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Rodríguez-López M, Bordin N, Lees J, Scholes H, Hassan S, Saintain Q, Kamrad S, Orengo C, Bähler J. Broad functional profiling of fission yeast proteins using phenomics and machine learning. eLife 2023; 12:RP88229. [PMID: 37787768 PMCID: PMC10547477 DOI: 10.7554/elife.88229] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023] Open
Abstract
Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through 'guilt by association' with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.
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Affiliation(s)
- María Rodríguez-López
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
| | - Nicola Bordin
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
| | - Jon Lees
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
- University of BristolBristolUnited Kingdom
| | - Harry Scholes
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
| | - Shaimaa Hassan
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
- Helwan University, Faculty of PharmacyCairoEgypt
| | - Quentin Saintain
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
| | - Stephan Kamrad
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
| | - Christine Orengo
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
| | - Jürg Bähler
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
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2
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Gabryelska MM, Conn SJ. The RNA interactome in the Hallmarks of Cancer. WILEY INTERDISCIPLINARY REVIEWS. RNA 2023; 14:e1786. [PMID: 37042179 PMCID: PMC10909452 DOI: 10.1002/wrna.1786] [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: 01/24/2023] [Revised: 03/12/2023] [Accepted: 03/20/2023] [Indexed: 04/13/2023]
Abstract
Ribonucleic acid (RNA) molecules are indispensable for cellular homeostasis in healthy and malignant cells. However, the functions of RNA extend well beyond that of a protein-coding template. Rather, both coding and non-coding RNA molecules function through critical interactions with a plethora of cellular molecules, including other RNAs, DNA, and proteins. Deconvoluting this RNA interactome, including the interacting partners, the nature of the interaction, and dynamic changes of these interactions in malignancies has yielded fundamental advances in knowledge and are emerging as a novel therapeutic strategy in cancer. Here, we present an RNA-centric review of recent advances in the field of RNA-RNA, RNA-protein, and RNA-DNA interactomic network analysis and their impact across the Hallmarks of Cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.
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Affiliation(s)
- Marta M Gabryelska
- Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
| | - Simon J Conn
- Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Flinders University, Bedford Park, South Australia, Australia
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3
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Sun YH, Wu YL, Liao BY. Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism. J Biomed Sci 2023; 30:58. [PMID: 37525275 PMCID: PMC10388531 DOI: 10.1186/s12929-023-00959-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.
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Affiliation(s)
- Y Henry Sun
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Zhunan, Miaoli, Taiwan.
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan.
| | - Yueh-Lin Wu
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Zhunan, Miaoli, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wei-Gong Memorial Hospital, Miaoli, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan
| | - Ben-Yang Liao
- Institute of Population Health Sciences, National Health Research Institute, Zhunan, Miaoli, Taiwan
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Weng H, Song W, Fu K, Guan Y, Cai G, Huang E, Chen X, Zou H, Ye Q. Proteomic profiling reveals the potential mechanisms and regulatory targets of sirtuin 4 in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced Parkinson's mouse model. Front Neurosci 2023; 16:1035444. [PMID: 36760798 PMCID: PMC9905825 DOI: 10.3389/fnins.2022.1035444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/06/2022] [Indexed: 01/26/2023] Open
Abstract
Introduction Parkinson's disease (PD), as a common neurodegenerative disease, currently has no effective therapeutic approaches to delay or stop its progression. There is an urgent need to further define its pathogenesis and develop new therapeutic targets. An increasing number of studies have shown that members of the sirtuin (SIRT) family are differentially involved in neurodegenerative diseases, indicating their potential to serve as targets in therapeutic strategies. Mitochondrial SIRT4 possesses multiple enzymatic activities, such as deacetylase, ADP ribosyltransferase, lipoamidase, and deacylase activities, and exhibits different enzymatic activities and target substrates in different tissues and cells; thus, mitochondrial SIRT4 plays an integral role in regulating metabolism. However, the role and mechanism of SIRT4 in PD are not fully understood. This study aimed to investigate the potential mechanism and possible regulatory targets of SIRT4 in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mice. Methods The expression of the SIRT4 protein in the MPTP-induced PD mouse mice or key familial Parkinson disease protein 7 knockout (DJ-1 KO) rat was compared against the control group by western blot assay. Afterwards, quantitative proteomics and bioinformatics analyses were performed to identify altered proteins in the vitro model and reveal the possible functional role of SIRT4. The most promising molecular target of SIRT4 were screened and validated by viral transfection, western blot assay and reverse transcription quantitative PCR (RT-qPCR) assays. Results The expression of the SIRT4 protein was found to be altered both in the MPTP-induced PD mouse mice and DJ-1KO rats. Following the viral transfection of SIRT4, a quantitative proteomics analysis identified 5,094 altered proteins in the vitro model, including 213 significantly upregulated proteins and 222 significantly downregulated proteins. The results from bioinformatics analyses indicated that SIRT4 mainly affected the ribosomal pathway, propionate metabolism pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway and peroxisome pathway in cells, and we screened 25 potential molecular targets. Finally, only fatty acid binding protein 4 (FABP4) in the PPAR signaling pathway was regulated by SIRT4 among the 25 molecules. Importantly, the alterations in FABP4 and PPARγ were verified in the MPTP-induced PD mouse model. Discussion Our results indicated that FABP4 in the PPAR signaling pathway is the most promising molecular target of SIRT4 in an MPTP-induced mouse model and revealed the possible functional role of SIRT4. This study provides a reference for future drug development and mechanism research with SIRT4 as a target or biomarker.
