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Halu A, Wang JG, Iwata H, Mojcher A, Abib AL, Singh SA, Aikawa M, Sharma A. Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation. eLife 2018; 7:37059. [PMID: 30303482 PMCID: PMC6179386 DOI: 10.7554/elife.37059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/30/2018] [Indexed: 02/06/2023] Open
Abstract
The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling. When human cells or tissues are injured, the body triggers a response known as inflammation to repair the damage and protect itself from further harm. However, if the same issue keeps recurring, the tissues become inflamed for longer periods of time, which may ultimately lead to health problems. This is what could be happening in cardiovascular diseases, where long-term inflammation could damage the heart and blood vessels. Many different proteins interact with each other to control inflammation; gaining an insight into the nature of these interactions could help to pinpoint the role of each molecular actor. Researchers have used a combination of unbiased, large-scale experimental and computational approaches to develop the interactome, a map of the known interactions between all proteins in humans. However, interactions between proteins can change between cell types, or during disease. Here, Halu et al. aimed to refine the human interactome and identify new proteins involved in inflammation, especially in the context of cardiovascular disease. Cells called macrophages produce signals that trigger inflammation whey they detect damage in other cells or tissues. The experiments used a technique called proteomics to measure the amounts of all the proteins in human macrophages. Combining these data with the human interactome made it possible to predict new links between proteins known to have a role in inflammation and other proteins in the interactome. Further analysis using other sets of data from macrophages helped identify two new candidate proteins – GBP1 and WARS – that may promote inflammation. Halu et al. then used a genetic approach to deactivate the genes and decrease the levels of these two proteins in macrophages, which caused the signals that encourage inflammation to drop. These findings suggest that GBP1 and WARS regulate the activity of macrophages to promote inflammation. The two proteins could therefore be used as drug targets to treat cardiovascular diseases and other disorders linked to inflammation, but further studies will be needed to precisely dissect how GBP1 and WARS work in humans.
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Affiliation(s)
- Arda Halu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.,Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Jian-Guo Wang
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Hiroshi Iwata
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Alexander Mojcher
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Ana Luisa Abib
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Sasha A Singh
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
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Sharma A, Kitsak M, Cho MH, Ameli A, Zhou X, Jiang Z, Crapo JD, Beaty TH, Menche J, Bakke PS, Santolini M, Silverman EK. Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module. Sci Rep 2018; 8:14439. [PMID: 30262855 PMCID: PMC6160419 DOI: 10.1038/s41598-018-32173-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 08/20/2018] [Indexed: 12/21/2022] Open
Abstract
The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.
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Affiliation(s)
- Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. .,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. .,Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA. .,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
| | - Maksim Kitsak
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Asher Ameli
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Department of Physics, Northeastern University, Boston, MA, 02115, United States
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Zhiqiang Jiang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - James D Crapo
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jörg Menche
- Department of Bioinformatics, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090, Vienna, Austria
| | - Per S Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marc Santolini
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. .,Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA. .,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Plubell DL, Fenton AM, Wilmarth PA, Bergstrom P, Zhao Y, Minnier J, Heinecke JW, Yang X, Pamir N. GM-CSF driven myeloid cells in adipose tissue link weight gain and insulin resistance via formation of 2-aminoadipate. Sci Rep 2018; 8:11485. [PMID: 30065264 PMCID: PMC6068153 DOI: 10.1038/s41598-018-29250-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 07/03/2018] [Indexed: 02/07/2023] Open
Abstract
In a GM-CSF driven myeloid cell deficient mouse model (Csf2−/−) that has preserved insulin sensitivity despite increased adiposity, we used unbiased three-dimensional integration of proteome profiles, metabolic profiles, and gene regulatory networks to understand adipose tissue proteome-wide changes and their metabolic implications. Multi-dimensional liquid chromatography mass spectrometry and extended multiplex mass labeling was used to analyze proteomes of epididymal adipose tissues isolated from Csf2+/+ and Csf2−/− mice that were fed low fat, high fat, or high fat plus cholesterol diets for 8 weeks. The metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, phospholipids, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet for both genotypes, while mice lacking Csf2 were protected from insulin resistance. Regardless of diet, 30 mostly mitochondrial, branch chain amino acids (BCAA), and lysine metabolism proteins were altered between Csf2−/− and Csf2+/+ mice (FDR < 0.05). Lack of GM-CSF driven myeloid cells lead to reduced adipose tissue 2-oxoglutarate dehydrogenase complex (DHTKD1) levels and subsequent increase in plasma 2-aminoadipate (2-AA) levels, both of which are reported to correlate with insulin resistance. Tissue DHTKD1 levels were >4-fold upregulated and plasma 2-AA levels were >2 fold reduced in Csf2−/− mice (p < 0.05). GM-CSF driven myeloid cells link peripheral insulin sensitivity to adiposity via lysine metabolism involving DHTKD1/2-AA axis in a diet independent manner.
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Affiliation(s)
- Deanna L Plubell
- Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA
| | - Alexandra M Fenton
- Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA
| | - Phillip A Wilmarth
- Proteomics Shared Resource, Oregon Health & Science University, Portland, OR, USA
| | - Paige Bergstrom
- Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA
| | - Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Jessica Minnier
- Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA
| | - Jay W Heinecke
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Nathalie Pamir
- Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.
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Heinig M. Using Gene Expression to Annotate Cardiovascular GWAS Loci. Front Cardiovasc Med 2018; 5:59. [PMID: 29922679 PMCID: PMC5996083 DOI: 10.3389/fcvm.2018.00059] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Accepted: 05/15/2018] [Indexed: 01/27/2023] Open
Abstract
Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions harbor regulatory sequences. Thus the study of genetic variants associated with transcription—so called expression quantitative trait loci—has emerged as a promising approach to identify regulatory sequence variants. The genes and pathways they control constitute candidate causal drivers at cardiovascular risk loci. This review provides an overview of the expression quantitative trait loci resources available for cardiovascular genetics research and the most commonly used approaches for candidate gene identification.
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Affiliation(s)
- Matthias Heinig
- Institute of Computational Biology, Helmholtz Zentrum München German Research Center for Environmental Health, Neuherberg, Germany.,Department of Informatics, Technical University of Munich, Munich, Germany
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Integrative gene network analysis identifies key signatures, intrinsic networks and host factors for influenza virus A infections. NPJ Syst Biol Appl 2017; 3:35. [PMID: 29214055 PMCID: PMC5712526 DOI: 10.1038/s41540-017-0036-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 10/17/2017] [Accepted: 11/07/2017] [Indexed: 12/13/2022] Open
Abstract
Influenza A virus, with the limited coding capacity of 10–14 proteins, requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system, but also provides further targets for antiviral drug development. To uncover novel pathways and key targets of influenza infection, we assembled a large amount of data from 12 cell-based gene-expression studies of influenza infection for an integrative network analysis. We systematically identified differentially expressed genes and gene co-expression networks induced by influenza infection. We revealed the dedicator of cytokinesis 5 (DOCK5) played potentially an important role for influenza virus replication. CRISPR/Cas9 knockout of DOCK5 reduced influenza virus replication, indicating that DOCK5 is a key regulator for the viral life cycle. DOCK5’s targets determined by the DOCK5 knockout experiments strongly validated the predicted gene signatures and networks. This study systematically uncovered and validated fundamental patterns of molecular responses, intrinsic structures of gene co-regulation, and novel key targets in influenza virus infection. Molecular response to influenza infection involves a large number of interacting pathways in the form of complex molecular networks. Most studies on influenza infection have largely focused on testing specific molecules and hypotheses with limited data. Therefore, a global picture of molecular interactions in influenza infection is missing. In this study, we performed an integrative network analysis on a large amount of data from 12 cell-based gene expression studies of influenza infections. By combining differential expression, co-expression networks, and gene knockout experiments, we uncovered and validated fundamental patterns of molecular responses, intrinsic structures of gene co-regulation, and novel key targets in influenza infection. Our findings pave the way for other functional investigations into identifying novel therapeutic targets against influenza infection.
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Bellazzi R, Engel F, Ferrazzi F. Gene network analysis: from heart development to cardiac therapy. Thromb Haemost 2017; 113:522-31. [DOI: 10.1160/th14-06-0483] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Accepted: 08/14/2014] [Indexed: 12/31/2022]
Abstract
SummaryNetworks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.
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McKenzie AT, Moyon S, Wang M, Katsyv I, Song WM, Zhou X, Dammer EB, Duong DM, Aaker J, Zhao Y, Beckmann N, Wang P, Zhu J, Lah JJ, Seyfried NT, Levey AI, Katsel P, Haroutunian V, Schadt EE, Popko B, Casaccia P, Zhang B. Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener 2017; 12:82. [PMID: 29110684 PMCID: PMC5674813 DOI: 10.1186/s13024-017-0219-3] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 10/17/2017] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Oligodendrocytes (OLs) and myelin are critical for normal brain function and have been implicated in neurodegeneration. Several lines of evidence including neuroimaging and neuropathological data suggest that Alzheimer's disease (AD) may be associated with dysmyelination and a breakdown of OL-axon communication. METHODS In order to understand this phenomenon on a molecular level, we systematically interrogated OL-enriched gene networks constructed from large-scale genomic, transcriptomic and proteomic data obtained from human AD postmortem brain samples. We then validated these networks using gene expression datasets generated from mice with ablation of major gene expression nodes identified in our AD-dysregulated networks. RESULTS The robust OL gene coexpression networks that we identified were highly enriched for genes associated with AD risk variants, such as BIN1 and demonstrated strong dysregulation in AD. We further corroborated the structure of the corresponding gene causal networks using datasets generated from the brain of mice with ablation of key network drivers, such as UGT8, CNP and PLP1, which were identified from human AD brain data. Further, we found that mice with genetic ablations of Cnp mimicked aspects of myelin and mitochondrial gene expression dysregulation seen in brain samples from patients with AD, including decreased protein expression of BIN1 and GOT2. CONCLUSIONS This study provides a molecular blueprint of the dysregulation of gene expression networks of OL in AD and identifies key OL- and myelination-related genes and networks that are highly associated with AD.
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Affiliation(s)
- Andrew T. McKenzie
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Sarah Moyon
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Neuroscience Initiative, The City University of New York, Advanced Science Research Center, 85 St. Nicholas Terrace, New York, NY 10031 USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Igor Katsyv
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Won-Min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Eric B. Dammer
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Duc M. Duong
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322 USA
- Integrated Proteomics Core Facility, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Joshua Aaker
- Department of Neurology, The University of Chicago Pritzker School of Medicine, 5841 S. Maryland Avenue, Chicago, IL 60637 USA
| | - Yongzhong Zhao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Noam Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - James J. Lah
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322 USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322 USA
- Integrated Proteomics Core Facility, Emory University School of Medicine, Atlanta, GA 30322 USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Allan I. Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322 USA
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Pavel Katsel
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Vahram Haroutunian
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
- Mental Illness Research, Education, and Clinical Center (VISN 3), James J. Peters VA Medical Center, Bronx, NY 10468 USA
| | - Eric E. Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Brian Popko
- Department of Neurology, The University of Chicago Pritzker School of Medicine, 5841 S. Maryland Avenue, Chicago, IL 60637 USA
| | - Patrizia Casaccia
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
- Neuroscience Initiative, The City University of New York, Advanced Science Research Center, 85 St. Nicholas Terrace, New York, NY 10031 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
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A functional genomics predictive network model identifies regulators of inflammatory bowel disease. Nat Genet 2017; 49:1437-1449. [PMID: 28892060 PMCID: PMC5660607 DOI: 10.1038/ng.3947] [Citation(s) in RCA: 160] [Impact Index Per Article: 22.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 08/11/2017] [Indexed: 02/07/2023]
Abstract
A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.
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Shu L, Chan KHK, Zhang G, Huan T, Kurt Z, Zhao Y, Codoni V, Trégouët DA, Yang J, Wilson JG, Luo X, Levy D, Lusis AJ, Liu S, Yang X. Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet 2017; 13:e1007040. [PMID: 28957322 PMCID: PMC5634657 DOI: 10.1371/journal.pgen.1007040] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 10/10/2017] [Accepted: 09/21/2017] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.
