1
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Kim S, Lee DH, Giovannucci EL, Keum N. Association between weight cycling and risk of colorectal cancer: a prospective cohort study. Br J Cancer 2024; 130:496-503. [PMID: 38114529 PMCID: PMC10844329 DOI: 10.1038/s41416-023-02529-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Weight cycling is the repeated episodes manifesting intentional weight loss and subsequent unintentional weight gain. Whether the frequency and magnitude of weight cycling is associated with colorectal cancer risk independent of body mass index (BMI) remains unknown. METHODS Two prospective cohort studies, Nurses' Health Study I and Health Professionals Follow-up Study, followed 85,562 participants from 1992 to 2014. Participants completed a questionnaire regarding the frequency and magnitude of intentional weight loss in the past 4 years at the baseline. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazard model. RESULTS We identified 1626 colorectal cancer cases during up to 22 years of follow-up. In the pooled analysis of HPFS and NHS, compared to non-weight cycling, moderate weight cycling (≥3 times of intentional weight loss of ≥2.3-4.4 kg) was associated with a reduced risk of colorectal cancer after adjustment for confounders, including attained BMI after weight cycling (HR = 0.82, 95% CI 0.69, 0.97). However, no significant association was observed in mild weight cyclers and in severe weight cyclers. CONCLUSIONS Moderate weight cycling was associated with a lower risk of colorectal cancer independent of BMI. This finding needs further studies for replication and putative biological mechanisms.
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Affiliation(s)
- Suryun Kim
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dong Hoon Lee
- Department of Sports Industry Studies, Yonsei University, Seoul, Republic of Korea
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - NaNa Keum
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Food Science and Biotechnology, Dongguk University, Goyang, Republic of Korea.
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2
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Wijesinghe SN, Badoume A, Nanus DE, Sharma‐Oates A, Farah H, Certo M, Alnajjar F, Davis ET, Mauro C, Lindsay MA, Jones SW. Obesity defined molecular endotypes in the synovium of patients with osteoarthritis provides a rationale for therapeutic targeting of fibroblast subsets. Clin Transl Med 2023; 13:e1232. [PMID: 37006170 PMCID: PMC10068310 DOI: 10.1002/ctm2.1232] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Osteoarthritis (OA), a multifaceted condition, poses a significant challenge for the successful clinical development of therapeutics due to heterogeneity. However, classifying molecular endotypes of OA pathogenesis could provide invaluable phenotype-directed routes for stratifying subgroups of patients for targeted therapeutics, leading to greater chances of success in trials. This study establishes endotypes in OA soft joint tissue driven by obesity in both load-bearing and non-load bearing joints. METHODS Hand, hip, knee and foot joint synovial tissue was obtained from OA patients (n = 32) classified as obese (BMI > 30) or normal weight (BMI 18.5-24.9). Isolated fibroblasts (OA SF) were assayed by Olink proteomic panel, seahorse metabolic flux assay, Illumina's NextSeq 500 bulk and Chromium 10X single cell RNA-sequencing, validated by Luminex and immunofluorescence. RESULTS Targeted proteomic, metabolic and transcriptomic analysis found the inflammatory landscape of OA SFs are independently impacted by obesity, joint loading and anatomical site with significant heterogeneity between obese and normal weight patients, confirmed by bulk RNAseq. Further investigation by single cell RNAseq identified four functional molecular endotypes including obesity specific subsets defined by an inflammatory endotype related to immune cell regulation, fibroblast activation and inflammatory signaling, with up-regulated CXCL12, CFD and CHI3L1 expression. Luminex confirmed elevated chitase3-like-1(229.5 vs. 49.5 ng/ml, p < .05) and inhibin (20.6 vs. 63.8 pg/ml, p < .05) in obese and normal weight OA SFs, respectively. Lastly, we find SF subsets in obese patients spatially localise in sublining and lining layers of OA synovium and can be distinguished by differential expression of the transcriptional regulators MYC and FOS. CONCLUSION These findings demonstrate the significance of obesity in changing the inflammatory landscape of synovial fibroblasts in both load bearing and non-load bearing joints. Describing multiple heterogeneous OA SF populations characterised by specific molecular endotypes, which drive heterogeneity in OA disease pathogenesis. These molecular endotypes may provide a route for the stratification of patients in clinical trials, providing a rational for the therapeutic targeting of specific SF subsets in specific patient populations with arthritic conditions.
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Affiliation(s)
- Susanne N. Wijesinghe
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
| | - Amel Badoume
- Department of Pharmacy and PharmacologyUniversity of Bath, Claverton DownBathUK
| | - Dominika E. Nanus
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
| | | | - Hussein Farah
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
| | - Michelangelo Certo
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
| | - Fawzeyah Alnajjar
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
| | | | - Claudio Mauro
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
| | - Mark A. Lindsay
- Department of Pharmacy and PharmacologyUniversity of Bath, Claverton DownBathUK
| | - Simon W. Jones
- Institute of Inflammation and AgeingMRC‐ARUK Centre for Musculoskeletal Ageing ResearchUniversity of BirminghamBirminghamUK
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3
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Ellegaard A, Knop FK. MYC
mRNA expression throughout the intestine is not associated with body mass index or type 2 diabetes. Endocrinol Diabetes Metab 2022; 5:e00327. [DOI: 10.1002/edm2.327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Anne‐Marie Ellegaard
- Center for Clinical Metabolic Research Copenhagen University Hospital–Herlev and Gentofte Hellerup Denmark
| | - Filip Krag Knop
- Center for Clinical Metabolic Research Copenhagen University Hospital–Herlev and Gentofte Hellerup Denmark
- Steno Diabetes Center Copenhagen Gentofte Denmark
- Novo Nordisk Foundation Centre for Basic Metabolic Research Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
- Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark
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4
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Constructing gene regulatory networks using epigenetic data. NPJ Syst Biol Appl 2021; 7:45. [PMID: 34887443 PMCID: PMC8660777 DOI: 10.1038/s41540-021-00208-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 11/01/2021] [Indexed: 12/24/2022] Open
Abstract
The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.