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Affiliation(s)
- Huidan Weng
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China,Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou, China
| | - Wenjing Song
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China,Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou, China
| | - Kangyue Fu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yunqian Guan
- Cell Therapy Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guoen Cai
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China,Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou, China
| | - En Huang
- The School of Basic Medical Sciences, Fujian Key Laboratory of Brain Aging and Neurodegenerative Diseases, Fujian Medical University, Fuzhou, China
| | - Xiaochun Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China,Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou, China
| | - Haiqiang Zou
- Department of Neurosurgery, General Hospital of Southern Theatre Command, PLA, Guangzhou, Guangdong, China,Haiqiang Zou,
| | - Qinyong Ye
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China,Fujian Key Laboratory of Molecular Neurology, Institute of Neuroscience, Fujian Medical University, Fuzhou, China,*Correspondence: Qinyong Ye,
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5
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Braberg H, Echeverria I, Kaake RM, Sali A, Krogan NJ. From systems to structure - using genetic data to model protein structures. Nat Rev Genet 2022; 23:342-354. [PMID: 35013567 PMCID: PMC8744059 DOI: 10.1038/s41576-021-00441-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2021] [Indexed: 12/11/2022]
Abstract
Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Gladstone Institutes, San Francisco, CA, USA.
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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6
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Miki T, Kajiwara K, Nakayama S, Hashimoto M, Mihara H. Effects of Hydrophobic Residues on the Intracellular Self-Assembly of De Novo Designed Peptide Tags and Their Orthogonality. ACS Synth Biol 2022; 11:2144-2153. [PMID: 35302350 DOI: 10.1021/acssynbio.2c00058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein assemblies forming nano- to micro-sized structures underlie versatile biological events in living systems. For mimicking and engineering these protein assemblies through a bottom-up approach, self-assembling peptides (SAPs) that form nanofibril structures via β-sheets serve as potential practical tags. Nevertheless, the development of SAP tags is still in its infancy, and insight into the relationship between peptide sequences and intracellular self-assembly is limited. In this study, we focused on hydrophobic residues in SAPs and examined the self-assembly of SAP-tagged superfolder GFPs (green fluorescent proteins) in COS-7 cells. Based on XEXK (X; hydrophobic amino acids: F, L, I, V, W, or Y) sequence units, we designed a panel of Xn peptides with different hydrophobic residues (X) and chain lengths (n). We observed that the self-assembly propensity, the size of the assemblies, the influence on protein denaturation, and the subcellular localization differed significantly depending on the hydrophobic amino acid. F9, L9, I7, and V13 peptides formed μm-scaled granules, W13 formed small oligomeric clusters in the cytoplasm, and Y15 formed assemblies in the nucleus. In addition, we investigated the orthogonality of their interaction. Strikingly, W13- and Y15-tagged proteins interacted independently and formed two distinct assemblies in cells. Herein, we have demonstrated the great opportunities for rationalizing artificial protein assemblies and orthogonal structures in an intracellular context using the designed SAPs.
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Affiliation(s)
- Takayuki Miki
- School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
| | - Keigo Kajiwara
- School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
| | - Sae Nakayama
- School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
| | - Masahiro Hashimoto
- School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
| | - Hisakazu Mihara
- School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
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7
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Pairwise Biological Network Alignment Based on Discrete Bat Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5548993. [PMID: 34777564 PMCID: PMC8580637 DOI: 10.1155/2021/5548993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/18/2022]
Abstract
The development of high-throughput technology has provided a reliable technical guarantee for an increased amount of available data on biological networks. Network alignment is used to analyze these data to identify conserved functional network modules and understand evolutionary relationships across species. Thus, an efficient computational network aligner is needed for network alignment. In this paper, the classic bat algorithm is discretized and applied to the network alignment. The bat algorithm initializes the population randomly and then searches for the optimal solution iteratively. Based on the bat algorithm, the global pairwise alignment algorithm BatAlign is proposed. In BatAlign, the individual velocity and the position are represented by a discrete code. BatAlign uses a search algorithm based on objective function that uses the number of conserved edges as the objective function. The similarity between the networks is used to initialize the population. The experimental results showed that the algorithm was able to match proteins with high functional consistency and reach a relatively high topological quality.
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8
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Fernández-Torras A, Comajuncosa-Creus A, Duran-Frigola M, Aloy P. Connecting chemistry and biology through molecular descriptors. Curr Opin Chem Biol 2021; 66:102090. [PMID: 34626922 DOI: 10.1016/j.cbpa.2021.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/23/2021] [Accepted: 09/03/2021] [Indexed: 01/14/2023]
Abstract
Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.
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Affiliation(s)
- Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Arnau Comajuncosa-Creus
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Ersilia Open Source Initiative, Cambridge, United Kingdom
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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9
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Anchang CG, Xu C, Raimondo MG, Atreya R, Maier A, Schett G, Zaburdaev V, Rauber S, Ramming A. The Potential of OMICs Technologies for the Treatment of Immune-Mediated Inflammatory Diseases. Int J Mol Sci 2021; 22:ijms22147506. [PMID: 34299122 PMCID: PMC8306614 DOI: 10.3390/ijms22147506] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 01/08/2023] Open
Abstract
Immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel diseases and inflammatory arthritis (e.g., rheumatoid arthritis, psoriatic arthritis), are marked by increasing worldwide incidence rates. Apart from irreversible damage of the affected tissue, the systemic nature of these diseases heightens the incidence of cardiovascular insults and colitis-associated neoplasia. Only 40–60% of patients respond to currently used standard-of-care immunotherapies. In addition to this limited long-term effectiveness, all current therapies have to be given on a lifelong basis as they are unable to specifically reprogram the inflammatory process and thus achieve a true cure of the disease. On the other hand, the development of various OMICs technologies is considered as “the great hope” for improving the treatment of IMIDs. This review sheds light on the progressive development and the numerous approaches from basic science that gradually lead to the transfer from “bench to bedside” and the implementation into general patient care procedures.
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Affiliation(s)
- Charles Gwellem Anchang
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Cong Xu
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Maria Gabriella Raimondo
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Raja Atreya
- Department of Internal Medicine 1, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany;
| | - Andreas Maier
- Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany;
| | - Georg Schett
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Vasily Zaburdaev
- Max-Planck-Zentrum für Physik und Medizin, 91054 Erlangen, Germany;
- Department of Biology, Mathematics in Life Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Simon Rauber
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
| | - Andreas Ramming
- Department of Internal Medicine 3—Rheumatology and Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum, 91054 Erlangen, Germany; (C.G.A.); (C.X.); (M.G.R.); (G.S.); (S.R.)