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Affiliation(s)
- Le Shu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Kei Hang K. Chan
- Departments of Epidemiology and Medicine and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States of America
- Hong Kong Institute of Diabetes and Obesity, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Guanglin Zhang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Tianxiao Huan
- The Framingham Heart Study, Framingham, MA, USA and the Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, United States of America
| | - Zeyneb Kurt
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Veronica Codoni
- Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | - David-Alexandre Trégouët
- Sorbonne Universités, UPMC Univ. Paris 06, INSERM, UMR_S 1166, Team Genomics & Pathophysiology of Cardiovascular Diseases, Paris, France
- ICAN Institute for Cardiometabolism and Nutrition, Paris, France
| | | | - Jun Yang
- Department of Public Health, Hangzhou Normal University School of Medicine, Hangzhou, China
- Collaborative Innovation Center for the Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, United States of America
| | - Xi Luo
- Department of Biostatistics, Brown University, Providence, RI, United States of America
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA, USA and the Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, United States of America
| | - Aldons J. Lusis
- Departments of Medicine, Human Genetics, and Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Simin Liu
- Departments of Epidemiology and Medicine and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States of America
- Department of Endocrinology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States of America
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA, United States of America
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, United States of America
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60
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Zhao Y, Forst CV, Sayegh CE, Wang IM, Yang X, Zhang B. Molecular and genetic inflammation networks in major human diseases. MOLECULAR BIOSYSTEMS 2017; 12:2318-41. [PMID: 27303926 DOI: 10.1039/c6mb00240d] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.
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Affiliation(s)
- Yongzhong Zhao
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA. and Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA
| | - Christian V Forst
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA. and Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA
| | - Camil E Sayegh
- Vertex Pharmaceuticals (Canada) Incorporated, 275 Armand-Frappier, Laval, Quebec H7V 4A7, Canada
| | - I-Ming Wang
- Informatics and Analysis, Merck Research Laboratories, Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA 19486, USA.
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA 90025, USA.
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA. and Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, NY 10029, USA
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61
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Mueller AJ, Canty-Laird EG, Clegg PD, Tew SR. Cross-species gene modules emerge from a systems biology approach to osteoarthritis. NPJ Syst Biol Appl 2017. [PMID: 28649440 PMCID: PMC5460168 DOI: 10.1038/s41540-017-0014-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Complexities in degenerative disorders, such as osteoarthritis, arise from multiscale biological, environmental, and temporal perturbations. Animal models serve to provide controlled representations of the natural history of degenerative disorders, but in themselves represent an additional layer of complexity. Comparing transcriptomic networks arising from gene co-expression data across species can facilitate an understanding of the preservation of functional gene modules and establish associations with disease phenotypes. This study demonstrates the preservation of osteoarthritis-associated gene modules, described by immune system and system development processes, across human and rat studies. Class prediction analysis establishes a minimal gene signature, including the expression of the Rho GDP dissociation inhibitor ARHGDIB, which consistently defined healthy human cartilage from osteoarthritic cartilage in an independent data set. The age of human clinical samples remains a strong confounder in defining the underlying gene regulatory mechanisms in osteoarthritis; however, defining preserved gene models across species may facilitate standardization of animal models of osteoarthritis to better represent human disease and control for ageing phenomena.
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Affiliation(s)
- Alan James Mueller
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX UK
| | - Elizabeth G Canty-Laird
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX UK.,The MRC-Arthritis Research UK, Centre for Integrated Research into Musculoskeletal Ageing (CIMA), Liverpool, UK
| | - Peter D Clegg
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX UK.,The MRC-Arthritis Research UK, Centre for Integrated Research into Musculoskeletal Ageing (CIMA), Liverpool, UK
| | - Simon R Tew
- Department of Musculoskeletal Biology, Institute of Ageing and Chronic Disease, Faculty of Health and Life Sciences, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX UK.,The MRC-Arthritis Research UK, Centre for Integrated Research into Musculoskeletal Ageing (CIMA), Liverpool, UK
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62
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Eising E, Shyti R, 't Hoen PAC, Vijfhuizen LS, Huisman SMH, Broos LAM, Mahfouz A, Reinders MJT, Ferrari MD, Tolner EA, de Vries B, van den Maagdenberg AMJM. Cortical Spreading Depression Causes Unique Dysregulation of Inflammatory Pathways in a Transgenic Mouse Model of Migraine. Mol Neurobiol 2017; 54:2986-2996. [PMID: 27032388 PMCID: PMC5390001 DOI: 10.1007/s12035-015-9681-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 12/23/2015] [Indexed: 01/03/2023]
Abstract
Familial hemiplegic migraine type 1 (FHM1) is a rare monogenic subtype of migraine with aura caused by mutations in CACNA1A that encodes the α1A subunit of voltage-gated CaV2.1 calcium channels. Transgenic knock-in mice that carry the human FHM1 R192Q missense mutation ('FHM1 R192Q mice') exhibit an increased susceptibility to cortical spreading depression (CSD), the mechanism underlying migraine aura. Here, we analysed gene expression profiles from isolated cortical tissue of FHM1 R192Q mice 24 h after experimentally induced CSD in order to identify molecular pathways affected by CSD. Gene expression profiles were generated using deep serial analysis of gene expression sequencing. Our data reveal a signature of inflammatory signalling upon CSD in the cortex of both mutant and wild-type mice. However, only in the brains of FHM1 R192Q mice specific genes are up-regulated in response to CSD that are implicated in interferon-related inflammatory signalling. Our findings show that CSD modulates inflammatory processes in both wild-type and mutant brains, but that an additional unique inflammatory signature becomes expressed after CSD in a relevant mouse model of migraine.
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Affiliation(s)
- Else Eising
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Reinald Shyti
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Sjoerd M H Huisman
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft, Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Ludo A M Broos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Ahmed Mahfouz
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft, Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Marcel J T Reinders
- Department of Intelligent Systems, Faculty of EEMCS, Delft University of Technology, Delft, Netherlands
| | - Michel D Ferrari
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Else A Tolner
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Boukje de Vries
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Arn M J M van den Maagdenberg
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.
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63
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Plubell DL, Wilmarth PA, Zhao Y, Fenton AM, Minnier J, Reddy AP, Klimek J, Yang X, David LL, Pamir N. Extended Multiplexing of Tandem Mass Tags (TMT) Labeling Reveals Age and High Fat Diet Specific Proteome Changes in Mouse Epididymal Adipose Tissue. Mol Cell Proteomics 2017; 16:873-890. [PMID: 28325852 DOI: 10.1074/mcp.m116.065524] [Citation(s) in RCA: 210] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 02/28/2017] [Indexed: 01/17/2023] Open
Abstract
The lack of high-throughput methods to analyze the adipose tissue protein composition limits our understanding of the protein networks responsible for age and diet related metabolic response. We have developed an approach using multiple-dimension liquid chromatography tandem mass spectrometry and extended multiplexing (24 biological samples) with tandem mass tags (TMT) labeling to analyze proteomes of epididymal adipose tissues isolated from mice fed either low or high fat diet for a short or a long-term, and from mice that aged on low versus high fat diets. The peripheral metabolic health (as measured by body weight, adiposity, plasma fasting glucose, insulin, triglycerides, total cholesterol levels, and glucose and insulin tolerance tests) deteriorated with diet and advancing age, with long-term high fat diet exposure being the worst. In response to short-term high fat diet, 43 proteins representing lipid metabolism (e.g. AACS, ACOX1, ACLY) and red-ox pathways (e.g. CPD2, CYP2E, SOD3) were significantly altered (FDR < 10%). Long-term high fat diet significantly altered 55 proteins associated with immune response (e.g. IGTB2, IFIT3, LGALS1) and rennin angiotensin system (e.g. ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet significantly altered only 18 proteins representing mainly urea cycle (e.g. OTC, ARG1, CPS1), and amino acid biosynthesis (e.g. GMT, AKR1C6). Surprisingly, high fat diet driven age-related changes culminated with alterations in 155 proteins involving primarily the urea cycle (e.g. ARG1, CPS1), immune response/complement activation (e.g. C3, C4b, C8, C9, CFB, CFH, FGA), extracellular remodeling (e.g. EFEMP1, FBN1, FBN2, LTBP4, FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis (e.g. YAP1, HIP1, NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored approach we have identified both age-related and high fat diet specific proteomic signatures highlighting a pronounced involvement of arginine metabolism in response to advancing age, and branched chain amino acid metabolism in early response to high fat feeding. Data are available via ProteomeXchange with identifier PXD005953.
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Affiliation(s)
- Deanna L Plubell
- From the ‡Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Sciences University, Portland, Oregon
| | - Phillip A Wilmarth
- §Proteomics Shared Resources, Oregon Health & Sciences University, Portland, Oregon
| | - Yuqi Zhao
- ¶Department of Integrative Biology and Physiology, University of California, Los Angeles, California
| | - Alexandra M Fenton
- From the ‡Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Sciences University, Portland, Oregon
| | - Jessica Minnier
- From the ‡Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Sciences University, Portland, Oregon
| | - Ashok P Reddy
- §Proteomics Shared Resources, Oregon Health & Sciences University, Portland, Oregon
| | - John Klimek
- §Proteomics Shared Resources, Oregon Health & Sciences University, Portland, Oregon
| | - Xia Yang
- ¶Department of Integrative Biology and Physiology, University of California, Los Angeles, California
| | - Larry L David
- §Proteomics Shared Resources, Oregon Health & Sciences University, Portland, Oregon
| | - Nathalie Pamir
- From the ‡Department of Medicine, Knight Cardiovascular Institute, Oregon Health & Sciences University, Portland, Oregon;
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64
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Li YF, Nanayakkara G, Sun Y, Li X, Wang L, Cueto R, Shao Y, Fu H, Johnson C, Cheng J, Chen X, Hu W, Yu J, Choi ET, Wang H, Yang XF. Analyses of caspase-1-regulated transcriptomes in various tissues lead to identification of novel IL-1β-, IL-18- and sirtuin-1-independent pathways. J Hematol Oncol 2017; 10:40. [PMID: 28153032 PMCID: PMC5290602 DOI: 10.1186/s13045-017-0406-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 01/25/2017] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND It is well established that caspase-1 exerts its biological activities through its downstream targets such as IL-1β, IL-18, and Sirt-1. The microarray datasets derived from various caspase-1 knockout tissues indicated that caspase-1 can significantly impact the transcriptome. However, it is not known whether all the effects exerted by caspase-1 on transcriptome are mediated only by its well-known substrates. Therefore, we hypothesized that the effects of caspase-1 on transcriptome may be partially independent from IL-1β, IL-18, and Sirt-1. METHODS To determine new global and tissue-specific gene regulatory effects of caspase-1, we took novel microarray data analysis approaches including Venn analysis, cooperation analysis, and meta-analysis methods. We used these statistical methods to integrate different microarray datasets conducted on different caspase-1 knockout tissues and datasets where caspase-1 downstream targets were manipulated. RESULTS We made the following important findings: (1) Caspase-1 exerts its regulatory effects on the majority of genes in a tissue-specific manner; (2) Caspase-1 regulatory genes partially cooperates with genes regulated by sirtuin-1 during organ injury and inflammation in adipose tissue but not in the liver; (3) Caspase-1 cooperates with IL-1β in regulating less than half of the genes involved in cardiovascular disease, organismal injury, and cancer in mouse liver; (4) The meta-analysis identifies 40 caspase-1 globally regulated genes across tissues, suggesting that caspase-1 globally regulates many novel pathways; and (5) The meta-analysis identified new cooperatively and non-cooperatively regulated genes in caspase-1, IL-1β, IL-18, and Sirt-1 pathways. CONCLUSIONS Our findings suggest that caspase-1 regulates many new signaling pathways potentially via its known substrates and also via transcription factors and other proteins that are yet to be identified.
