integrate these diverse data to reconstruct biologically meaningful signaling networks. Previously, we have established a framework for integrating transcriptional, proteomic and interactome data by searching for the solution to the prize-collecting Steiner tree problem. Here, we present a web server, SteinerNet, to make this method available in a user-friendly format for a broad range of users with data from any species. At a minimum, a user only needs to provide a set of experimentally detected proteins and/or genes and the server will search for connections among these data from the provided interactomes for yeast, human, mouse, Drosophila melanogaster and Caenorhabditis elegans. More advanced users can upload their own interactome data as well. The server provides interactive visualization of the resulting optimal network and downloadable files detailing the analysis and results. We believe that SteinerNet will be useful for researchers who would like to integrate their high-throughput data for a specific condition or cellular response and to find biologically meaningful pathways. SteinerNet is accessible at http://fraenkel.mit.edu/steinernet.
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Affiliation(s)
- Nurcan Tuncbag
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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