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Kim DH, Lee S, Ahn J, Kim JH, Lee E, Lee I, Byun S. Transcriptomic and metabolomic analysis unveils nanoplastic-induced gut barrier dysfunction via STAT1/6 and ERK pathways. ENVIRONMENTAL RESEARCH 2024; 249:118437. [PMID: 38346486 DOI: 10.1016/j.envres.2024.118437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/18/2024] [Accepted: 02/05/2024] [Indexed: 02/18/2024]
Abstract
The widespread prevalence of micro and nanoplastics in the environment raises concerns about their potential impact on human health. Recent evidence demonstrates the presence of nanoplastics in human blood and tissues following ingestion and inhalation, yet the specific risks and mechanisms of nanoplastic toxicity remain inadequately understood. In this study, we aimed to explore the molecular mechanisms underlying the toxicity of nanoplastics at both systemic and molecular levels by analyzing the transcriptomic/metabolomic responses and signaling pathways in the intestines of mice after oral administration of nanoplastics. Transcriptome analysis in nanoplastic-administered mice revealed a notable upregulation of genes involved in pro-inflammatory immune responses. In addition, nanoplastics substantially reduced the expression of tight junction proteins, including occludin, zonula occluden-1, and tricellulin, which are crucial for maintaining gut barrier integrity and function. Importantly, nanoplastic administration increased gut permeability and exacerbated dextran sulfate sodium-induced colitis. Further investigation into the underlying molecular mechanisms highlighted significant activation of signaling transsducer and activator of transcription (STAT)1 and STAT6 by nanoplastic administration, which was in line with the elevation of interferon and JAK-STAT pathway signatures identified through transcriptome enrichment analysis. Additionally, the consumption of nanoplastics specifically induced nuclear factor kappa-B (NF-κB) and extracellular signal-regulated kinase (ERK)1/2 signaling pathways in the intestines. Collectively, this study identifies molecular mechanisms contributing to adverse effects mediated by nanoplastics in the intestine, providing novel insights into the pathophysiological consequences of nanoplastic exposure.
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Affiliation(s)
- Da Hyun Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Sungho Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jisong Ahn
- Research Group of Traditional Food, Korea Food Research Institute, Wanju, 55365, Republic of Korea; Department of Food Science and Technology, Chonbuk National University, Jeonju, 54896, Republic of Korea
| | - Jae Hwan Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea
| | - Eunjung Lee
- Research Group of Traditional Food, Korea Food Research Institute, Wanju, 55365, Republic of Korea; Department of Food Biotechnology, Korea University of Science and Technology, Daejeon, Republic of Korea.
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea; POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
| | - Sanguine Byun
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea; POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
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2
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Sakamoto K, Nagao K. The Double-Stranded RNA Analog, Poly(I:C), Triggers Distinct Transcriptomic Shifts in Keratinocyte Subsets. J Invest Dermatol 2022; 142:2820-2823.e1. [PMID: 35395221 PMCID: PMC9509407 DOI: 10.1016/j.jid.2022.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/04/2022] [Accepted: 03/21/2022] [Indexed: 11/21/2022]
Affiliation(s)
- Keiko Sakamoto
- Cutaneous Leukocyte Biology Section, Dermatology Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Keisuke Nagao
- Cutaneous Leukocyte Biology Section, Dermatology Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA.
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3
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James K, Alsobhe A, Cockell SJ, Wipat A, Pocock M. Integration of probabilistic functional networks without an external Gold Standard. BMC Bioinformatics 2022; 23:302. [PMID: 35879662 PMCID: PMC9316706 DOI: 10.1186/s12859-022-04834-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Additionally, there typically are no Gold Standard data for non-model organisms. RESULTS We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping. In addition ssNet results in less loss of data and produces a more complete network. CONCLUSIONS The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source and with reduced data loss.
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Affiliation(s)
- Katherine James
- Department of Applied Sciences, Northumbria University, Sandyford Rd, Newcastle upon Tyne, NE1 8ST, UK. .,Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK.
| | - Aoesha Alsobhe
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK.,Saudi Electronic University, Abi Bakr As Siddiq Branch Rd, Riyadh, 1332, Saudi Arabia
| | - Simon J Cockell
- School of Biomedical, Nutritional and Sports Science, Faculty of Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK
| | - Matthew Pocock
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK
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4
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Zhang HB, Ding XB, Jin J, Guo WP, Yang QL, Chen PC, Yao H, Ruan L, Tao YT, Chen X. Predicted mouse interactome and network-based interpretation of differentially expressed genes. PLoS One 2022; 17:e0264174. [PMID: 35390003 PMCID: PMC8989236 DOI: 10.1371/journal.pone.0264174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 02/04/2022] [Indexed: 11/18/2022] Open
Abstract
The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn.