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Affiliation(s)
- Ya-Feng Li
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Cardiovascular Research, & Thrombosis Research, Departments of Pharmacology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.,The Shanxi Provincial People's Hospital, an Affiliate Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, China
| | - Gayani Nanayakkara
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Yu Sun
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Xinyuan Li
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Luqiao Wang
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Ramon Cueto
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Ying Shao
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Hangfei Fu
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Candice Johnson
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Jiali Cheng
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Xiongwen Chen
- Cardiovascular Research, & Thrombosis Research, Departments of Pharmacology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.,Department of Immunology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Wenhui Hu
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA
| | - Jun Yu
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Department of Immunology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Eric T Choi
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Department of Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Hong Wang
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA.,Department of Physiology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA
| | - Xiao-Feng Yang
- Centers for Metabolic Disease Research and Cardiovascular Research, Lewis Katz School of Medicine at Temple University, 3500 North Broad Street, MERB-1059, Philadelphia, PA, 19140, USA. .,Cardiovascular Research, & Thrombosis Research, Departments of Pharmacology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA. .,Department of Physiology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA. .,Department of Immunology, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.
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65
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Meng Q, Zhuang Y, Ying Z, Agrawal R, Yang X, Gomez-Pinilla F. Traumatic Brain Injury Induces Genome-Wide Transcriptomic, Methylomic, and Network Perturbations in Brain and Blood Predicting Neurological Disorders. EBioMedicine 2017; 16:184-194. [PMID: 28174132 PMCID: PMC5474519 DOI: 10.1016/j.ebiom.2017.01.046] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 01/31/2017] [Accepted: 01/31/2017] [Indexed: 12/28/2022] Open
Abstract
The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain.
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Affiliation(s)
- Qingying Meng
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yumei Zhuang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zhe Ying
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Rahul Agrawal
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Fernando Gomez-Pinilla
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Neurosurgery, UCLA Brain Injury Research Center, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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66
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Cohain A, Divaraniya AA, Zhu K, Scarpa JR, Kasarskis A, Zhu J, Chang R, Dudley JT, Schadt EE. EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017; 22:120-131. [PMID: 27896968 PMCID: PMC5161348 DOI: 10.1142/9789813207813_0013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge. While significant methodological developments have broadly enabled the application of BNs to generate and validate meaningful biological hypotheses, the reproducibility of BNs in this context has not been systematically explored. In this study, we aim to determine the criteria for generating reproducible BNs in the context of transcription-based regulatory networks. We utilize two unique tissues from independent datasets, whole blood from the GTEx Consortium and liver from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team (STARNET) study. We evaluated the reproducibility of the BNs by creating networks on data subsampled at different levels from each cohort and comparing these networks to the BNs constructed using the complete data. To help validate our results, we used simulated networks at varying sample sizes. Our study indicates that reproducibility of BNs in biological research is an issue worthy of further consideration, especially in light of the many publications that now employ findings from such constructs without appropriate attention paid to reproducibility. We find that while edge-to-edge reproducibility is strongly dependent on sample size, identification of more highly connected key driver nodes in BNs can be carried out with high confidence across a range of sample sizes.
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Affiliation(s)
- Ariella Cohain
- Icahn Institute and Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498, New York, NY, 10029, USA*Co-first Authors
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67
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Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Mäkinen VP, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genomics 2016; 17:874. [PMID: 27814671 PMCID: PMC5097440 DOI: 10.1186/s12864-016-3198-9] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 10/25/2016] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. RESULTS We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. CONCLUSION Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.
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Affiliation(s)
- Le Shu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zeyneb Kurt
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sean Geoffrey Byars
- Center for Systems Genomics, University of Melbourne, Melbourne, Australia.,School of BioSciences, University of Melbourne, Melbourne, Australia
| | | | | | - Luz D Orozco
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Matteo Pellegrini
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aldons J Lusis
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Bin Zhang
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Inouye
- Center for Systems Genomics, University of Melbourne, Melbourne, Australia.,School of BioSciences, University of Melbourne, Melbourne, Australia.,Department of Pathology, University of Melbourne, Melbourne, Australia
| | - Ville-Petteri Mäkinen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA. .,South Australian Health and Medical Research Institute, Adelaide, Australia. .,School of Biological Sciences, University of Adelaide, Adelaide, Australia. .,Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland.
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA. .,Insitute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA, USA.
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Han L, He H, Qu X, Liu Y, He S, Zheng X, He F, Bai H, Bo X. The relationships among host transcriptional responses reveal distinct signatures underlying viral infection-disease associations. MOLECULAR BIOSYSTEMS 2016; 12:653-65. [PMID: 26699092 DOI: 10.1039/c5mb00657k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Genome-scale DNA microarrays and computational biology facilitate new understanding of viral infections at the system level. Recent years have witnessed a major shift from microorganism-centric toward host-oriented characterization and categorization of viral infections and infection related diseases. We established host transcriptional response (HTR) relationships among 23 different types of human viral pathogens based on calculating HTR similarities using computational integration of 587 public available gene expression profiles. We further identified five virus clusters that show consensus internal HTRs and defined cluster signatures using common dysregulated genes. Individual cluster signature genes were distinguished from one another, and functional analysis revealed common and specific host cellular bioprocesses and signaling pathways involved in confronting viral infections. Through literature investigation and support from epidemiological studies, these were confirmed to be important gene factors associating viral infections with cluster-common and cluster-specific non-infectious human disease(s). Our analyses were the first to feature differential HTRs to viral infections as clusters, and they present a new perspective for understanding infection-disease associations and the underlying pathogeneses.
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Affiliation(s)
- Lu Han
- Department of Traditional Chinese Medicine and Neuroimmunopharmacology, Beijing Institute of Pharmacology and Toxicology, Beijing, China and Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
| | - Haochen He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
| | - Xinyan Qu
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
| | - Yang Liu
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
| | - Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
| | - Xiaofei Zheng
- Department of Biochemistry and Molecular Biology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Fuchu He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
| | - Hui Bai
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China. and No. 451 Hospital of Chinese People's Liberation Army, Xi'an, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, China.
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Abstract
Coronary artery disease (or coronary heart disease), is the leading cause of mortality in many of the developing as well as the developed countries of the world. Cholesterol-enriched plaques in the heart's blood vessels combined with inflammation lead to the lesion expansion, narrowing of blood vessels, reduced blood flow, and may subsequently cause lesion rupture and a heart attack. Even though several environmental risk factors have been established, such as high LDL-cholesterol, diabetes, and high blood pressure, the underlying genetic composition may substantially modify the disease risk; hence, genome composition and gene-environment interactions may be critical for disease progression. Ongoing scientific efforts have seen substantial advancements related to the fields of genetics and genomics, with the major breakthroughs yet to come. As genomics is the most rapidly advancing field in the life sciences, it is important to present a comprehensive overview of current efforts. Here, we present a summary of various genetic and genomics assays and approaches applied to coronary artery disease research.
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Affiliation(s)
- Milos Pjanic
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5233, USA
| | - Clint L Miller
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5233, USA
| | - Robert Wirka
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5233, USA
| | - Juyong B Kim
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5233, USA
| | - Daniel M DiRenzo
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5233, USA
| | - Thomas Quertermous
- Department of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5233, USA.
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Franzén O, Ermel R, Cohain A, Akers NK, Di Narzo A, Talukdar HA, Foroughi-Asl H, Giambartolomei C, Fullard JF, Sukhavasi K, Köks S, Gan LM, Giannarelli C, Kovacic JC, Betsholtz C, Losic B, Michoel T, Hao K, Roussos P, Skogsberg J, Ruusalepp A, Schadt EE, Björkegren JLM. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 2016; 353:827-30. [PMID: 27540175 DOI: 10.1126/science.aad6970] [Citation(s) in RCA: 187] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Accepted: 07/22/2016] [Indexed: 12/11/2022]
Abstract
Genome-wide association studies (GWAS) have identified hundreds of cardiometabolic disease (CMD) risk loci. However, they contribute little to genetic variance, and most downstream gene-regulatory mechanisms are unknown. We genotyped and RNA-sequenced vascular and metabolic tissues from 600 coronary artery disease patients in the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET). Gene expression traits associated with CMD risk single-nucleotide polymorphism (SNPs) identified by GWAS were more extensively found in STARNET than in tissue- and disease-unspecific gene-tissue expression studies, indicating sharing of downstream cis-/trans-gene regulation across tissues and CMDs. In contrast, the regulatory effects of other GWAS risk SNPs were tissue-specific; abdominal fat emerged as an important gene-regulatory site for blood lipids, such as for the low-density lipoprotein cholesterol and coronary artery disease risk gene PCSK9 STARNET provides insights into gene-regulatory mechanisms for CMD risk loci, facilitating their translation into opportunities for diagnosis, therapy, and prevention.
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Affiliation(s)
- Oscar Franzén
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden
| | - Raili Ermel
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. Department of Cardiac Surgery, Tartu University Hospital, 1a Ludwig Puusepa Street, 50406 Tartu, Estonia
| | - Ariella Cohain
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Nicholas K Akers
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Antonio Di Narzo
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Husain A Talukdar
- Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden
| | - Hassan Foroughi-Asl
- Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden
| | - Claudia Giambartolomei
- Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - John F Fullard
- Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Katyayani Sukhavasi
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia
| | - Sulev Köks
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia
| | - Li-Ming Gan
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Pepparedsleden 1, Mölndal, 431 83, Sweden
| | - Chiara Giannarelli
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Cardiovascular Research Center Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Jason C Kovacic
- Cardiovascular Research Center Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Christer Betsholtz
- AstraZeneca-Karolinska Integrated CardioMetabolic Centre (ICMC), Karolinska Institutet, Novum, Blickagången 6, 141 57 Huddinge, Sweden. Department of Immunology, Genetics and Pathology Dag Hammarskjölds Väg 20, 751 85 Uppsala, Sweden
| | - Bojan Losic
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Tom Michoel
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Old College, South Bridge, Edinburgh EH8 9YL, UK
| | - Ke Hao
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Division of Psychiatric Genomics, Department of Psychiatry and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Department of Psychiatry, J. J. Peters VA Medical Center, Mental Illness Research Education and Clinical Center (MIRECC), 130 West Kingsbridge Road, Bronx, NY 10468, USA
| | - Josefin Skogsberg
- Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden
| | - Arno Ruusalepp
- Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden. Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. Department of Cardiac Surgery, Tartu University Hospital, 1a Ludwig Puusepa Street, 50406 Tartu, Estonia
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York , NY 10029, USA. Clinical Gene Networks AB, Jungfrugatan 10, 114 44 Stockholm, Sweden. Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Scheeles Väg 2, 171 77 Stockholm, Sweden.
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71
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Zhang L, Feng XK, Ng YK, Li SC. Reconstructing directed gene regulatory network by only gene expression data. BMC Genomics 2016; 17 Suppl 4:430. [PMID: 27556418 PMCID: PMC5001240 DOI: 10.1186/s12864-016-2791-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Background Accurately identifying gene regulatory network is an important task in understanding in vivo biological activities. The inference of such networks is often accomplished through the use of gene expression data. Many methods have been developed to evaluate gene expression dependencies between transcription factor and its target genes, and some methods also eliminate transitive interactions. The regulatory (or edge) direction is undetermined if the target gene is also a transcription factor. Some methods predict the regulatory directions in the gene regulatory networks by locating the eQTL single nucleotide polymorphism, or by observing the gene expression changes when knocking out/down the candidate transcript factors; regrettably, these additional data are usually unavailable, especially for the samples deriving from human tissues. Results In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer’s disease; 2. ZNF329 and RB1 significantly regulate those ‘mesenchymal’ gene expression signature genes for brain tumors. Conclusion By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Xi Kang Feng
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Yen Kaow Ng
- Faculty of Information and Communication Technology, University Tunku Abdul Rahman, Kampar, Perak, Malaysia
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.
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Scarpa JR, Jiang P, Losic B, Readhead B, Gao VD, Dudley JT, Vitaterna MH, Turek FW, Kasarskis A. Systems Genetic Analyses Highlight a TGFβ-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease. PLoS Genet 2016; 12:e1006137. [PMID: 27390852 PMCID: PMC4938493 DOI: 10.1371/journal.pgen.1006137] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 05/31/2016] [Indexed: 12/22/2022] Open
Abstract
Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFβ-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.