- Correspondence: ; Tel.: +49-9131-8543048; Fax: +49-9131-8536448
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10
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Canto AM, Matos AHB, Godoi AB, Vieira AS, Aoyama BB, Rocha CS, Henning B, Carvalho BS, Pascoal VDB, Veiga DFT, Gilioli R, Cendes F, Lopes-Cendes I. Multi-omics analysis suggests enhanced epileptogenesis in the Cornu Ammonis 3 of the pilocarpine model of mesial temporal lobe epilepsy. Hippocampus 2020; 31:122-139. [PMID: 33037862 DOI: 10.1002/hipo.23268] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/04/2020] [Accepted: 09/26/2020] [Indexed: 12/11/2022]
Abstract
Mesial temporal lobe epilepsy (MTLE) is a chronic neurological disorder characterized by the occurrence of seizures, and histopathological abnormalities in the mesial temporal lobe structures, mainly hippocampal sclerosis (HS). We used a multi-omics approach to determine the profile of transcript and protein expression in the dorsal and ventral hippocampal dentate gyrus (DG) and Cornu Ammonis 3 (CA3) in an animal model of MTLE induced by pilocarpine. We performed label-free proteomics and RNAseq from laser-microdissected tissue isolated from pilocarpine-induced Wistar rats. We divided the DG and CA3 into dorsal and ventral areas and analyzed them separately. We performed a data integration analysis and evaluated enriched signaling pathways, as well as the integrated networks generated based on the gene ontology processes. Our results indicate differences in the transcriptomic and proteomic profiles among the DG and the CA3 subfields of the hippocampus. Moreover, our data suggest that epileptogenesis is enhanced in the CA3 region when compared to the DG, with most abnormalities in transcript and protein levels occurring in the CA3. Furthermore, our results show that the epileptogenesis in the pilocarpine model involves predominantly abnormal regulation of excitatory neuronal mechanisms mediated by N-methyl D-aspartate (NMDA) receptors, changes in the serotonin signaling, and neuronal activity controlled by calcium/calmodulin-dependent protein kinase (CaMK) regulation and leucine-rich repeat kinase 2 (LRRK2)/WNT signaling pathways.
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Affiliation(s)
- Amanda M Canto
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - Alexandre H B Matos
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - Alexandre B Godoi
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - André S Vieira
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil.,Department of Structural and Functional Biology, Institute of Biology. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Beatriz B Aoyama
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil.,Department of Structural and Functional Biology, Institute of Biology. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Cristiane S Rocha
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - Barbara Henning
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
| | - Benilton S Carvalho
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil.,Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Vinicius D B Pascoal
- Department of Basic Sciences, Fluminense Federal University (UFF), Nova Friburgo, Rio de Janeiroz, Brazil
| | - Diogo F T Veiga
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Rovilson Gilioli
- Laboratory of Animal Quality Control, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Fernando Cendes
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil.,Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Iscia Lopes-Cendes
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences. University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.,Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil
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11
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Buccitelli C, Selbach M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 2020; 21:630-644. [PMID: 32709985 DOI: 10.1038/s41576-020-0258-4] [Citation(s) in RCA: 502] [Impact Index Per Article: 125.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2020] [Indexed: 12/15/2022]
Abstract
Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA-protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.
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Affiliation(s)
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany. .,Charité - Universitätsmedizin Berlin, Berlin, Germany.
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12
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Kennedy SA, Jarboui MA, Srihari S, Raso C, Bryan K, Dernayka L, Charitou T, Bernal-Llinares M, Herrera-Montavez C, Krstic A, Matallanas D, Kotlyar M, Jurisica I, Curak J, Wong V, Stagljar I, LeBihan T, Imrie L, Pillai P, Lynn MA, Fasterius E, Al-Khalili Szigyarto C, Breen J, Kiel C, Serrano L, Rauch N, Rukhlenko O, Kholodenko BN, Iglesias-Martinez LF, Ryan CJ, Pilkington R, Cammareri P, Sansom O, Shave S, Auer M, Horn N, Klose F, Ueffing M, Boldt K, Lynn DJ, Kolch W. Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRAS G13D. Nat Commun 2020; 11:499. [PMID: 31980649 PMCID: PMC6981206 DOI: 10.1038/s41467-019-14224-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 12/05/2019] [Indexed: 02/07/2023] Open
Abstract
Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.
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Affiliation(s)
- Susan A Kennedy
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Mohamed-Ali Jarboui
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Werner Siemens Imaging Center, University of Tübingen, Tübingen, Germany
| | - Sriganesh Srihari
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
- QIMR-Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Cinzia Raso
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Kenneth Bryan
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Layal Dernayka
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Theodosia Charitou
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Manuel Bernal-Llinares
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | | | | | - David Matallanas
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovak Republic
| | - Jasna Curak
- Donnelly Centre, University of Toronto, Toronto, Canada
- Department of Biochemistry, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Victoria Wong
- Donnelly Centre, University of Toronto, Toronto, Canada
- Department of Biochemistry, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Igor Stagljar
- Donnelly Centre, University of Toronto, Toronto, Canada
- Department of Biochemistry, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Mediterranean Institute for Life Sciences, Split, Croatia
| | - Thierry LeBihan
- Synthetic and Systems Biology, University of Edinburgh, Edinburgh, UK
| | - Lisa Imrie
- Synthetic and Systems Biology, University of Edinburgh, Edinburgh, UK
| | - Priyanka Pillai
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Miriam A Lynn
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - Erik Fasterius
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Cristina Al-Khalili Szigyarto
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - James Breen
- School of Biological Sciences, University of Adelaide Bioinformatics Hub, Adelaide, SA, Australia
- Computational & Systems Biology Program, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Christina Kiel
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Conway Institute, University College Dublin, Dublin, Ireland
| | - Luis Serrano
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Nora Rauch
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | - Boris N Kholodenko
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Conway Institute, University College Dublin, Dublin, Ireland
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | | | - Colm J Ryan
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
- School of Computer Science, University College Dublin, Dublin, Ireland
| | - Ruth Pilkington
- Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | | | - Owen Sansom
- Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Studies, Glasgow University, Glasgow, UK
| | - Steven Shave
- School of Biological Sciences and School of Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Manfred Auer
- School of Biological Sciences and School of Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Nicola Horn
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Franziska Klose
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Marius Ueffing
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Karsten Boldt
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
| | - David J Lynn
- EMBL Australia Group, South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia.