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Affiliation(s)
- Hai-Bo Zhang
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Xiao-Bao Ding
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Jie Jin
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Wen-Ping Guo
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Qiao-Lei Yang
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peng-Cheng Chen
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Heng Yao
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Li Ruan
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
| | - Yu-Tian Tao
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
- * E-mail: (YTT); (XC)
| | - Xin Chen
- Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics & Information Engineering, Taizhou University, Taizhou, China
- Institute of Pharmaceutical Biotechnology, School of Medicine, Zhejiang University, Hangzhou, China
- Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, Hangzhou, China
- * E-mail: (YTT); (XC)
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5
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:243-269. [DOI: 10.1093/bfgp/elac007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
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6
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Grimes T, Datta S. A novel probabilistic generator for large-scale gene association networks. PLoS One 2021; 16:e0259193. [PMID: 34767561 PMCID: PMC8589155 DOI: 10.1371/journal.pone.0259193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 10/14/2021] [Indexed: 11/18/2022] Open
Abstract
MOTIVATION Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators-such as GeneNetWeaver-are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. RESULTS We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used "scale-free" model is insufficient for replicating these structures. AVAILABILITY This generator is implemented in the R package "SeqNet" and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).
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Affiliation(s)
- Tyler Grimes
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, Florida, United States of America
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7
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Zhao J, Yao K, Yu H, Zhang L, Xu Y, Chen L, Sun Z, Zhu Y, Zhang C, Qian Y, Ji S, Pan H, Zhang M, Chen J, Correia C, Weiskittel T, Lin DW, Zhao Y, Chandrasekaran S, Fu X, Zhang D, Fan HY, Xie W, Li H, Hu Z, Zhang J. Metabolic remodelling during early mouse embryo development. Nat Metab 2021; 3:1372-1384. [PMID: 34650276 DOI: 10.1038/s42255-021-00464-x] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 08/31/2021] [Indexed: 01/09/2023]
Abstract
During early mammalian embryogenesis, changes in cell growth and proliferation depend on strict genetic and metabolic instructions. However, our understanding of metabolic reprogramming and its influence on epigenetic regulation in early embryo development remains elusive. Here we show a comprehensive metabolomics profiling of key stages in mouse early development and the two-cell and blastocyst embryos, and we reconstructed the metabolic landscape through the transition from totipotency to pluripotency. Our integrated metabolomics and transcriptomics analysis shows that while two-cell embryos favour methionine, polyamine and glutathione metabolism and stay in a more reductive state, blastocyst embryos have higher metabolites related to the mitochondrial tricarboxylic acid cycle, and present a more oxidative state. Moreover, we identify a reciprocal relationship between α-ketoglutarate (α-KG) and the competitive inhibitor of α-KG-dependent dioxygenases, L-2-hydroxyglutarate (L-2-HG), where two-cell embryos inherited from oocytes and one-cell zygotes display higher L-2-HG, whereas blastocysts show higher α-KG. Lastly, increasing 2-HG availability impedes erasure of global histone methylation markers after fertilization. Together, our data demonstrate dynamic and interconnected metabolic, transcriptional and epigenetic network remodelling during early mouse embryo development.
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Affiliation(s)
- Jing Zhao
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Ke Yao
- School of Pharmaceutical Sciences, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China
| | - Hua Yu
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Ling Zhang
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Zhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Yuyan Xu
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Lang Chen
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhen Sun
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yuqing Zhu
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Cheng Zhang
- Zhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Yuli Qian
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyan Ji
- Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, THU-PKU Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
| | - Hongru Pan
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Min Zhang
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jie Chen
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Cristina Correia
- Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, NY, USA
| | - Taylor Weiskittel
- Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, NY, USA
| | - Da-Wei Lin
- Center of Computational Medicine and Bioinformatics, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, Research Unit of Chinese Academy of Medical Sciences, East China University of Science and Technology, Shanghai, China
| | - Sriram Chandrasekaran
- Center of Computational Medicine and Bioinformatics, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Xudong Fu
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China
- Zhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, Hangzhou, China
| | - Dan Zhang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Heng-Yu Fan
- Life Sciences Institute, Zhejiang University, Hangzhou, China
| | - Wei Xie
- Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, THU-PKU Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, NY, USA
| | - Zeping Hu
- School of Pharmaceutical Sciences, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China.
| | - Jin Zhang
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University, Hangzhou, China.
- Zhejiang Laboratory for Systems & Precision Medicine, Zhejiang University Medical Center, Hangzhou, China.
- Institute of Hematology, Zhejiang University, Hangzhou, China.