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Affiliation(s)
- Joseph R. Scarpa
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Peng Jiang
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Bojan Losic
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ben Readhead
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Vance D. Gao
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Joel T. Dudley
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Martha H. Vitaterna
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Fred W. Turek
- Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, Illinois, United States of America
| | - Andrew Kasarskis
- Icahn Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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73
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Zhao Y, Chen J, Freudenberg JM, Meng Q, Rajpal DK, Yang X. Network-Based Identification and Prioritization of Key Regulators of Coronary Artery Disease Loci. Arterioscler Thromb Vasc Biol 2016; 36:928-41. [PMID: 26966275 PMCID: PMC5576868 DOI: 10.1161/atvbaha.115.306725] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 03/01/2016] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Recent genome-wide association studies of coronary artery disease (CAD) have revealed 58 genome-wide significant and 148 suggestive genetic loci. However, the molecular mechanisms through which they contribute to CAD and the clinical implications of these findings remain largely unknown. We aim to retrieve gene subnetworks of the 206 CAD loci and identify and prioritize candidate regulators to better understand the biological mechanisms underlying the genetic associations. APPROACH AND RESULTS We devised a new integrative genomics approach that incorporated (1) candidate genes from the top CAD loci, (2) the complete genetic association results from the 1000 genomes-based CAD genome-wide association studies from the Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus the Coronary Artery Disease consortium, (3) tissue-specific gene regulatory networks that depict the potential relationship and interactions between genes, and (4) tissue-specific gene expression patterns between CAD patients and controls. The networks and top-ranked regulators according to these data-driven criteria were further queried against literature, experimental evidence, and drug information to evaluate their disease relevance and potential as drug targets. Our analysis uncovered several potential novel regulators of CAD such as LUM and STAT3, which possess properties suitable as drug targets. We also revealed molecular relations and potential mechanisms through which the top CAD loci operate. Furthermore, we found that multiple CAD-relevant biological processes such as extracellular matrix, inflammatory and immune pathways, complement and coagulation cascades, and lipid metabolism interact in the CAD networks. CONCLUSIONS Our data-driven integrative genomics framework unraveled tissue-specific relations among the candidate genes of the CAD genome-wide association studies loci and prioritized novel network regulatory genes orchestrating biological processes relevant to CAD.
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Affiliation(s)
- Yuqi Zhao
- From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.)
| | - Jing Chen
- From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.)
| | - Johannes M Freudenberg
- From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.)
| | - Qingying Meng
- From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.)
| | - Deepak K Rajpal
- From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.).
| | - Xia Yang
- From the Department of Integrative Biology and Physiology, University of California, Los Angeles (Y.Z., Q.M., X.Y.); and Target Sciences Computational Biology (US), GSK, King of Prussia, PA (J.C., J.M.F., D.K.R.).
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Li Q, Lee CH, Peters LA, Mastropaolo LA, Thoeni C, Elkadri A, Schwerd T, Zhu J, Zhang B, Zhao Y, Hao K, Dinarzo A, Hoffman G, Kidd BA, Murchie R, Adham ZA, Guo C, Kotlarz D, Cutz E, Walters TD, Shouval DS, Curran M, Dobrin R, Brodmerkel C, Snapper SB, Klein C, Brumell JH, Hu M, Nanan R, Snanter-Nanan B, Wong M, Le Deist F, Haddad E, Roifman CM, Deslandres C, Griffiths AM, Gaskin KJ, Uhlig HH, Schadt EE, Muise AM. Variants in TRIM22 That Affect NOD2 Signaling Are Associated With Very-Early-Onset Inflammatory Bowel Disease. Gastroenterology 2016; 150:1196-1207. [PMID: 26836588 PMCID: PMC4842103 DOI: 10.1053/j.gastro.2016.01.031] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 01/22/2016] [Accepted: 01/22/2016] [Indexed: 12/17/2022]
Abstract
BACKGROUND & AIMS Severe forms of inflammatory bowel disease (IBD) that develop in very young children can be caused by variants in a single gene. We performed whole-exome sequence (WES) analysis to identify genetic factors that might cause granulomatous colitis and severe perianal disease, with recurrent bacterial and viral infections, in an infant of consanguineous parents. METHODS We performed targeted WES analysis of DNA collected from the patient and her parents. We validated our findings by a similar analysis of DNA from 150 patients with very-early-onset IBD not associated with known genetic factors analyzed in Toronto, Oxford, and Munich. We compared gene expression signatures in inflamed vs noninflamed intestinal and rectal tissues collected from patients with treatment-resistant Crohn's disease who participated in a trial of ustekinumab. We performed functional studies of identified variants in primary cells from patients and cell culture. RESULTS We identified a homozygous variant in the tripartite motif containing 22 gene (TRIM22) of the patient, as well as in 2 patients with a disease similar phenotype. Functional studies showed that the variant disrupted the ability of TRIM22 to regulate nucleotide binding oligomerization domain containing 2 (NOD2)-dependent activation of interferon-beta signaling and nuclear factor-κB. Computational studies demonstrated a correlation between the TRIM22-NOD2 network and signaling pathways and genetic factors associated very early onset and adult-onset IBD. TRIM22 is also associated with antiviral and mycobacterial effectors and markers of inflammation, such as fecal calprotectin, C-reactive protein, and Crohn's disease activity index scores. CONCLUSIONS In WES and targeted exome sequence analyses of an infant with severe IBD characterized by granulomatous colitis and severe perianal disease, we identified a homozygous variant of TRIM22 that affects the ability of its product to regulate NOD2. Combined computational and functional studies showed that the TRIM22-NOD2 network regulates antiviral and antibacterial signaling pathways that contribute to inflammation. Further study of this network could lead to new disease markers and therapeutic targets for patients with very early and adult-onset IBD.
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Affiliation(s)
- Qi Li
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Cheng Hiang Lee
- Gastroenterology Department, The Children's Hospital at Westmead, Westmead, 2145, New South Wales, Australia,The James Fairfax Institute of Paediatric Nutrition, the University of Sydney, New South Wales, Australia
| | - Lauren A Peters
- Icahn School of Medicine at Mount Sinai, New York, New York, USA. Graduate School of Biomedical Sciences, New York, New York, USA,Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Lucas A Mastropaolo
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Cornelia Thoeni
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Abdul Elkadri
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Tobias Schwerd
- Translational Gastroenterology Unit, Nuffield Department Clinical Medicine, Experimental Medicine Division, University of Oxford, and Department of Pediatrics, John Radcliffe Hospital, Oxford, UK
| | - Jun Zhu
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Bin Zhang
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Yongzhong Zhao
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Ke Hao
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Antonio Dinarzo
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Gabriel Hoffman
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Brian A Kidd
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Ryan Murchie
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Ziad Al Adham
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada,Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Conghui Guo
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Daniel Kotlarz
- Department of Pediatrics, Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Ernest Cutz
- Division of Pathology, The Hospital for Sick Children, Toronto, Canada
| | - Thomas D Walters
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Dror S Shouval
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Mark Curran
- Janssen R&D, LLC, 1400 McKean Road, Spring House, PA 19477
| | - Radu Dobrin
- Janssen R&D, LLC, 1400 McKean Road, Spring House, PA 19477
| | | | - Scott B Snapper
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, USA,Division of Gastroenterology and Hepatology, Brigham & Women's Hospital, Department of Medicine, Boston, USA
| | - Christoph Klein
- Department of Pediatrics, Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - John H Brumell
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Institute of Medical Science, University of Toronto, Toronto, ON, Canada,Molecular Genetics, University of Toronto
| | - Mingjing Hu
- Gastroenterology Department, The Children's Hospital at Westmead, Westmead, 2145, New South Wales, Australia,The James Fairfax Institute of Paediatric Nutrition, the University of Sydney, New South Wales, Australia
| | - Ralph Nanan
- Gastroenterology Department, The Children's Hospital at Westmead, Westmead, 2145, New South Wales, Australia,The James Fairfax Institute of Paediatric Nutrition, the University of Sydney, New South Wales, Australia
| | - Brigitte Snanter-Nanan
- Gastroenterology Department, The Children's Hospital at Westmead, Westmead, 2145, New South Wales, Australia,The James Fairfax Institute of Paediatric Nutrition, the University of Sydney, New South Wales, Australia
| | - Melanie Wong
- Immunology Department, The Children's Hospital at Westmead, Westmead, 2145, New South Wales, Australia
| | - Francoise Le Deist
- Department of Microbiology and Immunology, CHU Sainte Justine and Department of Microbiology, Infectiology and Immunology, University of Montreal, QC, Canada
| | - Elie Haddad
- CHU Sainte-Justine, Department of Pediatrics, Department of Microbiology, Infectiology and Immunology, University of Montreal, QC, Canada
| | - Chaim M Roifman
- Division of Immunology, Department of Pediatrics, University of Toronto, The Hospital for Sick Children, Toronto, Canada
| | - Colette Deslandres
- Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, CHU Sainte-Justine, Montreal, QC, Canada
| | - Anne M Griffiths
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada,Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, Canada
| | - Kevin J Gaskin
- Gastroenterology Department, The Children's Hospital at Westmead, Westmead, 2145, New South Wales, Australia,The James Fairfax Institute of Paediatric Nutrition, the University of Sydney, New South Wales, Australia
| | - Holm H Uhlig
- Translational Gastroenterology Unit, Nuffield Department Clinical Medicine, Experimental Medicine Division, University of Oxford, and Department of Pediatrics, John Radcliffe Hospital, Oxford, UK
| | - Eric E Schadt
- Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences and the Icahn Institute for Genomics and Multiscale Biology, New York, NY 10029
| | - Aleixo M Muise
- SickKids Inflammatory Bowel Disease Center and Cell Biology Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada; Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.
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75
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Sutherland JJ, Jolly RA, Goldstein KM, Stevens JL. Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes. PLoS Comput Biol 2016; 12:e1004847. [PMID: 27028627 PMCID: PMC4814051 DOI: 10.1371/journal.pcbi.1004847] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 03/03/2016] [Indexed: 12/13/2022] Open
Abstract
The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver / HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance. Gene expression studies in model systems are widely used for understanding the mechanism of drugs and other perturbations in biological systems. Other researchers have examined the reproducibility of microarray studies between laboratories, or comparing microarrays and/or RNA sequencing. However, no large scale studies have compared results from protocols which differ in minor details, or results generated in vivo vs. in vitro culture systems thought to serve as useful models. The rat liver is by far the most extensively studied model evaluating effects of drugs and other perturbations, and existing data allowed us to assess the level of concordance between rat liver and rat primary hepatocytes cultured in collagen-coated plates (i.e. “flat” culture) for hundreds of drugs. We found that the mouse liver serves as a better model of the rat liver than do rat primary hepatocytes, even after allowing for differences due to pharmacokinetics. The low concordance observed between rat liver and rat hepatocytes suggests that validating the utility of ‘omics data generated on emerging cell culture approaches (e.g. “organ-on-a-chip”, 3D-printed tissues) using rat cells and comparison to the rat liver may be necessary in order to gain confidence these approaches substantially improve on traditional culture models of human cells.
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Affiliation(s)
- Jeffrey J. Sutherland
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
- * E-mail: (JJS); (JLS)
| | - Robert A. Jolly
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
| | - Keith M. Goldstein
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
| | - James L. Stevens
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America
- * E-mail: (JJS); (JLS)
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76
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Abstract
Inborn errors of metabolism (IEM) are not unlike common diseases. They often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. This greatly impacts patient care and reveals fundamental gaps in our knowledge of disease modifying biology. Systems biology approaches that integrate multi-omics data into molecular networks have significantly improved our understanding of complex diseases. Similar approaches to study IEM are rare despite their complex nature. We highlight that existing common disease-derived datasets and networks can be repurposed to generate novel mechanistic insight in IEM and potentially identify candidate modifiers. While understanding disease pathophysiology will advance the IEM field, the ultimate goal should be to understand per individual how their phenotype emerges given their primary mutation on the background of their whole genome, not unlike personalized medicine. We foresee that panomics and network strategies combined with recent experimental innovations will facilitate this.