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
| | - Walter Kolch
- Systems Biology Ireland, University College Dublin, Dublin, Ireland.
- Conway Institute, University College Dublin, Dublin, Ireland.
- School of Medicine, University College Dublin, Dublin, Ireland.
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13
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Abstract
Macromolecular complexes play a key role in cellular function. Predicting the structure and dynamics of these complexes is one of the key challenges in structural biology. Docking applications have traditionally been used to predict pairwise interactions between proteins. However, few methods exist for modeling multi-protein assemblies. Here we present two methods, CombDock and DockStar, that can predict multi-protein assemblies starting from subunit structural models. CombDock can assemble subunits without any assumptions about the pairwise interactions between subunits, while DockStar relies on the interaction graph or, alternatively, a homology model or a cryo-electron microscopy (EM) density map of the entire complex. We demonstrate the two methods using RNA polymerase II with 12 subunits and TRiC/CCT chaperonin with 16 subunits.
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Affiliation(s)
- Dina Schneidman-Duhovny
- School of Computer Science and Engineering and the Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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14
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Ung CY, Ghanat Bari M, Zhang C, Liang J, Correia C, Li H. Regulostat Inferelator: a novel network biology platform to uncover molecular devices that predetermine cellular response phenotypes. Nucleic Acids Res 2019; 47:e82. [PMID: 31114928 PMCID: PMC6698671 DOI: 10.1093/nar/gkz417] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 04/18/2019] [Accepted: 05/17/2019] [Indexed: 12/24/2022] Open
Abstract
With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary ‘on’ or ‘off’ response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify ‘regulostat’ constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of fine-tuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug–regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.
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Affiliation(s)
- Choong Yong Ung
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Mehrab Ghanat Bari
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Cheng Zhang
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Jingjing Liang
- Department of Population and Quantitative Health Science, Case Western Reserve University, Cleveland, OH, USA
| | - Cristina Correia
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
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15
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Malod-Dognin N, Pržulj N. Functional geometry of protein interactomes. Bioinformatics 2019; 35:3727-3734. [PMID: 30821317 DOI: 10.1093/bioinformatics/btz146] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 01/15/2019] [Accepted: 02/25/2019] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) are usually modeled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactomes, including protein complexes. RESULTS To model the multi-scale organization of these complex biological systems, we utilize simplicial complexes from computational geometry. The question is how to mine these new representations of protein interactomes to reveal additional biological information. To address this, we define simplets, a generalization of graphlets to simplicial complexes. By using simplets, we define a sensitive measure of similarity between simplicial complex representations that allows for clustering them according to their data types better than clustering them by using other state-of-the-art measures, e.g. spectral distance, or facet distribution distance. We model human and baker's yeast protein interactomes as simplicial complexes that capture PPIs and protein complexes as simplices. On these models, we show that our newly introduced simplet-based methods cluster proteins by function better than the clustering methods that use the standard PPI networks, uncovering the new underlying functional organization of the cell. We demonstrate the existence of the functional geometry in the protein interactome data and the superiority of our simplet-based methods to effectively mine for new biological information hidden in the complexity of the higher-order organization of protein interactomes. AVAILABILITY AND IMPLEMENTATION Codes and datasets are freely available at http://www0.cs.ucl.ac.uk/staff/natasa/Simplets/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Noël Malod-Dognin
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona, Spain
| | - Nataša Pržulj
- Department of Life Sciences, Barcelona Supercomputing Center, Barcelona, Spain.,ICREA, Pg. Lluís Companys 23, Barcelona, Spain
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16
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Tan A, Huang H, Zhang P, Li S. Network-based cancer precision medicine: A new emerging paradigm. Cancer Lett 2019; 458:39-45. [DOI: 10.1016/j.canlet.2019.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/29/2019] [Accepted: 05/15/2019] [Indexed: 12/20/2022]
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17
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Sundah NR, Ho NRY, Lim GS, Natalia A, Ding X, Liu Y, Seet JE, Chan CW, Loh TP, Shao H. Barcoded DNA nanostructures for the multiplexed profiling of subcellular protein distribution. Nat Biomed Eng 2019; 3:684-694. [PMID: 31285580 DOI: 10.1038/s41551-019-0417-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 05/14/2019] [Indexed: 11/09/2022]
Abstract
Massively parallel DNA sequencing is established, yet high-throughput protein profiling remains challenging. Here, we report a barcoding approach that leverages the combinatorial sequence content and the configurational programmability of DNA nanostructures for high-throughput multiplexed profiling of the subcellular expression and distribution of proteins in whole cells. The barcodes are formed by in situ hybridization of tetrahedral DNA nanostructures and short DNA sequences conjugated with protein-targeting antibodies, and by nanostructure-assisted ligation (either enzymatic or chemical) of the nanostructures and exogenous DNA sequences bound to nanoparticles of different sizes (which cause these localization sequences to differentially distribute across subcellular compartments). Compared with linear DNA barcoding, the nanostructured barcodes enhance the signal by more than 100-fold. By implementing the barcoding approach on a microfluidic device for the analysis of rare patient samples, we show that molecular subtypes of breast cancer can be accurately classified and that subcellular spatial markers of disease aggressiveness can be identified.
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Affiliation(s)
- Noah R Sundah
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore.,Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Nicholas R Y Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Geok Soon Lim
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Auginia Natalia
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Xianguang Ding
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Yu Liu
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore.,Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
| | - Ju Ee Seet
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Ching Wan Chan
- Department of Surgery, National University Hospital, Singapore, Singapore.,Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tze Ping Loh
- Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.,Department of Laboratory Medicine, National University Hospital, Singapore, Singapore
| | - Huilin Shao
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore. .,Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore. .,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore. .,Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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18
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Chung NC, Mirza B, Choi H, Wang J, Wang D, Ping P, Wang W. Unsupervised classification of multi-omics data during cardiac remodeling using deep learning. Methods 2019; 166:66-73. [PMID: 30853547 DOI: 10.1016/j.ymeth.2019.03.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 03/04/2019] [Indexed: 12/20/2022] Open
Abstract
Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.