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8
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K, Gkonta M, Hotova J, Kasionis I, Hatzis P, Pavlopoulos GA. Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review. Biomolecules 2021; 11:1245. [PMID: 34439912 PMCID: PMC8391349 DOI: 10.3390/biom11081245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Kleanthi Voutsadaki
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Ioannis Kasionis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
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9
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Cho JW, Son J, Ha SJ, Lee I. Systems biology analysis identifies TNFRSF9 as a functional marker of tumor-infiltrating regulatory T-cell enabling clinical outcome prediction in lung cancer. Comput Struct Biotechnol J 2021; 19:860-868. [PMID: 33598101 PMCID: PMC7851794 DOI: 10.1016/j.csbj.2021.01.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/17/2021] [Accepted: 01/18/2021] [Indexed: 12/21/2022] Open
Abstract
Regulatory T cells (Tregs) are enriched in the tumor microenvironment and play key roles in immune evasion of cancer cells. Cell surface markers specific for tumor-infiltrating Tregs (TI-Tregs) can be effectively targeted to enhance antitumor immunity and used for stratification of immunotherapy outcomes. Here, we present a systems biology approach to identify functional cell surface markers for TI-Tregs. We selected differentially expressed genes for surface proteins of TI-Tregs and compared these with other CD4+ T cells using bulk RNA-sequencing data from murine lung cancer models. Thereafter, we filtered for human orthologues with conserved expression in TI-Tregs using single-cell transcriptome data from patients with non-small cell lung cancer (NSCLC). To evaluate the functional importance of expression-based markers of TI-Tregs, we utilized network-based measure of context-associated centrality in a Treg-specific coregulatory network. We identified TNFRSF9 (also known as 4-1BB or CD137), a previously reported target for enhancing antitumor immunity, among the final candidates for TI-Treg markers with high functional importance score. We found that the low TNFRSF9 expression level in Tregs was associated with enhanced overall survival rate and response to anti-PD-1 immunotherapy in patients with NSCLC, proposing that TNFRSF9 promotes immune suppressive activity of Tregs in tumor. Collectively, these results demonstrated that integrative transcriptome and network analysis can facilitate the discovery of functional markers of tumor-specific immune cells to develop novel therapeutic targets and biomarkers for boosting cancer immunotherapy.
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Affiliation(s)
- Jae-Won Cho
- Department of Biotechnology, College of Life Science & Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Jimin Son
- Department of Biochemistry, College of Life Science & Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Sang-Jun Ha
- Department of Biochemistry, College of Life Science & Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science & Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
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10
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Lee J, Shah M, Ballouz S, Crow M, Gillis J. CoCoCoNet: conserved and comparative co-expression across a diverse set of species. Nucleic Acids Res 2020; 48:W566-W571. [PMID: 32392296 PMCID: PMC7319556 DOI: 10.1093/nar/gkaa348] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/21/2020] [Accepted: 04/24/2020] [Indexed: 12/19/2022] Open
Abstract
Co-expression analysis has provided insight into gene function in organisms from Arabidopsis to zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.
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Affiliation(s)
- John Lee
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Blvd., Woodbury, NY 11797, USA
| | - Manthan Shah
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Blvd., Woodbury, NY 11797, USA
| | - Sara Ballouz
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Blvd., Woodbury, NY 11797, USA
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Blvd., Woodbury, NY 11797, USA
| | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, 500 Sunnyside Blvd., Woodbury, NY 11797, USA
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11
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Zhang W, Zhang H, Yang H, Li M, Xie Z, Li W. Computational resources associating diseases with genotypes, phenotypes and exposures. Brief Bioinform 2020; 20:2098-2115. [PMID: 30102366 PMCID: PMC6954426 DOI: 10.1093/bib/bby071] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/01/2018] [Indexed: 12/16/2022] Open
Abstract
The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.
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Affiliation(s)
- Wenliang Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Huan Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Miaoxin Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhi Xie
- State Key Lab of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 500040, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
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12
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Bigan E, Sasidharan Nair S, Lejeune FX, Fragnaud H, Parmentier F, Mégret L, Verny M, Aaronson J, Rosinski J, Neri C. Genetic cooperativity in multi-layer networks implicates cell survival and senescence in the striatum of Huntington's disease mice synchronous to symptoms. Bioinformatics 2020; 36:186-196. [PMID: 31228193 PMCID: PMC6956776 DOI: 10.1093/bioinformatics/btz514] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 06/11/2019] [Accepted: 06/18/2019] [Indexed: 02/06/2023] Open
Abstract
Motivation Huntington’s disease (HD) may evolve through gene deregulation. However, the impact of gene deregulation on the dynamics of genetic cooperativity in HD remains poorly understood. Here, we built a multi-layer network model of temporal dynamics of genetic cooperativity in the brain of HD knock-in mice (allelic series of Hdh mice). To enhance biological precision and gene prioritization, we integrated three complementary families of source networks, all inferred from the same RNA-seq time series data in Hdh mice, into weighted-edge networks where an edge recapitulates path-length variation across source-networks and age-points. Results Weighted edge networks identify two consecutive waves of tight genetic cooperativity enriched in deregulated genes (critical phases), pre-symptomatically in the cortex, implicating neurotransmission, and symptomatically in the striatum, implicating cell survival (e.g. Hipk4) intertwined with cell proliferation (e.g. Scn4b) and cellular senescence (e.g. Cdkn2a products) responses. Top striatal weighted edges are enriched in modulators of defective behavior in invertebrate models of HD pathogenesis, validating their relevance to neuronal dysfunction in vivo. Collectively, these findings reveal highly dynamic temporal features of genetic cooperativity in the brain of Hdh mice where a 2-step logic highlights the importance of cellular maintenance and senescence in the striatum of symptomatic mice, providing highly prioritized targets. Availability and implementation Weighted edge network analysis (WENA) data and source codes for performing spectral decomposition of the signal (SDS) and WENA analysis, both written using Python, are available at http://www.broca.inserm.fr/HD-WENA/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Erwan Bigan
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | - Satish Sasidharan Nair
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | - François-Xavier Lejeune
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | - Hélissande Fragnaud
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | - Frédéric Parmentier
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | - Lucile Mégret
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | - Marc Verny
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
| | | | | | - Christian Neri
- Sorbonnes Université, Centre National de la Recherche Scientifique, Research Unit Biology of Adaptation and Aging (B2A), Team Compensation in Neurodegenerative Diseases and Aging (Brain-C), Paris F-75252, France
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13
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Giurgiu M, Reinhard J, Brauner B, Dunger-Kaltenbach I, Fobo G, Frishman G, Montrone C, Ruepp A. CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res 2020; 47:D559-D563. [PMID: 30357367 PMCID: PMC6323970 DOI: 10.1093/nar/gky973] [Citation(s) in RCA: 383] [Impact Index Per Article: 95.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 10/18/2018] [Indexed: 12/17/2022] Open
Abstract
CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (67%), mouse (15%) and rat (10%). Given the vital functions of these macromolecular machines, their identification and functional characterization is foundational to our understanding of normal and disease biology. The new CORUM 3.0 release encompasses 4274 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 4473 different genes, representing 22% of the protein coding genes in humans. Protein complexes are described by a protein complex name, subunit composition, cellular functions as well as the literature references. Information about stoichiometry of subunits depends on availability of experimental data. Recent developments include a graphical tool displaying known interactions between subunits. This allows the prediction of structural interconnections within protein complexes of unknown structure. In addition, we present a set of 58 protein complexes with alternatively spliced subunits. Those were found to affect cellular functions such as regulation of apoptotic activity, protein complex assembly or define cellular localization. CORUM is freely accessible at http://mips.helmholtz-muenchen.de/corum/.