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Affiliation(s)
- Carmen A Argmann
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Box 1498, New York, NY 10029, USA.
| | - Sander M Houten
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Box 1498, New York, NY 10029, USA
| | - Jun Zhu
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Box 1498, New York, NY 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Box 1498, New York, NY 10029, USA.
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77
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Zhang B, Tran L, Emilsson V, Zhu J. Characterization of Genetic Networks Associated with Alzheimer's Disease. Methods Mol Biol 2016; 1303:459-77. [PMID: 26235085 DOI: 10.1007/978-1-4939-2627-5_28] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
At the molecular level, the genetics of complex disease such as Alzheimer's disease (AD) manifests itself as series of alterations in the molecular interactions in pathways and networks that define biological processes underlying the pathophysiological states of disease. While large-scale genome-wide association (GWA) studies of late-onset alzheimer's disease (LOAD) have uncovered prominent genomic regions linked to the disease, the cause for the vast majority of LOAD cases still remains unknown. Increasingly available large-scale genomic and genetic data related to LOAD has made it possible to comprehensively uncover the mechanisms causally lined to LOAD in a completely data-driven manner. Here we review the various aspects of systems/network biology approaches and methodology in constructing genetic networks associated with AD from large sampling of postmortem brain tissues. We describe in detail a multiscale network modeling approach (MNMA) that integrates interaction and causal gene networks to analyze large-scale DNA, gene expression and pathophysiological data from multiple post-mortem brain regions of LOAD patients as well non-demented normal controls. MNMA first employs weighted gene co-expression network analysis (WGCNA) to construct multi-tissue networks that simultaneously capture intra-tissue and inter-tissue gene-gene interactions and then quantifies the change in connectivity among highly co-expressed genes in LOAD with respect to the normal state. Co-expressed gene modules are then rank ordered by relevance to pathophysiological traits and enrichment of genes differentially expressed in LOAD. Causal regulatory relationships among the genes in each module are then determined by a Bayesian network inference framework that is used to formally integrate genetic and gene expression information. MNMA has uncovered a massive remodeling of network structures in LOAD and identified novel subnetworks and key regulators that are causally linked to LOAD. In the end, we will outline the challenges in systems/network approaches to LOAD.
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Affiliation(s)
- Bin Zhang
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY, 10029, USA,
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78
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Wang M, Zhao Y, Zhang B. Efficient Test and Visualization of Multi-Set Intersections. Sci Rep 2015; 5:16923. [PMID: 26603754 PMCID: PMC4658477 DOI: 10.1038/srep16923] [Citation(s) in RCA: 233] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 10/22/2015] [Indexed: 01/10/2023] Open
Abstract
Identification of sets of objects with shared features is a common operation in all
disciplines. Analysis of intersections among multiple sets is fundamental for
in-depth understanding of their complex relationships. However, so far no method has
been developed to assess statistical significance of intersections among three or
more sets. Moreover, the state-of-the-art approaches for visualization of multi-set
intersections are not scalable. Here, we first developed a theoretical framework for
computing the statistical distributions of multi-set intersections based upon
combinatorial theory, and then accordingly designed a procedure to efficiently
calculate the exact probabilities of multi-set intersections. We further developed
multiple efficient and scalable techniques to visualize multi-set intersections and
the corresponding intersection statistics. We implemented both the theoretical
framework and the visualization techniques in a unified R software package,
SuperExactTest. We demonstrated the utility of SuperExactTest
through an intensive simulation study and a comprehensive analysis of seven
independently curated cancer gene sets as well as six disease or trait associated
gene sets identified by genome-wide association studies. We expect
SuperExactTest developed by this study will have a broad range of
applications in scientific data analysis in many disciplines.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, NY 10029, USA
| | - Yongzhong Zhao
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, NY 10029, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, NY 10029, USA
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79
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Topol A, English JA, Flaherty E, Rajarajan P, Hartley BJ, Gupta S, Desland F, Zhu S, Goff T, Friedman L, Rapoport J, Felsenfeld D, Cagney G, Mackay-Sim A, Savas JN, Aronow B, Fang G, Zhang B, Cotter D, Brennand KJ. Increased abundance of translation machinery in stem cell-derived neural progenitor cells from four schizophrenia patients. Transl Psychiatry 2015; 5:e662. [PMID: 26485546 PMCID: PMC4930118 DOI: 10.1038/tp.2015.118] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Revised: 06/23/2015] [Accepted: 06/25/2015] [Indexed: 12/17/2022] Open
Abstract
The genetic and epigenetic factors contributing to risk for schizophrenia (SZ) remain unresolved. Here we demonstrate, for the first time, perturbed global protein translation in human-induced pluripotent stem cell (hiPSC)-derived forebrain neural progenitor cells (NPCs) from four SZ patients relative to six unaffected controls. We report increased total protein levels and protein synthesis, together with two independent sets of quantitative mass spectrometry evidence indicating markedly increased levels of ribosomal and translation initiation and elongation factor proteins, in SZ hiPSC NPCs. We posit that perturbed levels of global protein synthesis in SZ hiPSC NPCs represent a novel post-transcriptional mechanism that might contribute to disease progression.
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Affiliation(s)
- A Topol
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - J A English
- Royal College of Surgeons in Ireland, Beaumont Hospital, Beaumont, Dublin, Ireland
| | - E Flaherty
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - P Rajarajan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - B J Hartley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - S Gupta
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - F Desland
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - S Zhu
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - T Goff
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - L Friedman
- Childhood Psychiatry Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - J Rapoport
- Childhood Psychiatry Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - D Felsenfeld
- Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - G Cagney
- UCD Conway Institute of Biomolecular and Biomedical Research, Dublin, Ireland
| | - A Mackay-Sim
- Eskitis Institute for Drug Discovery, Griffith University, Brisbane, QLD, Australia
| | - J N Savas
- Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA, USA
| | - B Aronow
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - G Fang
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - B Zhang
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - D Cotter
- Royal College of Surgeons in Ireland, Beaumont Hospital, Beaumont, Dublin, Ireland
| | - K J Brennand
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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80
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Liang K, Zhu L, Tan J, Shi W, He Q, Yu B. Identification of autophagy signaling network that contributes to stroke in the ischemic rodent brain via gene expression. Neurosci Bull 2015; 31:480-90. [PMID: 26254060 DOI: 10.1007/s12264-015-1547-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 07/09/2015] [Indexed: 11/24/2022] Open
Abstract
Autophagy plays a vital role in cerebral ischemia and may be a potential target for developing novel therapy for stroke. In this study, we constructed an autophagy-related pathway network by analyzing the genes related to autophagy and ischemic stroke, and the risk genes were screened. Two autophagy-related modules were significantly up-regulated and clustered to influence cerebral ischemia. Besides, three key modular genes (NFKB1, RELA, and STAT3) were revealed. With 5-fold cross validation, the ROC curves of NFKB1, RELA, and STAT3 were 0.8256, 0.8462, and 0.8923. They formed a complex module and competitively mediated the activation of autophagy in cerebral ischemia. In conclusion, a module containing NFKB1, RELA, and STAT3 mediates autophagy, serving as a potential biomarker for the diagnosis and therapy of ischemic stroke.
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Affiliation(s)
- Kun Liang
- Department of Vascular Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, China
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81
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Kidd BA, Readhead BP, Eden C, Parekh S, Dudley JT. Integrative network modeling approaches to personalized cancer medicine. Per Med 2015; 12:245-257. [PMID: 27019658 DOI: 10.2217/pme.14.87] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The ability to collect millions of molecular measurements from patients is a now a reality for clinical medicine. This reality has created the challenge of how to integrate these vast amounts of data into models that accurately predict complex pathophysiology and can translate this complexity into clinically actionable outputs. Integrative informatics and data-driven approaches provide a framework for analyzing large-scale datasets and combining them into multiscale models that can be used to determine the key drivers of disease and identify optimal therapies for treating tumors. In this perspective we discuss how an integrative modeling approach is being used to inform individual treatment decisions, highlighting a recent case report that illustrates the challenges and opportunities for personalized oncology.
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Affiliation(s)
- Brian A Kidd
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics & Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ben P Readhead
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics & Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Caroline Eden
- Department of Medicine Hematology & Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Samir Parekh
- Department of Medicine Hematology & Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joel T Dudley
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics & Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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82
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Poitou C, Perret C, Mathieu F, Truong V, Blum Y, Durand H, Alili R, Chelghoum N, Pelloux V, Aron-Wisnewsky J, Torcivia A, Bouillot JL, Parks BW, Ninio E, Clément K, Tiret L. Bariatric Surgery Induces Disruption in Inflammatory Signaling Pathways Mediated by Immune Cells in Adipose Tissue: A RNA-Seq Study. PLoS One 2015; 10:e0125718. [PMID: 25938420 PMCID: PMC4418598 DOI: 10.1371/journal.pone.0125718] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 03/17/2015] [Indexed: 11/30/2022] Open
Abstract
Background Bariatric surgery is associated to improvements in obesity-associated comorbidities thought to be mediated by a decrease of adipose inflammation. However, the molecular mechanisms behind these beneficial effects are poorly understood. Methodology/Principal Findings We analyzed RNA-seq expression profiles in adipose tissue from 22 obese women before and 3 months after surgery. Of 15,972 detected genes, 1214 were differentially expressed after surgery at a 5% false discovery rate. Upregulated genes were mostly involved in the basal cellular machinery. Downregulated genes were enriched in metabolic functions of adipose tissue. At baseline, 26 modules of coexpressed genes were identified. The four most stable modules reflected the innate and adaptive immune responses of adipose tissue. A first module reflecting a non-specific signature of innate immune cells, mainly macrophages, was highly conserved after surgery with the exception of DUSP2 and CD300C. A second module reflected the adaptive immune response elicited by T lymphocytes; after surgery, a disconnection was observed between genes involved in T-cell signaling and mediators of the signal transduction such as CXCR1, CXCR2, GPR97, CCR7 and IL7R. A third module reflected neutrophil-mediated inflammation; after surgery, several genes were dissociated from the module, including S100A8, S100A12, CD300E, VNN2, TUBB1 and FAM65B. We also identified a dense network of 19 genes involved in the interferon-signaling pathway which was strongly preserved after surgery, with the exception of DDX60, an antiviral factor involved in RIG-I-mediated interferon signaling. A similar loss of connection was observed in lean mice compared to their obese counterparts. Conclusions/Significance These results suggest that improvements of the inflammatory state following surgery might be explained by a disruption of immuno-inflammatory cascades involving a few crucial molecules which could serve as potential therapeutic targets.