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Affiliation(s)
- Neo Christopher Chung
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.
| | - Bilal Mirza
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Howard Choi
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jie Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Ding Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Peipei Ping
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine (Cardiology), University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Wei Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA; Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA.
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19
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Mirza B, Wang W, Wang J, Choi H, Chung NC, Ping P. Machine Learning and Integrative Analysis of Biomedical Big Data. Genes (Basel) 2019; 10:E87. [PMID: 30696086 PMCID: PMC6410075 DOI: 10.3390/genes10020087] [Citation(s) in RCA: 157] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 01/08/2019] [Accepted: 01/21/2019] [Indexed: 12/11/2022] Open
Abstract
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.
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Affiliation(s)
- Bilal Mirza
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
| | - Wei Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA.
| | - Jie Wang
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
| | - Howard Choi
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA.
| | - Neo Christopher Chung
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.
| | - Peipei Ping
- NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Physiology, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Scalable Analytics Institute (ScAi), University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA 90095, USA.
- Department of Medicine (Cardiology), University of California Los Angeles, Los Angeles, CA 90095, USA.
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20
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Klobucar K, Brown ED. Use of genetic and chemical synthetic lethality as probes of complexity in bacterial cell systems. FEMS Microbiol Rev 2018; 42:4563584. [PMID: 29069427 DOI: 10.1093/femsre/fux054] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 10/23/2017] [Indexed: 12/22/2022] Open
Abstract
Different conditions and genomic contexts are known to have an impact on gene essentiality and interactions. Synthetic lethal interactions occur when a combination of perturbations, either genetic or chemical, result in a more profound fitness defect than expected based on the effect of each perturbation alone. Synthetic lethality in bacterial systems has long been studied; however, during the past decade, the emerging fields of genomics and chemical genomics have led to an increase in the scale and throughput of these studies. Here, we review the concepts of genomics and chemical genomics in the context of synthetic lethality and their revolutionary roles in uncovering novel biology such as the characterization of genes of unknown function and in antibacterial drug discovery. We provide an overview of the methodologies, examples and challenges of both genetic and chemical synthetic lethal screening platforms. Finally, we discuss how to apply genetic and chemical synthetic lethal approaches to rationalize the synergies of drugs, screen for new and improved antibacterial therapies and predict drug mechanism of action.
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Affiliation(s)
- Kristina Klobucar
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main St West, Hamilton, ON L8N 3Z5, Canada
| | - Eric D Brown
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main St West, Hamilton, ON L8N 3Z5, Canada
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21
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Meyer K, Kirchner M, Uyar B, Cheng JY, Russo G, Hernandez-Miranda LR, Szymborska A, Zauber H, Rudolph IM, Willnow TE, Akalin A, Haucke V, Gerhardt H, Birchmeier C, Kühn R, Krauss M, Diecke S, Pascual JM, Selbach M. Mutations in Disordered Regions Can Cause Disease by Creating Dileucine Motifs. Cell 2018; 175:239-253.e17. [PMID: 30197081 DOI: 10.1016/j.cell.2018.08.019] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 06/09/2018] [Accepted: 08/08/2018] [Indexed: 01/12/2023]
Abstract
Many disease-causing missense mutations affect intrinsically disordered regions (IDRs) of proteins, but the molecular mechanism of their pathogenicity is enigmatic. Here, we employ a peptide-based proteomic screen to investigate the impact of mutations in IDRs on protein-protein interactions. We find that mutations in disordered cytosolic regions of three transmembrane proteins (GLUT1, ITPR1, and CACNA1H) lead to an increased clathrin binding. All three mutations create dileucine motifs known to mediate clathrin-dependent trafficking. Follow-up experiments on GLUT1 (SLC2A1), the glucose transporter causative of GLUT1 deficiency syndrome, revealed that the mutated protein mislocalizes to intracellular compartments. Mutant GLUT1 interacts with adaptor proteins (APs) in vitro, and knocking down AP-2 reverts the cellular mislocalization and restores glucose transport. A systematic analysis of other known disease-causing variants revealed a significant and specific overrepresentation of gained dileucine motifs in structurally disordered cytosolic domains of transmembrane proteins. Thus, several mutations in disordered regions appear to cause "dileucineopathies."
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Affiliation(s)
- Katrina Meyer
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Marieluise Kirchner
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Bora Uyar
- Bioinformatics Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Jing-Yuan Cheng
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Giulia Russo
- Molecular Pharmacology and Cell Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Luis R Hernandez-Miranda
- Developmental Biology/Signal Transduction, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Anna Szymborska
- Integrative Vascular Biology Laboratory, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research) partner site, 13347 Berlin, Germany
| | - Henrik Zauber
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Ina-Maria Rudolph
- Molecular Cardiovascular Research, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Thomas E Willnow
- Molecular Cardiovascular Research, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Altuna Akalin
- Bioinformatics Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Volker Haucke
- Molecular Pharmacology and Cell Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Holger Gerhardt
- Integrative Vascular Biology Laboratory, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany; DZHK (German Centre for Cardiovascular Research) partner site, 13347 Berlin, Germany; Berlin Institute of Health (BIH), 10178 Berlin, Germany
| | - Carmen Birchmeier
- Developmental Biology/Signal Transduction, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Ralf Kühn
- Berlin Institute of Health (BIH), 10178 Berlin, Germany; Core Facility Transgenics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Michael Krauss
- Molecular Pharmacology and Cell Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Sebastian Diecke
- DZHK (German Centre for Cardiovascular Research) partner site, 13347 Berlin, Germany; Berlin Institute of Health (BIH), 10178 Berlin, Germany; Core Facility Pluripotent Stem Cells, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany
| | - Juan M Pascual
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390, USA
| | - Matthias Selbach
- Proteome Dynamics, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-Rössle-Str. 10, 13125 Berlin, Germany; Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
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22
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Franks AM, Markowetz F, Airoldi EM. REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES. Ann Appl Stat 2018; 12:1361-1384. [PMID: 36506698 PMCID: PMC9733905 DOI: 10.1214/16-aoas915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.