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Affiliation(s)
- Madalina Giurgiu
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Julian Reinhard
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Barbara Brauner
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Irmtraud Dunger-Kaltenbach
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Gisela Fobo
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Goar Frishman
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Corinna Montrone
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
| | - Andreas Ruepp
- Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
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14
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Pourhaghighi R, Ash PEA, Phanse S, Goebels F, Hu LZM, Chen S, Zhang Y, Wierbowski SD, Boudeau S, Moutaoufik MT, Malty RH, Malolepsza E, Tsafou K, Nathan A, Cromar G, Guo H, Abdullatif AA, Apicco DJ, Becker LA, Gitler AD, Pulst SM, Youssef A, Hekman R, Havugimana PC, White CA, Blum BC, Ratti A, Bryant CD, Parkinson J, Lage K, Babu M, Yu H, Bader GD, Wolozin B, Emili A. BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain. Cell Syst 2020; 10:333-350.e14. [PMID: 32325033 PMCID: PMC7938770 DOI: 10.1016/j.cels.2020.03.003] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 11/25/2019] [Accepted: 03/20/2020] [Indexed: 12/12/2022]
Abstract
Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global "interactome" comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/.
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Affiliation(s)
- Reza Pourhaghighi
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Peter E A Ash
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Sadhna Phanse
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Department of Biochemistry, University of Regina, Regina, SK, Canada; Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Florian Goebels
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Lucas Z M Hu
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Siwei Chen
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA
| | - Yingying Zhang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA
| | - Shayne D Wierbowski
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA
| | - Samantha Boudeau
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | | | - Ramy H Malty
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Edyta Malolepsza
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Boston, MA, USA
| | - Kalliopi Tsafou
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Boston, MA, USA
| | - Aparna Nathan
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Boston, MA, USA
| | - Graham Cromar
- Program in Molecular Medicine, Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Hongbo Guo
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Ali Al Abdullatif
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Daniel J Apicco
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - Lindsay A Becker
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Aaron D Gitler
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Stefan M Pulst
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Ahmed Youssef
- Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA, USA
| | - Ryan Hekman
- Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA, USA
| | - Pierre C Havugimana
- Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA, USA; Departments of Biochemistry and Biology, Boston University, Boston, MA, USA
| | - Carl A White
- Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA, USA
| | - Benjamin C Blum
- Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA, USA
| | - Antonia Ratti
- Department of Neurology and Laboratory of Neuroscience, IRCCS, Milan, Italy
| | - Camron D Bryant
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA
| | - John Parkinson
- Program in Molecular Medicine, Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Kasper Lage
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Boston, MA, USA
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA
| | - Gary D Bader
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Benjamin Wolozin
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA; Program in Neuroscience, Boston University, Boston, MA, USA.
| | - Andrew Emili
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA; Department of Biochemistry, Boston University School of Medicine, Boston University, Boston, MA, USA; Departments of Biochemistry and Biology, Boston University, Boston, MA, USA.