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Affiliation(s)
- Christine Poitou
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Nutriomics team, F-75005, Paris, France
| | - Claire Perret
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Genomics and Pathophysiology of Cardiovascular Diseases team, F-75013, Paris, France
| | - François Mathieu
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Genomics and Pathophysiology of Cardiovascular Diseases team, F-75013, Paris, France
| | - Vinh Truong
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Genomics and Pathophysiology of Cardiovascular Diseases team, F-75013, Paris, France
| | - Yuna Blum
- Department of Medicine/Division of Cardiology, University of California Los Angeles, Los Angeles, California 90095, United States of America
| | - Hervé Durand
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Genomics and Pathophysiology of Cardiovascular Diseases team, F-75013, Paris, France
| | - Rohia Alili
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Nutriomics team, F-75005, Paris, France
| | - Nadjim Chelghoum
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Post-Genomic Platform of Pitié-Salpêtrière (P3S), F-75013, Paris, France
| | - Véronique Pelloux
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Nutriomics team, F-75005, Paris, France
| | - Judith Aron-Wisnewsky
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Nutriomics team, F-75005, Paris, France
| | - Adriana Torcivia
- Assistance Publique-Hôpitaux de Paris, Department of Visceral Surgery, Pitié-Salpêtrière Hospital, F-75013, Paris, France
| | - Jean-Luc Bouillot
- Assistance Publique-Hôpitaux de Paris, Department of General, Digestive and Metabolic Surgery, Ambroise-Paré Hospital, F- 92100, Boulogne-Billancourt, France
| | - Brian W. Parks
- Department of Medicine/Division of Cardiology, University of California Los Angeles, Los Angeles, California 90095, United States of America
| | - Ewa Ninio
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Genomics and Pathophysiology of Cardiovascular Diseases team, F-75013, Paris, France
| | - Karine Clément
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Nutriomics team, F-75005, Paris, France
| | - Laurence Tiret
- Institute of Cardiometabolism And Nutrition (ICAN), Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Nutrition Department, F-75013, Paris, France
- Sorbonne Universités, University Pierre et Marie Curie (UPMC), Institut National de la Santé et de la Recherche Médicale (INSERM) UMR_S 1166, Genomics and Pathophysiology of Cardiovascular Diseases team, F-75013, Paris, France
- * E-mail:
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83
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Björkegren JLM, Kovacic JC, Dudley JT, Schadt EE. Genome-wide significant loci: how important are they? Systems genetics to understand heritability of coronary artery disease and other common complex disorders. J Am Coll Cardiol 2015; 65:830-845. [PMID: 25720628 DOI: 10.1016/j.jacc.2014.12.033] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Accepted: 12/19/2014] [Indexed: 12/19/2022]
Abstract
Genome-wide association studies (GWAS) have been extensively used to study common complex diseases such as coronary artery disease (CAD), revealing 153 suggestive CAD loci, of which at least 46 have been validated as having genome-wide significance. However, these loci collectively explain <10% of the genetic variance in CAD. Thus, we must address the key question of what factors constitute the remaining 90% of CAD heritability. We review possible limitations of GWAS, and contextually consider some candidate CAD loci identified by this method. Looking ahead, we propose systems genetics as a complementary approach to unlocking the CAD heritability and etiology. Systems genetics builds network models of relevant molecular processes by combining genetic and genomic datasets to ultimately identify key "drivers" of disease. By leveraging systems-based genetic approaches, we can help reveal the full genetic basis of common complex disorders, enabling novel diagnostic and therapeutic opportunities.
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Affiliation(s)
- Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Division of Vascular Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden; Department of Pathological Anatomy and Forensic Medicine, University of Tartu, Tartu, Estonia.
| | - Jason C Kovacic
- Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
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84
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Jiang P, Scarpa JR, Fitzpatrick K, Losic B, Gao VD, Hao K, Summa KC, Yang HS, Zhang B, Allada R, Vitaterna MH, Turek FW, Kasarskis A. A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders. Cell Rep 2015; 11:835-48. [PMID: 25921536 DOI: 10.1016/j.celrep.2015.04.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 12/23/2014] [Accepted: 03/30/2015] [Indexed: 02/06/2023] Open
Abstract
Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders.
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Affiliation(s)
- Peng Jiang
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Joseph R Scarpa
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Karrie Fitzpatrick
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Bojan Losic
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Vance D Gao
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Keith C Summa
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - He S Yang
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ravi Allada
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Martha H Vitaterna
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Fred W Turek
- Center for Sleep & Circadian Biology, Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA.
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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85
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Huan T, Meng Q, Saleh MA, Norlander AE, Joehanes R, Zhu J, Chen BH, Zhang B, Johnson AD, Ying S, Courchesne P, Raghavachari N, Wang R, Liu P, O'Donnell CJ, Vasan R, Munson PJ, Madhur MS, Harrison DG, Yang X, Levy D. Integrative network analysis reveals molecular mechanisms of blood pressure regulation. Mol Syst Biol 2015; 11:799. [PMID: 25882670 PMCID: PMC4422556 DOI: 10.15252/msb.20145399] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Genome‐wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP‐associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single‐gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression‐related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP‐related genes were uncovered. As proof‐of‐concept, we validated SH2B3, one of the top key drivers, using Sh2b3−/− mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3−/− mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.
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Affiliation(s)
- Tianxiao Huan
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Qingying Meng
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Mohamed A Saleh
- Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University, Nashville, TN, USA Department of Pharmacology and Toxicology, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt
| | - Allison E Norlander
- Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Roby Joehanes
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA Mathematical and Statistical Computing Laboratory, Center for Information Technology National Institutes of Health, Bethesda, MD, USA Harvard Medical School, Boston, MA, USA Hebrew SeniorLife, Boston, MA, USA
| | - Jun Zhu
- Institute of Genomics and Multiscale Biology, New York, NY, USA Graduate School of Biological Sciences Mount Sinai School of Medicine, New York, NY, USA
| | - Brian H Chen
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Bin Zhang
- Institute of Genomics and Multiscale Biology, New York, NY, USA Graduate School of Biological Sciences Mount Sinai School of Medicine, New York, NY, USA
| | - Andrew D Johnson
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA Cardiovascular Epidemiology and Human Genomics Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Saixia Ying
- Mathematical and Statistical Computing Laboratory, Center for Information Technology National Institutes of Health, Bethesda, MD, USA
| | - Paul Courchesne
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Nalini Raghavachari
- Division of Geriatrics and Clinical Gerontology, National Institute on Aging, Bethesda, MD, USA
| | - Richard Wang
- Genomics Core facility Genetics & Developmental Biology Center, The National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Poching Liu
- Genomics Core facility Genetics & Developmental Biology Center, The National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | | | - Christopher J O'Donnell
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA Cardiovascular Epidemiology and Human Genomics Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
| | - Ramachandran Vasan
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Peter J Munson
- Mathematical and Statistical Computing Laboratory, Center for Information Technology National Institutes of Health, Bethesda, MD, USA
| | - Meena S Madhur
- Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - David G Harrison
- Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Daniel Levy
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
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86
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Ota A, Kovary KM, Wu OH, Ahrends R, Shen WJ, Costa MJ, Feldman BJ, Kraemer FB, Teruel MN. Using SRM-MS to quantify nuclear protein abundance differences between adipose tissue depots of insulin-resistant mice. J Lipid Res 2015; 56:1068-78. [PMID: 25840986 PMCID: PMC4409283 DOI: 10.1194/jlr.d056317] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Indexed: 01/14/2023] Open
Abstract
Insulin resistance (IR) underlies metabolic disease. Visceral, but not subcutaneous, white adipose tissue (WAT) has been linked to the development of IR, potentially due to differences in regulatory protein abundance. Here we investigate how protein levels are changed in IR in different WAT depots by developing a targeted proteomics approach to quantitatively compare the abundance of 42 nuclear proteins in subcutaneous and visceral WAT from a commonly used insulin-resistant mouse model, Lepr(db/db), and from C57BL/6J control mice. The most differentially expressed proteins were important in adipogenesis, as confirmed by siRNA-mediated depletion experiments, suggesting a defect in adipogenesis in visceral, but not subcutaneous, insulin-resistant WAT. Furthermore, differentiation of visceral, but not subcutaneous, insulin-resistant stromal vascular cells (SVCs) was impaired. In an in vitro approach to understand the cause of this impaired differentiation, we compared insulin-resistant visceral SVCs to preadipocyte cell culture models made insulin resistant by different stimuli. The insulin-resistant visceral SVC protein abundance profile correlated most with preadipocyte cell culture cells treated with both palmitate and TNFα. Together, our study introduces a method to simultaneously measure and quantitatively compare nuclear protein expression patterns in primary adipose tissue and adipocyte cell cultures, which we show can reveal relationships between differentiation and disease states of different adipocyte tissue types.
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Affiliation(s)
- Asuka Ota
- Departments of Chemical and Systems Biology, Stanford University, Stanford, CA
| | - Kyle M Kovary
- Departments of Chemical and Systems Biology, Stanford University, Stanford, CA
| | - Olivia H Wu
- Departments of Chemical and Systems Biology, Stanford University, Stanford, CA
| | - Robert Ahrends
- Departments of Chemical and Systems Biology, Stanford University, Stanford, CA
| | - Wen-Jun Shen
- Medicine/Division of Endocrinology, Stanford University, Stanford, CA Veterans Administration Palo Alto Health Care System, Palo Alto, CA
| | - Maria J Costa
- Pediatrics/Division of Endocrinology, Stanford University, Stanford, CA
| | - Brian J Feldman
- Pediatrics/Division of Endocrinology, Stanford University, Stanford, CA
| | - Fredric B Kraemer
- Medicine/Division of Endocrinology, Stanford University, Stanford, CA Veterans Administration Palo Alto Health Care System, Palo Alto, CA
| | - Mary N Teruel
- Departments of Chemical and Systems Biology, Stanford University, Stanford, CA
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87
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Tillmann T, Gibson AR, Scott G, Harrison O, Dominiczak A, Hanlon P. Systems Medicine 2.0: potential benefits of combining electronic health care records with systems science models. J Med Internet Res 2015; 17:e64. [PMID: 25831125 PMCID: PMC4387294 DOI: 10.2196/jmir.3082] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 09/05/2014] [Accepted: 09/12/2014] [Indexed: 12/25/2022] Open
Abstract
Background The global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment. Objective Our aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases. Methods We undertook an unsystematic review of peer-reviewed literature and technology websites. Results Our review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome. Conclusions Systems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.
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Affiliation(s)
- Taavi Tillmann
- Department of Epidemiology & Public Health, University College London, London, United Kingdom.
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88
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Phenotypic differences in hiPSC NPCs derived from patients with schizophrenia. Mol Psychiatry 2015; 20:361-8. [PMID: 24686136 PMCID: PMC4182344 DOI: 10.1038/mp.2014.22] [Citation(s) in RCA: 321] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Revised: 01/30/2014] [Accepted: 02/03/2014] [Indexed: 12/13/2022]
Abstract
Consistent with recent reports indicating that neurons differentiated in vitro from human-induced pluripotent stem cells (hiPSCs) are immature relative to those in the human brain, gene expression comparisons of our hiPSC-derived neurons to the Allen BrainSpan Atlas indicate that they most resemble fetal brain tissue. This finding suggests that, rather than modeling the late features of schizophrenia (SZ), hiPSC-based models may be better suited for the study of disease predisposition. We now report that a significant fraction of the gene signature of SZ hiPSC-derived neurons is conserved in SZ hiPSC neural progenitor cells (NPCs). We used two independent discovery-based approaches-microarray gene expression and stable isotope labeling by amino acids in cell culture (SILAC) quantitative proteomic mass spectrometry analyses-to identify cellular phenotypes in SZ hiPSC NPCs from four SZ patients. From our findings that SZ hiPSC NPCs show abnormal gene expression and protein levels related to cytoskeletal remodeling and oxidative stress, we predicted, and subsequently observed, aberrant migration and increased oxidative stress in SZ hiPSC NPCs. These reproducible NPC phenotypes were identified through scalable assays that can be applied to expanded cohorts of SZ patients, making them a potentially valuable tool with which to study the developmental mechanisms contributing to SZ.
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89
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Das SK, Sharma NK, Zhang B. Integrative network analysis reveals different pathophysiological mechanisms of insulin resistance among Caucasians and African Americans. BMC Med Genomics 2015; 8:4. [PMID: 25868721 PMCID: PMC4351975 DOI: 10.1186/s12920-015-0078-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 01/27/2015] [Indexed: 12/15/2022] Open
Abstract
Background African Americans (AA) have more pronounced insulin resistance and higher insulin secretion than European Americans (Caucasians or CA) when matched for age, gender, and body mass index (BMI). We hypothesize that physiological differences (including insulin sensitivity [SI]) between CAs and AAs can be explained by co-regulated gene networks in tissues involved in glucose homeostasis. Methods We performed integrative gene network analyses of transcriptomic data in subcutaneous adipose tissue of 99 CA and 37 AA subjects metabolically characterized as non-diabetic, with a range of SI and BMI values. Results Transcripts negatively correlated with SI in only the CA or AA subjects were enriched for inflammatory response genes and integrin-signaling genes, respectively. A sub-network (module) with TYROBP as a hub enriched for genes involved in inflammatory response (corrected p = 1.7E-26) was negatively correlated with SI (r = −0.426, p = 4.95E-04) in CA subjects. SI was positively correlated with transcript modules enriched for mitochondrial metabolism in both groups. Several SI-associated co-expressed modules were enriched for genes differentially expressed between groups. Two modules involved in immune response to viral infections and function of adherens junction, are significantly correlated with SI only in CAs. Five modules involved in drug/intracellular transport and oxidoreductase activity, among other activities, are correlated with SI only in AAs. Furthermore, we identified driver genes of these race-specific SI-associated modules. Conclusions SI-associated transcriptional networks that were deranged predominantly in one ethnic group may explain the distinctive physiological features of glucose homeostasis among AA subjects. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0078-0) contains supplementary material, which is available to authorized users.