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Affiliation(s)
- Alexander M Franks
- Department of Statistics and, Applied Probability, University of California, Santa Barbara, South Hall, Santa Barbara, California 93106, USA
| | - Florian Markowetz
- Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Edoardo M Airoldi
- Fox School of Business, Department of Statistical Science, Temple University, Center for Data Science, 1810 Liacouras Walk, Philadelphia, Pennsylvania 19122, USA
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23
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Sanyal S, Molnarova L, Richterova J, Huraiova B, Benko Z, Polakova S, Cipakova I, Sevcovicova A, Gaplovska-Kysela K, Mechtler K, Cipak L, Gregan J. Mutations that prevent methylation of cohesin render sensitivity to DNA damage in S. pombe. J Cell Sci 2018; 131:jcs214924. [PMID: 29898918 PMCID: PMC6051343 DOI: 10.1242/jcs.214924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 06/04/2018] [Indexed: 01/18/2023] Open
Abstract
The canonical role of cohesin is to mediate sister chromatid cohesion. In addition, cohesin plays important roles in processes such as DNA repair and regulation of gene expression. Mounting evidence suggests that various post-translational modifications, including phosphorylation, acetylation and sumoylation regulate cohesin functions. Our mass spectrometry analysis of cohesin purified from Schizosaccharomyces pombe cells revealed that the cohesin subunit Psm1 is methylated on two evolutionarily conserved lysine residues, K536 and K1200. We found that mutations that prevent methylation of Psm1 K536 and K1200 render sensitivity to DNA-damaging agents and show positive genetic interactions with mutations in genes encoding the Mus81-Eme1 endonuclease. Yeast two-hybrid and co-immunoprecipitation assays showed that there were interactions between subunits of the cohesin and Mus81-Eme1 complexes. We conclude that cohesin is methylated and that mutations that prevent methylation of Psm1 K536 and K1200 show synthetic phenotypes with mutants defective in the homologous recombination DNA repair pathway.
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Affiliation(s)
- Swastika Sanyal
- Department of Chromosome Biology, MFPL, University of Vienna, Dr. Bohr-Gasse 9, 1030 Vienna, Austria
| | - Lucia Molnarova
- Department of Genetics, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 842 15 Bratislava, Slovakia
| | - Judita Richterova
- Department of Genetics, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 842 15 Bratislava, Slovakia
| | - Barbora Huraiova
- Department of Genetics, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 842 15 Bratislava, Slovakia
| | - Zsigmond Benko
- Department of Membrane Biochemistry, Institute of Animal Biochemistry and Genetics, Centre of Biosciences, Slovak Academy of Sciences, 84505 Bratislava, Slovakia
| | - Silvia Polakova
- Department of Membrane Biochemistry, Institute of Animal Biochemistry and Genetics, Centre of Biosciences, Slovak Academy of Sciences, 84505 Bratislava, Slovakia
| | - Ingrid Cipakova
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy of Sciences, Dubravska cesta 9, 84505 Bratislava, Slovakia
| | - Andrea Sevcovicova
- Department of Genetics, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 842 15 Bratislava, Slovakia
| | - Katarina Gaplovska-Kysela
- Department of Genetics, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 842 15 Bratislava, Slovakia
| | - Karl Mechtler
- Research Institute of Molecular Pathology, Campus-Vienna-Biocenter 1, 1030 Vienna, Austria
| | - Lubos Cipak
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy of Sciences, Dubravska cesta 9, 84505 Bratislava, Slovakia
| | - Juraj Gregan
- Department of Chromosome Biology, MFPL, University of Vienna, Dr. Bohr-Gasse 9, 1030 Vienna, Austria
- Department of Genetics, Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovicova 6, 842 15 Bratislava, Slovakia
- Advanced Microscopy Facility, Vienna Biocenter Core Facilities, Dr. Bohr-Gasse 3, 1030 Vienna, Austria
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24
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Kauppi K, Rosenthal SB, Lo MT, Sanyal N, Jiang M, Abagyan R, McEvoy LK, Andreassen OA, Chen CH. Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia. Am J Psychiatry 2018; 175:674-682. [PMID: 29495895 PMCID: PMC6028303 DOI: 10.1176/appi.ajp.2017.17040410] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product. METHOD The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328). RESULTS Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms. CONCLUSIONS The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.
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Affiliation(s)
- Karolina Kauppi
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Sara Brin Rosenthal
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Min-Tzu Lo
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Nilotpal Sanyal
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Mian Jiang
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Ruben Abagyan
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Linda K McEvoy
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Ole A Andreassen
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
| | - Chi-Hua Chen
- From the Center for Multimodal Imaging and Genetics, the Department of Radiology, the Center for Computational Biology and Bioinformatics, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, Calif.; the Department of Radiation Sciences, Umeå University, Umeå, Sweden; and NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo
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25
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Dihazi H, Asif AR, Beißbarth T, Bohrer R, Feussner K, Feussner I, Jahn O, Lenz C, Majcherczyk A, Schmidt B, Schmitt K, Urlaub H, Valerius O. Integrative omics - from data to biology. Expert Rev Proteomics 2018; 15:463-466. [DOI: 10.1080/14789450.2018.1476143] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Hassan Dihazi
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Nephrology and Rheumatology, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Abdul R. Asif
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Clinical Chemistry/UMG-Laborateries, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Tim Beißbarth
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Department of Medical Statistics, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Rainer Bohrer
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Gesellschaft für Wissenschaftlische Datenverarbeitung mbH, Göttingen, Germany
| | - Kirstin Feussner
- Göttingen Metabolomics and Lipidomics Platform (GMLP), Göttingen, Germany
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Göttingen, Germany
| | - Ivo Feussner
- Göttingen Metabolomics and Lipidomics Platform (GMLP), Göttingen, Germany
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Göttingen, Germany
| | - Olaf Jahn
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Proteomics Group, Max Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Christof Lenz
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Clinical Chemistry/UMG-Laborateries, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
- Bioanalytical Mass Spectrometry, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Andrzej Majcherczyk
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Büsgen-Institute, Section Molecular Wood Biotechnology and Technical Mycology, University of Göttingen, Göttingen, Germany
| | - Bernhard Schmidt
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Department of Cellular Biochemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Kerstin Schmitt
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany
| | - Henning Urlaub
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Clinical Chemistry/UMG-Laborateries, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
- Bioanalytical Mass Spectrometry, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Oliver Valerius
- Göttingen Proteomics Forum (GPF), Göttingen, Germany
- Institute for Microbiology and Genetics, University of Göttingen, Göttingen, Germany
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26
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Abstract
Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.