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15
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Kim E, Bae D, Yang S, Ko G, Lee S, Lee B, Lee I. BiomeNet: a database for construction and analysis of functional interaction networks for any species with a sequenced genome. Bioinformatics 2020; 36:1584-1589. [PMID: 31599923 PMCID: PMC7703761 DOI: 10.1093/bioinformatics/btz776] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/01/2019] [Accepted: 10/08/2019] [Indexed: 01/03/2023] Open
Abstract
Motivation Owing to advanced DNA sequencing and genome assembly technology, the number of species with sequenced genomes is rapidly increasing. The aim of the recently launched Earth BioGenome Project is to sequence genomes of all eukaryotic species on Earth over the next 10 years, making it feasible to obtain genomic blueprints of the majority of animal and plant species by this time. Genetic models of the sequenced species will later be subject to functional annotation, and a comprehensive molecular network should facilitate functional analysis of individual genes and pathways. However, network databases are lagging behind genome sequencing projects as even the largest network database provides gene networks for less than 10% of sequenced eukaryotic genomes, and the knowledge gap between genomes and interactomes continues to widen. Results We present BiomeNet, a database of 95 scored networks comprising over 8 million co-functional links, which can build and analyze gene networks for any species with the sequenced genome. BiomeNet transfers functional interactions between orthologous proteins from source networks to the target species within minutes and automatically constructs gene networks with the quality comparable to that of existing networks. BiomeNet enables assembly of the first-in-species gene networks not available through other databases, which are highly predictive of diverse biological processes and can also provide network analysis by extracting subnetworks for individual biological processes and network-based gene prioritizations. These data indicate that BiomeNet could enhance the benefits of decoding the genomes of various species, thus improving our understanding of the Earth’ biodiversity. Availability and implementation The BiomeNet is freely available at http://kobic.re.kr/biomenet/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eiru Kim
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Dasom Bae
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Sunmo Yang
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Gunhwan Ko
- Korean Bioinformation Center, KRIBB, Yuseong-gu, Daejeon 34141, Korea
| | - Sungho Lee
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
| | - Byungwook Lee
- Korean Bioinformation Center, KRIBB, Yuseong-gu, Daejeon 34141, Korea
| | - Insuk Lee
- Department of Biotechnology, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
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16
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Lee S, Lee T, Yang S, Lee I. BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L. FRONTIERS IN PLANT SCIENCE 2020; 11:98. [PMID: 32133024 PMCID: PMC7040090 DOI: 10.3389/fpls.2020.00098] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 01/22/2020] [Indexed: 05/14/2023]
Abstract
Cultivated barley (Hordeum vulgare L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http://www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.
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Affiliation(s)
| | | | | | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, South Korea
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17
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Kim J, Yoon S, Nam D. netGO: R-Shiny package for network-integrated pathway enrichment analysis. Bioinformatics 2020; 36:3283-3285. [DOI: 10.1093/bioinformatics/btaa077] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/30/2019] [Accepted: 01/29/2020] [Indexed: 11/14/2022] Open
Abstract
Abstract
Summary
We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher’s exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS) data, netGO captured the relevant gene-sets better than existing tools, especially when analyzing a small number of genes. Specifically, netGO provides user-interactive visualization of the target genes, enriched gene-set and their network interactions for both netGO and FET results for further analysis. For this visualization, we also developed a standalone R-Shiny package shinyCyJS to connect R-shiny and the JavaScript version of cytoscape.
Availability and implementation
netGO R-Shiny package is freely available from github, https://github.com/unistbig/netGO.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Dougu Nam
- School of Life Sciences
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
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18
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Epithelial RABGEF1 deficiency promotes intestinal inflammation by dysregulating intrinsic MYD88-dependent innate signaling. Mucosal Immunol 2020; 13:96-109. [PMID: 31628426 DOI: 10.1038/s41385-019-0211-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 09/18/2019] [Accepted: 09/28/2019] [Indexed: 02/06/2023]
Abstract
Intestinal epithelial cells (IECs) contribute to the regulation of intestinal homeostasis and inflammation through their interactions with the environment and host immune responses. Yet our understanding of IEC-intrinsic regulatory pathways remains incomplete. Here, we identify the guanine nucleotide exchange factor RABGEF1 as a regulator of intestinal homeostasis and innate pathways dependent on IECs. Mice with IEC-specific Rabgef1 deletion (called Rabgef1IEC-KO mice) developed a delayed spontaneous colitis associated with the local upregulation of IEC chemokine expression. In mouse models of colitis based on Interleukin-10 deficiency or dextran sodium sulfate (DSS) exposure, we found that IEC-intrinsic RABGEF1 deficiency exacerbated development of intestinal pathology and dysregulated IEC innate pathways and chemokine expression. Mechanistically, we showed that RABGEF1 deficiency in mouse IECs in vitro was associated with an impairment of early endocytic events, an increased activation of the p38 mitogen-activated protein kinase (MAPK)-dependent pathway, and increased chemokine secretion. Moreover, we provided evidence that the development of spontaneous colitis was dependent on microbiota-derived signals and intrinsic MYD88-dependent pathways in vivo. Our study identifies mouse RABGEF1 as an important regulator of intestinal inflammation, MYD88-dependent IEC-intrinsic signaling, and chemokine production. This suggests that RABGEF1-dependent pathways represent interesting therapeutic targets for inflammatory conditions in the gut.
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19
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Han H, Lee S, Lee I. NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets. Mol Cells 2019; 42:579-588. [PMID: 31307154 PMCID: PMC6715341 DOI: 10.14348/molcells.2019.0065] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 06/28/2019] [Accepted: 06/30/2019] [Indexed: 11/27/2022] Open
Abstract
Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.