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90
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Sharma A, Menche J, Huang CC, Ort T, Zhou X, Kitsak M, Sahni N, Thibault D, Voung L, Guo F, Ghiassian SD, Gulbahce N, Baribaud F, Tocker J, Dobrin R, Barnathan E, Liu H, Panettieri RA, Tantisira KG, Qiu W, Raby BA, Silverman EK, Vidal M, Weiss ST, Barabási AL. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum Mol Genet 2015; 24:3005-20. [PMID: 25586491 DOI: 10.1093/hmg/ddv001] [Citation(s) in RCA: 120] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 01/05/2015] [Indexed: 01/24/2023] Open
Abstract
Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.
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Affiliation(s)
- Amitabh Sharma
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jörg Menche
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Theoretical Physics, Budapest University of Technology and Economics, H1111, Budapest, Hungary Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
| | - C Chris Huang
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Tatiana Ort
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Maksim Kitsak
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nidhi Sahni
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Derek Thibault
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Linh Voung
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Feng Guo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Susan Dina Ghiassian
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Natali Gulbahce
- Department of Cellular and Molecular Pharmacology, University of California 1700, 4th Street, Byers Hall 308D, San Francisco, CA 94158, USA
| | - Frédéric Baribaud
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Joel Tocker
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Radu Dobrin
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Elliot Barnathan
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Hao Liu
- Janssen Research & Development, Inc., 1400 McKean Road, Spring House, PA 19477, USA
| | - Reynold A Panettieri
- Pulmonary Allergy and Critical Care Division, Department of Medicine, University of Pennsylvania, 125 South 31st Street, TRL Suite 1200, Philadelphia, PA 19104, USA
| | - Kelan G Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Weiliang Qiu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin A Raby
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Albert-László Barabási
- Center for Complex Networks Research, Department of Physics, Northeastern University, Boston, MA 02115, USA Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Theoretical Physics, Budapest University of Technology and Economics, H1111, Budapest, Hungary Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
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Schulte PA, Whittaker C, Curran CP. Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2015; 12 Suppl 1:S69-S81. [PMID: 26583908 PMCID: PMC4685594 DOI: 10.1080/15459624.2015.1060323#.xhlte1uzbx4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.
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Affiliation(s)
- P. A. Schulte
- Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Education and Information Division, Cincinnati, Ohio
| | - C. Whittaker
- Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Education and Information Division, Cincinnati, Ohio
| | - C. P. Curran
- Northern Kentucky University, Department of Biological Sciences, Highland Heights, Kentucky
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Kelder T, Summer G, Caspers M, van Schothorst EM, Keijer J, Duivenvoorde L, Klaus S, Voigt A, Bohnert L, Pico C, Palou A, Bonet ML, Dembinska-Kiec A, Malczewska-Malec M, Kieć-Wilk B, del Bas JM, Caimari A, Arola L, van Erk M, van Ommen B, Radonjic M. White adipose tissue reference network: a knowledge resource for exploring health-relevant relations. GENES & NUTRITION 2015; 10:439. [PMID: 25466819 PMCID: PMC4252261 DOI: 10.1007/s12263-014-0439-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 10/24/2014] [Indexed: 12/13/2022]
Abstract
Optimal health is maintained by interaction of multiple intrinsic and environmental factors at different levels of complexity-from molecular, to physiological, to social. Understanding and quantification of these interactions will aid design of successful health interventions. We introduce the reference network concept as a platform for multi-level exploration of biological relations relevant for metabolic health, by integration and mining of biological interactions derived from public resources and context-specific experimental data. A White Adipose Tissue Health Reference Network (WATRefNet) was constructed as a resource for discovery and prioritization of mechanism-based biomarkers for white adipose tissue (WAT) health status and the effect of food and drug compounds on WAT health status. The WATRefNet (6,797 nodes and 32,171 edges) is based on (1) experimental data obtained from 10 studies addressing different adiposity states, (2) seven public knowledge bases of molecular interactions, (3) expert's definitions of five physiologically relevant processes key to WAT health, namely WAT expandability, Oxidative capacity, Metabolic state, Oxidative stress and Tissue inflammation, and (4) a collection of relevant biomarkers of these processes identified by BIOCLAIMS ( http://bioclaims.uib.es ). The WATRefNet comprehends multiple layers of biological complexity as it contains various types of nodes and edges that represent different biological levels and interactions. We have validated the reference network by showing overrepresentation with anti-obesity drug targets, pathology-associated genes and differentially expressed genes from an external disease model dataset. The resulting network has been used to extract subnetworks specific to the above-mentioned expert-defined physiological processes. Each of these process-specific signatures represents a mechanistically supported composite biomarker for assessing and quantifying the effect of interventions on a physiological aspect that determines WAT health status. Following this principle, five anti-diabetic drug interventions and one diet intervention were scored for the match of their expression signature to the five biomarker signatures derived from the WATRefNet. This confirmed previous observations of successful intervention by dietary lifestyle and revealed WAT-specific effects of drug interventions. The WATRefNet represents a sustainable knowledge resource for extraction of relevant relationships such as mechanisms of action, nutrient intervention targets and biomarkers and for assessment of health effects for support of health claims made on food products.
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Affiliation(s)
- Thomas Kelder
- Microbiology & Systems Biology, TNO, Zeist, The Netherlands
- Present Address: EdgeLeap B.V., Hooghiemstraplein 15, 3514 AX Utrecht, The Netherlands
| | - Georg Summer
- Microbiology & Systems Biology, TNO, Zeist, The Netherlands
- CARIM, Maastricht University, Maastricht, The Netherlands
| | | | | | - Jaap Keijer
- Human and Animal Physiology, Wageningen University, Wageningen, The Netherlands
| | - Loes Duivenvoorde
- Human and Animal Physiology, Wageningen University, Wageningen, The Netherlands
| | - Susanne Klaus
- Group of Energy Metabolism, German Institute of Human Nutrition in Potsdam, Nuthetal, Germany
| | - Anja Voigt
- Group of Energy Metabolism, German Institute of Human Nutrition in Potsdam, Nuthetal, Germany
| | - Laura Bohnert
- Group of Energy Metabolism, German Institute of Human Nutrition in Potsdam, Nuthetal, Germany
| | - Catalina Pico
- Molecular Biology, Nutrition and Biotechnology (Nutrigenomics), University of the Balearic Islands (UIB), Palma de Mallorca, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Palma de Mallorca, Spain
| | - Andreu Palou
- Molecular Biology, Nutrition and Biotechnology (Nutrigenomics), University of the Balearic Islands (UIB), Palma de Mallorca, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Palma de Mallorca, Spain
| | - M. Luisa Bonet
- Molecular Biology, Nutrition and Biotechnology (Nutrigenomics), University of the Balearic Islands (UIB), Palma de Mallorca, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Palma de Mallorca, Spain
| | - Aldona Dembinska-Kiec
- Department of Clinical Biochemistry, Jagiellonian University Medical College, Krakow, Poland
| | | | - Beata Kieć-Wilk
- Department of Metabolic Disorders, Jagiellonian University Medical College, Krakow, Poland
| | - Josep M. del Bas
- Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain
| | - Antoni Caimari
- Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain
| | - Lluis Arola
- Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain
- Rovira i Virgili University, Tarragona, Spain
| | - Marjan van Erk
- Microbiology & Systems Biology, TNO, Zeist, The Netherlands
| | - Ben van Ommen
- Microbiology & Systems Biology, TNO, Zeist, The Netherlands
| | - Marijana Radonjic
- Microbiology & Systems Biology, TNO, Zeist, The Netherlands
- Present Address: EdgeLeap B.V., Hooghiemstraplein 15, 3514 AX Utrecht, The Netherlands
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Schulte PA, Whittaker C, Curran CP. Considerations for Using Genetic and Epigenetic Information in Occupational Health Risk Assessment and Standard Setting. JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE 2015; 12 Suppl 1:S69-81. [PMID: 26583908 PMCID: PMC4685594 DOI: 10.1080/15459624.2015.1060323] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Risk assessment forms the basis for both occupational health decision-making and the development of occupational exposure limits (OELs). Although genetic and epigenetic data have not been widely used in risk assessment and ultimately, standard setting, it is possible to envision such uses. A growing body of literature demonstrates that genetic and epigenetic factors condition biological responses to occupational and environmental hazards or serve as targets of them. This presentation addresses the considerations for using genetic and epigenetic information in risk assessments, provides guidance on using this information within the classic risk assessment paradigm, and describes a framework to organize thinking about such uses. The framework is a 4 × 4 matrix involving the risk assessment functions (hazard identification, dose-response modeling, exposure assessment, and risk characterization) on one axis and inherited and acquired genetic and epigenetic data on the other axis. The cells in the matrix identify how genetic and epigenetic data can be used for each risk assessment function. Generally, genetic and epigenetic data might be used as endpoints in hazard identification, as indicators of exposure, as effect modifiers in exposure assessment and dose-response modeling, as descriptors of mode of action, and to characterize toxicity pathways. Vast amounts of genetic and epigenetic data may be generated by high-throughput technologies. These data can be useful for assessing variability and reducing uncertainty in extrapolations, and they may serve as the foundation upon which identification of biological perturbations would lead to a new paradigm of toxicity pathway-based risk assessments.
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Affiliation(s)
- P. A. Schulte
- Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Education and Information Division, Cincinnati, Ohio
- Address correspondence to Paul A. Schulte, Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Education and Information Division, 4676 Columbia Parkway, MS-C14 Cincinnati, OH45226, . E-mail:
| | - C. Whittaker
- Centers for Disease Control and Prevention (CDC), National Institute for Occupational Safety and Health (NIOSH), Education and Information Division, Cincinnati, Ohio
| | - C. P. Curran
- Northern Kentucky University, Department of Biological Sciences, Highland Heights, Kentucky
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94
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Schadt EE, Buchanan S, Brennand KJ, Merchant KM. Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders. Front Pharmacol 2014; 5:252. [PMID: 25520658 PMCID: PMC4251289 DOI: 10.3389/fphar.2014.00252] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 10/30/2014] [Indexed: 12/14/2022] Open
Abstract
A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS) disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC)-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer's Disease, and the psychiatric disorder schizophrenia, we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature data in order to construct predictive disease network models that can (i) elucidate subtypes of syndromic diseases, (ii) provide insights into disease networks and targets and (iii) facilitate a novel drug screening strategy using patient-derived hiPSCs to discover novel therapeutics for CNS disorders.