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Affiliation(s)
- Konrad J Karczewski
- Massachusetts General Hospital, Boston, MA, USA
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
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27
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Tiys ES, Ivanisenko TV, Demenkov PS, Ivanisenko VA. FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets. BMC Genomics 2018; 19:76. [PMID: 29504895 PMCID: PMC5836822 DOI: 10.1186/s12864-018-4474-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background Estimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members. Results We developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones. Conclusions FunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/. Electronic supplementary material The online version of this article (10.1186/s12864-018-4474-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Evgeny S Tiys
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia. .,Laboratory of Computer Genomics, Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia.
| | - Timofey V Ivanisenko
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia.,Laboratory of Computer Genomics, Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia
| | - Pavel S Demenkov
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia
| | - Vladimir A Ivanisenko
- The Institute of Cytology and Genetics, The Siberian Branch of the Russian Academy of Sciences, Prospekt Lavrentyeva 10, 630090, Novosibirsk, Russia
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28
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Wang X, Huang L. Dissecting Dynamic and Heterogeneous Proteasome Complexes Using In Vivo Cross-Linking-Assisted Affinity Purification and Mass Spectrometry. Methods Mol Biol 2018; 1844:401-410. [PMID: 30242723 DOI: 10.1007/978-1-4939-8706-1_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Protein-protein interactions are essential for protein complex formation and function. Affinity purification coupled with mass spectrometry (AP-MS) is the method of choice for studying protein-protein interactions at the systems level under different physiological conditions. Although effective in capturing stable protein interactions, transient, weak, and/or dynamic interactors are often lost due to extended procedures during conventional AP-MS experiments. To circumvent this problem, we have recently developed XAP (in vivo cross-linking (X)-assisted affinity purification)-MS strategy to better preserve dynamic protein complexes under native lysis conditions. In addition, we have developed XBAP (in vivo cross-linking (X)-assisted bimolecular tandem affinity purification)-MS method by incorporating XAP with bimolecular affinity purification to define dynamic and heterogeneous protein subcomplexes. Here we describe general experimental protocols of XAP- and XBAP-MS to study dynamic protein complexes and their subcomplexes, respectively. Specifically, we present their applications in capturing and identifying proteasome dynamic interactors and ubiquitin receptor (UbR)-proteasome subcomplexes.
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Affiliation(s)
- Xiaorong Wang
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA
| | - Lan Huang
- Department of Physiology and Biophysics, University of California, Irvine, CA, USA.
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29
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DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions. Cell Rep 2017; 17:303-315. [PMID: 27681439 DOI: 10.1016/j.celrep.2016.08.095] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 05/06/2016] [Accepted: 08/29/2016] [Indexed: 01/06/2023] Open
Abstract
We adapted the yeast 2-hybrid assay to simultaneously uncover multiple transient protein interactions within a single screen by using a strategy termed DEEPN (dynamic enrichment for evaluation of protein networks). This approach incorporates high-throughput DNA sequencing and computation to follow competition among a plasmid population encoding interacting partners. To demonstrate the capacity of DEEPN, we identify a wide range of ubiquitin-binding proteins, including interactors that we verify biochemically. To demonstrate the specificity of DEEPN, we show that DEEPN allows simultaneous comparison of candidate interactors across multiple bait proteins, allowing differential interactions to be identified. This feature was used to identify interactors that distinguish between GTP- and GDP-bound conformations of Rab5.
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30
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Abstract
A complete understanding of human cancer variants requires new methods to systematically and efficiently assess the functional effects of genomic mutations at a large scale. Here, we describe a set of tools to rapidly clone and stratify thousands of cancer mutations at base resolution. This protocol provides a massively parallel pipeline to achieve high stringency and throughput. The approach includes high-throughput generation of mutant clones by Gateway, confirmation of variant identity by barcoding and next-generation sequencing, and stratification of cancer variants by multiplexed interaction profiling. Compared with alternative site-directed mutagenesis methods, our protocol requires less sequencing effort and enables robust statistical calling of allele-specific effects. To ensure the precision of variant interaction profiling, we further describe two complementary methods-a high-throughput enhanced yeast two-hybrid (HT-eY2H) assay and a mammalian-cell-based Gaussia princeps luciferase protein-fragment complementation assay (GPCA). These independent assays with standard controls validate mutational interaction profiles with high quality. This protocol provides experimentally derived guidelines for classifying candidate cancer alleles emerging from whole-genome or whole-exome sequencing projects as 'drivers' or 'passengers'. For ∼100 genomic mutations, the protocol-including target primer design, variant library construction, and sequence verification-can be completed within as little as 2-3 weeks, and cancer variant stratification can be completed within 2 weeks.
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31
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Green VA, Pelkmans L. A Systems Survey of Progressive Host-Cell Reorganization during Rotavirus Infection. Cell Host Microbe 2017; 20:107-20. [PMID: 27414499 DOI: 10.1016/j.chom.2016.06.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 04/12/2016] [Accepted: 05/24/2016] [Indexed: 12/19/2022]
Abstract
Pathogen invasion is often accompanied by widespread alterations in cellular physiology, which reflects the hijacking of host factors and processes for pathogen entry and replication. Although genetic perturbation screens have revealed the complexity of host factors involved for numerous pathogens, it has remained challenging to temporally define the progression of events in host cell reorganization during infection. We combine high-confidence genome-scale RNAi screening of host factors required for rotavirus infection in human intestinal cells with an innovative approach to infer the trajectory of virus infection from fixed cell populations. This approach reveals a comprehensive network of host cellular processes involved in rotavirus infection and implicates AMPK in initiating the development of a rotavirus-permissive environment. Our work provides a powerful approach that can be generalized to order complex host cellular requirements along a trajectory of cellular reorganization during pathogen invasion.