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Affiliation(s)
- Heonjong Han
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722,
Korea
| | - Sangyoung Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722,
Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722,
Korea
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722,
Korea
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20
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Perlasca P, Frasca M, Ba CT, Notaro M, Petrini A, Casiraghi E, Grossi G, Gliozzo J, Valentini G, Mesiti M. UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction. BMC Bioinformatics 2019; 20:422. [PMID: 31412768 PMCID: PMC6694573 DOI: 10.1186/s12859-019-2959-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 06/18/2019] [Indexed: 01/06/2023] Open
Abstract
Background One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate —in a function-specific fashion— the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. Results We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks —e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. Conclusions UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.
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Affiliation(s)
- Paolo Perlasca
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Marco Frasca
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Cheick Tidiane Ba
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Marco Notaro
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Alessandro Petrini
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Elena Casiraghi
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Giuliano Grossi
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Jessica Gliozzo
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.,Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Università degli Studi di Milano, Via della Commenda 10, Milano, 20122, Italy
| | - Giorgio Valentini
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy
| | - Marco Mesiti
- Department of Computer Science, Università degli Studi di Milano, Via Celoria 18, Milano, 20133, Italy.
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21
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Large-scale neuroanatomical study uncovers 198 gene associations in mouse brain morphogenesis. Nat Commun 2019; 10:3465. [PMID: 31371714 PMCID: PMC6671969 DOI: 10.1038/s41467-019-11431-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 07/13/2019] [Indexed: 01/03/2023] Open
Abstract
Brain morphogenesis is an important process contributing to higher-order cognition, however our knowledge about its biological basis is largely incomplete. Here we analyze 118 neuroanatomical parameters in 1,566 mutant mouse lines and identify 198 genes whose disruptions yield NeuroAnatomical Phenotypes (NAPs), mostly affecting structures implicated in brain connectivity. Groups of functionally similar NAP genes participate in pathways involving the cytoskeleton, the cell cycle and the synapse, display distinct fetal and postnatal brain expression dynamics and importantly, their disruption can yield convergent phenotypic patterns. 17% of human unique orthologues of mouse NAP genes are known loci for cognitive dysfunction. The remaining 83% constitute a vast pool of genes newly implicated in brain architecture, providing the largest study of mouse NAP genes and pathways. This offers a complementary resource to human genetic studies and predict that many more genes could be involved in mammalian brain morphogenesis. Brain morphogenesis is an important process contributing to higher-order cognition, however our knowledge about its biological basis is largely incomplete. Here, authors analyzed 118 neuroanatomical parameters in 1,566 mutant mouse lines to identify 198 genes whose disruptions yield neuroanatomical phenotypes
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22
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Lee T, Lee S, Yang S, Lee I. MaizeNet: a co-functional network for network-assisted systems genetics in Zea mays. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 99:571-582. [PMID: 31006149 DOI: 10.1111/tpj.14341] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 03/21/2019] [Accepted: 03/28/2019] [Indexed: 05/27/2023]
Abstract
Maize (Zea mays) has multiple uses in human food, animal fodder, starch and sweetener production and as a biofuel, and is accordingly the most extensively cultivated cereal worldwide. To enhance maize production, genetic factors underlying important agricultural traits, including stress tolerance and flowering, have been explored through forward and reverse genetics approaches. Co-functional gene networks are systems biology resources useful in identifying trait-associated genes in plants by prioritizing candidate genes. Here, we present MaizeNet (http://www.inetbio.org/maizenet/), a genome-scale co-functional network of Z. mays genes, and a companion web server for network-assisted systems genetics. We describe the validation of MaizeNet network quality and its ability to functionally predict molecular pathways and complex traits in maize. Furthermore, we demonstrate that MaizeNet-based prioritization of candidate genes can facilitate the identification of cell wall biosynthesis genes and detect network communities associated with flowering-time candidate genes derived from genome-wide association studies. The demonstrated gene prioritization and subnetwork analysis can be conducted by simply submitting maize gene models based on the commonly used B73 RefGen_v3 and the latest B73 RefGen_v4 reference genomes on the MaizeNet web server. MaizeNet-based network-assisted systems genetics will substantially accelerate the discovery of trait-associated genes for crop improvement.