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5
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Weighill D, Ben Guebila M, Glass K, Platig J, Yeh JJ, Quackenbush J. Gene Targeting in Disease Networks. Front Genet 2021; 12:649942. [PMID: 33968133 PMCID: PMC8103030 DOI: 10.3389/fgene.2021.649942] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/15/2021] [Indexed: 01/12/2023] Open
Abstract
Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.
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Affiliation(s)
- Deborah Weighill
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Kimberly Glass
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Jen Jen Yeh
- Departments of Surgery and Pharmacology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - John Quackenbush
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, United States
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
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6
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Kim BK, Lee HS, Lee SY, Park HW. Different Biological Pathways Between Good and Poor Inhaled Corticosteroid Responses in Asthma. Front Med (Lausanne) 2021; 8:652824. [PMID: 33816533 PMCID: PMC8012484 DOI: 10.3389/fmed.2021.652824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 02/25/2021] [Indexed: 12/18/2022] Open
Abstract
Gene regulatory networks address how transcription factors (TFs) and their regulatory roles in gene expression determine the responsiveness to anti-asthma therapy. The purpose of this study was to assess gene regulatory networks of adult patients with asthma who showed good or poor lung function improvements in response to inhaled corticosteroids (ICSs). A total of 47 patients with asthma were recruited and classified as good responders (GRs) and poor responders (PRs) based on their responses to ICSs. Genome-wide gene expression was measured using peripheral blood mononuclear cells obtained in a stable state. We used Passing Attributes between Networks for Data Assimilations to construct the gene regulatory networks associated with GRs and PRs to ICSs. We identified the top-10 TFs that showed large differences in high-confidence edges between the GR and PR aggregate networks. These top-10 TFs and their differentially-connected genes in the PR and GR aggregate networks were significantly enriched in distinct biological pathways, such as TGF-β signaling, cell cycle, and IL-4 and IL-13 signaling pathways. We identified multiple TFs and related biological pathways influencing ICS responses in asthma. Our results provide potential targets to overcome insensitivity to corticosteroids in patients with asthma.
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Affiliation(s)
- Byung-Keun Kim
- Department of Internal Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Hyun-Seung Lee
- Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, South Korea
| | - Suh-Young Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Heung-Woo Park
- Institute of Allergy and Clinical Immunology, Seoul National University Medical Research Center, Seoul, South Korea.,Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.,Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
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7
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Kuijjer ML, Fagny M, Marin A, Quackenbush J, Glass K. PUMA: PANDA Using MicroRNA Associations. Bioinformatics 2021; 36:4765-4773. [PMID: 32860050 PMCID: PMC7750953 DOI: 10.1093/bioinformatics/btaa571] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 05/19/2020] [Accepted: 06/10/2020] [Indexed: 12/27/2022] Open
Abstract
Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marieke L Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo 0318, Norway
| | - Maud Fagny
- UMR7206 Eco-Anthropologie, Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Université de Paris, Paris 75016, France
| | - Alessandro Marin
- Centre for Computing in Science Education, Department of Physics, University of Oslo, Oslo 0316, Norway
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA.,Channing Division of Network Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Harvard Medical School, Boston, MA 02115, USA
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8
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Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
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Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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9
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Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019; 10:294. [PMID: 31031797 PMCID: PMC6470635 DOI: 10.3389/fgene.2019.00294] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
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Affiliation(s)
- Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, United States
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10
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Sonawane AR, Platig J, Fagny M, Chen CY, Paulson JN, Lopes-Ramos CM, DeMeo DL, Quackenbush J, Glass K, Kuijjer ML. Understanding Tissue-Specific Gene Regulation. Cell Rep 2017; 21:1077-1088. [PMID: 29069589 PMCID: PMC5828531 DOI: 10.1016/j.celrep.2017.10.001] [Citation(s) in RCA: 225] [Impact Index Per Article: 32.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Revised: 08/09/2017] [Accepted: 09/28/2017] [Indexed: 12/20/2022] Open
Abstract
Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - John Platig
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Maud Fagny
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Cho-Yi Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Joseph Nathaniel Paulson
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Camila Miranda Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Dawn Lisa DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - John Quackenbush
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
| | - Marieke Lydia Kuijjer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
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11
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Schlauch D, Paulson JN, Young A, Glass K, Quackenbush J. Estimating gene regulatory networks with pandaR. Bioinformatics 2017; 33:2232-2234. [PMID: 28334344 PMCID: PMC5870629 DOI: 10.1093/bioinformatics/btx139] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 03/07/2017] [Accepted: 03/09/2017] [Indexed: 12/11/2022] Open
Abstract
CONTACT johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu. AVAILABILITY AND IMPLEMENTATION PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.
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Affiliation(s)
- Daniel Schlauch
- Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Brigham and Women’s Hospital, Boston, MA, USA
| | - Joseph N Paulson
- Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Brigham and Women’s Hospital, Boston, MA, USA
| | - Albert Young
- Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Brigham and Women’s Hospital, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - John Quackenbush
- Department of Computational Biology and Biostatistics, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Brigham and Women’s Hospital, Boston, MA, USA
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