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Affiliation(s)
- Eric E Schadt
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai New York, NY, USA ; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai New York, NY, USA
| | - Sean Buchanan
- Lilly Research Laboratories, Eli Lilly and Company Indianapolis, IN, USA
| | - Kristen J Brennand
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai New York, NY, USA
| | - Kalpana M Merchant
- Lilly Research Laboratories, Eli Lilly and Company Indianapolis, IN, USA
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95
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Chan KHK, Huang YT, Meng Q, Wu C, Reiner A, Sobel EM, Tinker L, Lusis AJ, Yang X, Liu S. Shared molecular pathways and gene networks for cardiovascular disease and type 2 diabetes mellitus in women across diverse ethnicities. CIRCULATION. CARDIOVASCULAR GENETICS 2014; 7:911-9. [PMID: 25371518 DOI: 10.1161/circgenetics.114.000676] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D) share many common risk factors, potential molecular mechanisms that may also be shared for these 2 disorders remain unknown. METHODS AND RESULTS Using an integrative pathway and network analysis, we performed genome-wide association studies in 8155 blacks, 3494 Hispanic American, and 3697 Caucasian American women who participated in the national Women's Health Initiative single-nucleotide polymorphism (SNP) Health Association Resource and the Genomics and Randomized Trials Network. Eight top pathways and gene networks related to cardiomyopathy, calcium signaling, axon guidance, cell adhesion, and extracellular matrix seemed to be commonly shared between CVD and T2D across all 3 ethnic groups. We also identified ethnicity-specific pathways, such as cell cycle (specific for Hispanic American and Caucasian American) and tight junction (CVD and combined CVD and T2D in Hispanic American). In network analysis of gene-gene or protein-protein interactions, we identified key drivers that included COL1A1, COL3A1, and ELN in the shared pathways for both CVD and T2D. These key driver genes were cross-validated in multiple mouse models of diabetes mellitus and atherosclerosis. CONCLUSIONS Our integrative analysis of American women of 3 ethnicities identified multiple shared biological pathways and key regulatory genes for the development of CVD and T2D. These prospective findings also support the notion that ethnicity-specific susceptibility genes and process are involved in the pathogenesis of CVD and T2D.
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Affiliation(s)
- Kei Hang K Chan
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Yen-Tsung Huang
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Qingying Meng
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Chunyuan Wu
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Alexander Reiner
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Eric M Sobel
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Lesley Tinker
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Aldons J Lusis
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.)
| | - Xia Yang
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.).
| | - Simin Liu
- From the Department of Epidemiology (K.H.K.C., Y.-T.H., S.L.) and Division of Endocrinology, Department of Medicine (S.L.), Warren Alpert Medical School of Brown University, Providence, RI; Department of Integrative Biology and Physiology (K.H.K.C., Q.M., X.Y.), Department of Human Genetics (E.M.S.), Department of Medicine/Division of Cardiology, David Geffen School of Medicine (A.J.L.), and Departments of Medicine and Obstetrics and Gynecology, David Geffen School of Medicine (S.L.), University of California Los Angeles; Biostatistics Division (C.W.), Public Health Sciences Division (L.T.), Fred Hutchinson Cancer Research Center, Seattle, WA; and Department of Epidemiology, University of Washington, Seattle (A.R.).
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Ozdemir C, Akpulat U, Sharafi P, Yıldız Y, Onbaşılar I, Kocaefe C. Periostin is temporally expressed as an extracellular matrix component in skeletal muscle regeneration and differentiation. Gene 2014; 553:130-9. [PMID: 25303869 DOI: 10.1016/j.gene.2014.10.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 10/05/2014] [Accepted: 10/07/2014] [Indexed: 12/22/2022]
Abstract
The transcriptional events and pathways responsible for the acquisition of the myogenic phenotype during regeneration and myogenesis have been studied extensively. The modulators that shape the extracellular matrix in health and disease, however, are less understood. Understanding the components and pathways of this remodeling will aid the restoration of the architecture and prevent deterioration under pathological conditions such as fibrosis. Periostin, a matricellular protein associated with remodeling of the extracellular matrix and connective tissue architecture, is emerging in pathological conditions associated with fibrosis in adult life. Periostin also complicates fibrosis in degenerative skeletal muscle conditions such as dystrophies. This study primarily addresses the spatial and temporal involvement of periostin along skeletal muscle regeneration. In the acute skeletal muscle injury model that shows recovery without fibrosis, we show that periostin is rapidly disrupted along with the extensive necrosis and periostin mRNA is transiently upregulated during the myotube maturation. This expression is stringently initiated from the newly regenerating fibers. However, this observation is contrasting to a model that displays extensive fibrosis where upregulation of periostin expression is stable and confined to the fibrotic compartments of endomysial and perimysial space. In vitro myoblast differentiation further supports the claim that upregulation of periostin expression is a function of extracellular matrix remodeling during myofiber differentiation and maturation. We further seek to identify the expression kinetics of various periostin isoforms during the differentiation of rat and mouse myoblasts. Results depict that a singular periostin isoform dominated the rat muscle, contrasting to multiple isoforms in C2C12 myoblast cells. This study shows that periostin, a mediator with deleterious impact on conditions exhibiting fibrosis, is also produced and secreted by myoblasts and regenerating myofibers during architectural remodeling in the course of development and regeneration.
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Affiliation(s)
- Cansu Ozdemir
- Dept. of Medical Biology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Uğur Akpulat
- Dept. of Medical Biology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Parisa Sharafi
- Dept. of Medical Biology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Yılmaz Yıldız
- Dept. of Medical Biology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Ilyas Onbaşılar
- Laboratory Animal Breeding and Research Unit, Hacettepe University School of Medicine, Ankara, Turkey
| | - Cetin Kocaefe
- Dept. of Medical Biology, Hacettepe University School of Medicine, Ankara, Turkey.
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97
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Kelder T, Verschuren L, van Ommen B, van Gool AJ, Radonjic M. Network signatures link hepatic effects of anti-diabetic interventions with systemic disease parameters. BMC SYSTEMS BIOLOGY 2014; 8:108. [PMID: 25204982 PMCID: PMC4363943 DOI: 10.1186/s12918-014-0108-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Accepted: 08/29/2014] [Indexed: 11/10/2022]
Abstract
Background Multifactorial diseases such as type 2 diabetes mellitus (T2DM), are driven by a complex network of interconnected mechanisms that translate to a diverse range of complications at the physiological level. To optimally treat T2DM, pharmacological interventions should, ideally, target key nodes in this network that act as determinants of disease progression. Results We set out to discover key nodes in molecular networks based on the hepatic transcriptome dataset from a preclinical study in obese LDLR-/- mice recently published by Radonjic et al. Here, we focus on comparing efficacy of anti-diabetic dietary (DLI) and two drug treatments, namely PPARA agonist fenofibrate and LXR agonist T0901317. By combining knowledge-based and data-driven networks with a random walks based algorithm, we extracted network signatures that link the DLI and two drug interventions to dyslipidemia-related disease parameters. Conclusions This study identified specific and prioritized sets of key nodes in hepatic molecular networks underlying T2DM, uncovering pathways that are to be modulated by targeted T2DM drug interventions in order to modulate the complex disease phenotype.
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Affiliation(s)
- Thomas Kelder
- TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands. .,Current address: EdgeLeap B.V, Utrecht, The Netherlands.
| | - Lars Verschuren
- TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands.
| | - Ben van Ommen
- TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands.
| | - Alain J van Gool
- TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands. .,Department of Laboratory Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands. .,Faculty of Physics, Mathematics and Informatics, Radboud University Nijmegen, Nijmegen, The Netherlands.
| | - Marijana Radonjic
- TNO, Research Group Microbiology & Systems Biology, Zeist, The Netherlands. .,Current address: EdgeLeap B.V, Utrecht, The Netherlands.
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Krewski D, Westphal M, Andersen ME, Paoli GM, Chiu WA, Al-Zoughool M, Croteau MC, Burgoon LD, Cote I. A framework for the next generation of risk science. ENVIRONMENTAL HEALTH PERSPECTIVES 2014; 122:796-805. [PMID: 24727499 PMCID: PMC4123023 DOI: 10.1289/ehp.1307260] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Accepted: 04/09/2014] [Indexed: 05/19/2023]
Abstract
OBJECTIVES In 2011, the U.S. Environmental Protection Agency initiated the NexGen project to develop a new paradigm for the next generation of risk science. METHODS The NexGen framework was built on three cornerstones: the availability of new data on toxicity pathways made possible by fundamental advances in basic biology and toxicological science, the incorporation of a population health perspective that recognizes that most adverse health outcomes involve multiple determinants, and a renewed focus on new risk assessment methodologies designed to better inform risk management decision making. RESULTS The NexGen framework has three phases. Phase I (objectives) focuses on problem formulation and scoping, taking into account the risk context and the range of available risk management decision-making options. Phase II (risk assessment) seeks to identify critical toxicity pathway perturbations using new toxicity testing tools and technologies, and to better characterize risks and uncertainties using advanced risk assessment methodologies. Phase III (risk management) involves the development of evidence-based population health risk management strategies of a regulatory, economic, advisory, community-based, or technological nature, using sound principles of risk management decision making. CONCLUSIONS Analysis of a series of case study prototypes indicated that many aspects of the NexGen framework are already beginning to be adopted in practice.
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Affiliation(s)
- Daniel Krewski
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada
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Mäkinen VP, Civelek M, Meng Q, Zhang B, Zhu J, Levian C, Huan T, Segrè AV, Ghosh S, Vivar J, Nikpay M, Stewart AFR, Nelson CP, Willenborg C, Erdmann J, Blakenberg S, O'Donnell CJ, März W, Laaksonen R, Epstein SE, Kathiresan S, Shah SH, Hazen SL, Reilly MP, Lusis AJ, Samani NJ, Schunkert H, Quertermous T, McPherson R, Yang X, Assimes TL. Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease. PLoS Genet 2014; 10:e1004502. [PMID: 25033284 PMCID: PMC4102418 DOI: 10.1371/journal.pgen.1004502] [Citation(s) in RCA: 155] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 05/27/2014] [Indexed: 12/13/2022] Open
Abstract
The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions. Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- School of Molecular and Biomedical Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Mete Civelek
- Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Qingying Meng
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Candace Levian
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Tianxiao Huan
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Ayellet V. Segrè
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Sujoy Ghosh
- Department of Cardiovascular and Metabolic Research, Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, United States of America
- Program in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore
| | - Juan Vivar
- Department of Cardiovascular and Metabolic Research, Biomedical Biotechnology Research Institute, North Carolina Central University, Durham, North Carolina, United States of America
| | - Majid Nikpay
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Alexandre F. R. Stewart
- John and Jennifer Ruddy Canadian Cardiovascular Research Center, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- National Institute for Health Research (NIHR) Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Christina Willenborg
- Institut für Integrative und Experimentelle Genomik, University of Lübeck, Lübeck, Germany
| | - Jeanette Erdmann
- Institut für Integrative und Experimentelle Genomik, University of Lübeck, Lübeck, Germany
- DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg, Kiel, Lübeck, Germany
| | - Stefan Blakenberg
- Clinic for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Christopher J. O'Donnell
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
- Cardiology Division, Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Winfried März
- Mannheim Institute of Public Health, Social and Preventive Medicine, Medical Faculty of Mannheim, University of Heidelberg, Mannheim, Germany
- Synlab Academy, Mannheim, Germany
| | - Reijo Laaksonen
- Science Center, Tampere University Hospital, Tampere, Finland
| | - Stephen E. Epstein
- Cardiovascular Research Institute, Washington Hospital Center, Washington, D.C., United States of America
| | - Sekar Kathiresan
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America
- Cardiology Division, Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Svati H. Shah
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, United States of America
| | | | - Muredach P. Reilly
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | | | - Aldons J. Lusis
- Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester, United Kingdom
- National Institute for Health Research (NIHR) Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
| | - Heribert Schunkert
- DZHK (German Research Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Ruth McPherson
- Atherogenomics Laboratory, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California, United States of America
- * E-mail: (XY); (TLA)
| | - Themistocles L. Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America
- * E-mail: (XY); (TLA)
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100
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Unifying immunology with informatics and multiscale biology. Nat Immunol 2014; 15:118-27. [PMID: 24448569 DOI: 10.1038/ni.2787] [Citation(s) in RCA: 128] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Accepted: 11/14/2013] [Indexed: 12/14/2022]
Abstract
The immune system is a highly complex and dynamic system. Historically, the most common scientific and clinical practice has been to evaluate its individual components. This kind of approach cannot always expose the interconnecting pathways that control immune-system responses and does not reveal how the immune system works across multiple biological systems and scales. High-throughput technologies can be used to measure thousands of parameters of the immune system at a genome-wide scale. These system-wide surveys yield massive amounts of quantitative data that provide a means to monitor and probe immune-system function. New integrative analyses can help synthesize and transform these data into valuable biological insight. Here we review some of the computational analysis tools for high-dimensional data and how they can be applied to immunology.
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