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Affiliation(s)
- Tak Lee
- Department of Biotechnology, College of Life Sciences and Biotechnology, Yonsei University, Seoul, 03722, Korea
| | - Sungho Lee
- Department of Biotechnology, College of Life Sciences and Biotechnology, Yonsei University, Seoul, 03722, Korea
| | - Sunmo Yang
- Department of Biotechnology, College of Life Sciences and Biotechnology, Yonsei University, Seoul, 03722, Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Sciences and Biotechnology, Yonsei University, Seoul, 03722, Korea
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23
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Ke YD, Chan G, Stefanoska K, Au C, Bi M, Müller J, Przybyla M, Feiten A, Prikas E, Halliday GM, Piguet O, Kiernan MC, Kassiou M, Hodges JR, Loy CT, Mattick JS, Ittner A, Kril JJ, Sutherland GT, Ittner LM. CNS cell type-specific gene profiling of P301S tau transgenic mice identifies genes dysregulated by progressive tau accumulation. J Biol Chem 2019; 294:14149-14162. [PMID: 31366728 DOI: 10.1074/jbc.ra118.005263] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 07/24/2019] [Indexed: 12/20/2022] Open
Abstract
The microtubule-associated protein tau undergoes aberrant modification resulting in insoluble brain deposits in various neurodegenerative diseases, including frontotemporal dementia (FTD), progressive supranuclear palsy, and corticobasal degeneration. Tau aggregates can form in different cell types of the central nervous system (CNS) but are most prevalent in neurons. We have previously recapitulated aspects of human FTD in mouse models by overexpressing mutant human tau in CNS neurons, including a P301S tau variant in TAU58/2 mice, characterized by early-onset and progressive behavioral deficits and FTD-like neuropathology. The molecular mechanisms underlying the functional deficits of TAU58/2 mice remain mostly elusive. Here, we employed functional genomics (i.e. RNAseq) to determine differentially expressed genes in young and aged TAU58/2 mice to identify alterations in cellular processes that may contribute to neuropathy. We identified genes in cortical brain samples differentially regulated between young and old TAU58/2 mice relative to nontransgenic littermates and by comparative analysis with a dataset of CNS cell type-specific genes expressed in nontransgenic mice. Most differentially-regulated genes had known or putative roles in neurons and included presynaptic and excitatory genes. Specifically, we observed changes in presynaptic factors, glutamatergic signaling, and protein scaffolding. Moreover, in the aged mice, expression levels of several genes whose expression was annotated to occur in other brain cell types were altered. Immunoblotting and immunostaining of brain samples from the TAU58/2 mice confirmed altered expression and localization of identified and network-linked proteins. Our results have revealed genes dysregulated by progressive tau accumulation in an FTD mouse model.
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Affiliation(s)
- Yazi D Ke
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Gabriella Chan
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Kristie Stefanoska
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Carol Au
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Mian Bi
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Julius Müller
- The Jenner Institute, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Magdalena Przybyla
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Astrid Feiten
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Emmanuel Prikas
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Glenda M Halliday
- Brain and Mind Centre, University of Sydney, Sydney, New South Wales 2005, Australia
| | - Olivier Piguet
- Brain and Mind Centre, University of Sydney, Sydney, New South Wales 2005, Australia.,School of Psychology, University of Sydney, Sydney, New South Wales 2005, Australia.,ARC Centre of Excellence in Cognition and Its Disorders, University of Sydney, Sydney, New South Wales 2005, Australia
| | - Matthew C Kiernan
- Brain and Mind Centre, University of Sydney, Sydney, New South Wales 2005, Australia.,Institute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, New South Wales 2005, Australia
| | - Michael Kassiou
- School of Chemistry, University of Sydney, Sydney, New South Wales 2005, Australia
| | - John R Hodges
- Brain and Mind Centre, University of Sydney, Sydney, New South Wales 2005, Australia
| | - Clement T Loy
- Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia.,St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, New South Wales 2010, Australia.,Sydney School of Public Health, University of Sydney, New South Wales 2006, Australia
| | - John S Mattick
- Garvan Institute of Medical Research, Darlinghurst, New South Wales 2010, Australia.,St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, New South Wales 2010, Australia
| | - Arne Ittner
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Jillian J Kril
- Charles Perkins Centre and Discipline of Pathology, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales 2005, Australia
| | - Greg T Sutherland
- Charles Perkins Centre and Discipline of Pathology, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales 2005, Australia
| | - Lars M Ittner
- Dementia Research Centre and Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
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24
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Schäfer M, Klein HU, Schwender H. Integrative analysis of multiple genomic variables using a hierarchical Bayesian model. Bioinformatics 2018; 33:3220-3227. [PMID: 28582573 DOI: 10.1093/bioinformatics/btx356] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 05/31/2017] [Indexed: 12/13/2022] Open
Abstract
Motivation Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels. Results We propose a coefficient quantifying the degree to which genes present consistent alterations in multiple (more than two) genomic variables when comparing samples presenting a condition of interest (e.g. cancer) to a reference group. A hierarchical Bayesian model is employed to assess uncertainty on a gene level, incorporating information on functional relationships between genes. We demonstrate the approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histone modification measurements. Both the coefficient-based ranking and the inference based on the model lead to a plausible prioritizing of candidate genes when analyzing multiple genomic variables. Availability and implementation BUGS code in the Supplement. Contact m.schaefer@uni-duesseldorf.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Martin Schäfer
- Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany
| | - Hans-Ulrich Klein
- Program in Translational Neuropsychiatric Genomics, Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA.,Harvard Medical School, Boston, MA 02115, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02141, USA
| | - Holger Schwender
- Mathematical Institute, Heinrich Heine University, D-40225 Düsseldorf, Germany
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25
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Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, Yang S, Kim CY, Lee M, Kim E, Lee S, Kang B, Jeong D, Kim Y, Jeon HN, Jung H, Nam S, Chung M, Kim JH, Lee I. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 2018; 46:D380-D386. [PMID: 29087512 PMCID: PMC5753191 DOI: 10.1093/nar/gkx1013] [Citation(s) in RCA: 1013] [Impact Index Per Article: 168.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 10/02/2017] [Accepted: 10/13/2017] [Indexed: 02/07/2023] Open
Abstract
Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation. Here, we present TRRUST v2 (www.grnpedia.org/trrust) with a significant improvement from the previous version, including a significantly increased size of the database consisting of 8444 regulatory interactions for 800 TFs in humans. More importantly, TRRUST v2 also contains a database for TF-target interactions in mice, including 6552 TF-target interactions for 828 mouse TFs. TRRUST v2 is also substantially more comprehensive and less biased than other TF-target interaction databases. We also improved the web interface, which now enables prioritization of key TFs for a physiological condition depicted by a set of user-input transcriptional responsive genes. With the significant expansion in the database size and inclusion of the new web tool for TF prioritization, we believe that TRRUST v2 will be a versatile database for the study of the transcriptional regulation involved in human diseases.
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Affiliation(s)
- Heonjong Han
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Jae-Won Cho
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Sangyoung Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Ayoung Yun
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Hyojin Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Dasom Bae
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Sunmo Yang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Chan Yeong Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Muyoung Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Eunbeen Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Sungho Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Byunghee Kang
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Dabin Jeong
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Yaeji Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Hyeon-Nae Jeon
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Haein Jung
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Sunhwee Nam
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Michael Chung
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
| | - Jong-Hoon Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Korea University, Seoul 02841, Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Korea
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26
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Knowlton MN, Smith CL. Naming CRISPR alleles: endonuclease-mediated mutation nomenclature across species. Mamm Genome 2017; 28:367-376. [PMID: 28589392 PMCID: PMC5569137 DOI: 10.1007/s00335-017-9698-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 05/27/2017] [Indexed: 12/29/2022]
Abstract
The widespread use of CRISPR/Cas and other targeted endonuclease technologies in many species has led to an explosion in the generation of new mutations and alleles. The ability to generate many different mutations from the same target sequence either by homology-directed repair with a donor sequence or non-homologous end joining-induced insertions and deletions necessitates a means for representing these mutations in literature and databases. Standardized nomenclature can be used to generate unambiguous, concise, and specific symbols to represent mutations and alleles. The research communities of a variety of species using CRISPR/Cas and other endonuclease-mediated mutation technologies have developed different approaches to naming and identifying such alleles and mutations. While some organism-specific research communities have developed allele nomenclature that incorporates the method of generation within the official allele or mutant symbol, others use metadata tags that include method of generation or mutagen. Organism-specific research community databases together with organism-specific nomenclature committees are leading the way in providing standardized nomenclature and metadata to facilitate the integration of data from alleles and mutations generated using CRISPR/Cas and other targeted endonucleases.
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Affiliation(s)
| | - Cynthia L Smith
- Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, 04609, USA
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27
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Kim E, Lee I. Network-Based Gene Function Prediction in Mouse and Other Model Vertebrates Using MouseNet Server. Methods Mol Biol 2017; 1611:183-198. [PMID: 28451980 DOI: 10.1007/978-1-4939-7015-5_14] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The mouse, Mus musculus, is a popular model organism for the study of human genes involved in development, immunology, and disease phenotypes. Despite recent revolutions in gene-knockout technologies in mouse, identification of candidate genes for functions of interest can further accelerate the discovery of novel gene functions. The collaborative nature of genetic functions allows for the inference of gene functions based on the principle of guilt-by-association. Genome-scale co-functional networks could therefore provide functional predictions for genes via network analysis. We recently constructed such a network for mouse (MouseNet), which interconnects over 88% of protein-coding genes with 788,080 functional relationships. The companion web server ( www.inetbio.org/mousenet ) enables researchers with no bioinformatics expertise to generate predictions that facilitate discovery of novel gene functions. In this chapter, we present the theoretical framework for MouseNet, as well as step-by-step instructions and technical tips for functional prediction of genes and pathways in mouse and other model vertebrates.
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Affiliation(s)
- Eiru Kim
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Insuk Lee
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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28
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Li P, Tompkins RG, Xiao W. KERIS: kaleidoscope of gene responses to inflammation between species. Nucleic Acids Res 2016; 45:D908-D914. [PMID: 27789704 PMCID: PMC5210651 DOI: 10.1093/nar/gkw974] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 09/29/2016] [Accepted: 10/22/2016] [Indexed: 12/29/2022] Open
Abstract
A cornerstone of modern biomedical research is the use of animal models to study disease mechanisms and to develop new therapeutic approaches. In order to help the research community to better explore the similarities and differences of genomic response between human inflammatory diseases and murine models, we developed KERIS: kaleidoscope of gene responses to inflammation between species (available at http://www.igenomed.org/keris/). As of June 2016, KERIS includes comparisons of the genomic response of six human inflammatory diseases (burns, trauma, infection, sepsis, endotoxin and acute respiratory distress syndrome) and matched mouse models, using 2257 curated samples from the Inflammation and the Host Response to Injury Glue Grant studies and other representative studies in Gene Expression Omnibus. A researcher can browse, query, visualize and compare the response patterns of genes, pathways and functional modules across different diseases and corresponding murine models. The database is expected to help biologists choosing models when studying the mechanisms of particular genes and pathways in a disease and prioritizing the translation of findings from disease models into clinical studies.
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Affiliation(s)
- Peng Li
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Ronald G Tompkins
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Wenzhong Xiao
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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29
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PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants. Sci Rep 2016; 6:31356. [PMID: 27515999 PMCID: PMC4981870 DOI: 10.1038/srep31356] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 07/18/2016] [Indexed: 01/05/2023] Open
Abstract
Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants.
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