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Affiliation(s)
- Victoria A Green
- Faculty of Sciences, Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
| | - Lucas Pelkmans
- Faculty of Sciences, Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
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32
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Malod-Dognin N, Pržulj N. Omics Data Complementarity Underlines Functional Cross-Communication in Yeast. J Integr Bioinform 2017; 14:/j/jib.ahead-of-print/jib-2017-0018/jib-2017-0018.xml. [PMID: 28600905 PMCID: PMC6042824 DOI: 10.1515/jib-2017-0018] [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: 03/22/2017] [Accepted: 04/18/2017] [Indexed: 11/26/2022] Open
Abstract
Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker’s yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein–protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.
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Huttlin EL, Bruckner RJ, Paulo JA, Cannon JR, Ting L, Baltier K, Colby G, Gebreab F, Gygi MP, Parzen H, Szpyt J, Tam S, Zarraga G, Pontano-Vaites L, Swarup S, White AE, Schweppe DK, Rad R, Erickson BK, Obar RA, Guruharsha KG, Li K, Artavanis-Tsakonas S, Gygi SP, Harper JW. Architecture of the human interactome defines protein communities and disease networks. Nature 2017; 545:505-509. [PMID: 28514442 PMCID: PMC5531611 DOI: 10.1038/nature22366] [Citation(s) in RCA: 961] [Impact Index Per Article: 137.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Accepted: 04/11/2017] [Indexed: 02/07/2023]
Abstract
The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.
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Affiliation(s)
- Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Raphael J Bruckner
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Joe R Cannon
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Lily Ting
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Kurt Baltier
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Greg Colby
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Fana Gebreab
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Melanie P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Hannah Parzen
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - John Szpyt
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Stanley Tam
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Gabriela Zarraga
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Laura Pontano-Vaites
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Sharan Swarup
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Anne E White
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Devin K Schweppe
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Ramin Rad
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Brian K Erickson
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Robert A Obar
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.,Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - K G Guruharsha
- Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - Kejie Li
- Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - Spyros Artavanis-Tsakonas
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.,Biogen Inc., 250 Binney Street, Cambridge, Massachusetts 02142, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - J Wade Harper
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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34
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Li S, Li R, Wang H, Li L, Li H, Li Y. The Key Genes of Chronic Pancreatitis which Bridge Chronic Pancreatitis and Pancreatic Cancer Can be Therapeutic Targets. Pathol Oncol Res 2017; 24:215-222. [PMID: 28435988 DOI: 10.1007/s12253-017-0217-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 03/24/2017] [Indexed: 01/15/2023]
Abstract
An important question in systems biology is what role the underlying molecular mechanisms play in disease progression. The relationship between chronic pancreatitis and pancreatic cancer needs further exploration in a system view. We constructed the disease network based on gene expression data and protein-protein interaction. We proposed an approach to discover the underlying core network and molecular factors in the progression of pancreatic diseases, which contain stages of chronic pancreatitis and pancreatic cancer. The chronic pancreatitis and pancreatic cancer core network and key factors were revealed and then verified by gene set enrichment analysis of pathways and diseases. The key factors provide the microenvironment for tumor initiation and the change of gene expression level of key factors bridge chronic pancreatitis and pancreatic cancer. Some new candidate genes need further verification by experiments. Transcriptome profiling-based network analysis reveals the importance of chronic pancreatitis genes and pathways in pancreatic cancer development on a system level by computational method and they can be therapeutic targets.
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Affiliation(s)
- Shuang Li
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Rui Li
- National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China
| | - Heping Wang
- Department of Neurosurgery, Tongji Hospital, Tongji Medical School, Wuhan, China
| | - Lisha Li
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, China.
| | - Huiyu Li
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Yulin Li
- The Key Laboratory of Pathobiology, Ministry of Education, College of Basic Medical Sciences, Jilin University, Changchun, China
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35
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Abstract
Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.
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36
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Yi S, Lin S, Li Y, Zhao W, Mills GB, Sahni N. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 2017; 18:395-410. [PMID: 28344341 DOI: 10.1038/nrg.2017.8] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants, including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signalling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is crucial to understanding the complex pleiotropic effect of cancer genes and provides a possible link between genotype and phenotype in cancer.
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Affiliation(s)
- Song Yi
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Shengda Lin
- Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Yongsheng Li
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Wei Zhao
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Gordon B Mills
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Nidhi Sahni
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.,Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030, USA
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Rallis C, Townsend S, Bähler J. Genetic interactions and functional analyses of the fission yeast gsk3 and amk2 single and double mutants defective in TORC1-dependent processes. Sci Rep 2017; 7:44257. [PMID: 28281664 PMCID: PMC5345095 DOI: 10.1038/srep44257] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 02/06/2017] [Indexed: 01/03/2023] Open
Abstract
The Target of Rapamycin (TOR) signalling network plays important roles in aging and disease. The AMP-activated protein kinase (AMPK) and the Gsk3 kinase inhibit TOR during stress. We performed genetic interaction screens using synthetic genetic arrays (SGA) with gsk3 and amk2 as query mutants, the latter encoding the regulatory subunit of AMPK. We identified 69 negative and 82 positive common genetic interactors, with functions related to cellular growth and stress. The 120 gsk3-specific negative interactors included genes functioning in translation and ribosomes. The 215 amk2-specific negative interactors included genes functioning in chromatin silencing and DNA damage repair. Both amk2- and gsk3-specific interactors were enriched in phenotype categories related to abnormal cell size and shape. We also performed SGA screen with the amk2 gsk3 double mutant as a query. Mutants sensitive to 5-fluorouracil, an anticancer drug are under-represented within the 305 positive interactors specific for the amk2 gsk3 query. The triple-mutant SGA screen showed higher number of negative interactions than the double mutant SGA screens and uncovered additional genetic network information. These results reveal common and specialized roles of AMPK and Gsk3 in mediating TOR-dependent processes, indicating that AMPK and Gsk3 act in parallel to inhibit TOR function in fission yeast.
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Affiliation(s)
- Charalampos Rallis
- Research Department of Genetics, Evolution &Environment and UCL Institute of Healthy Ageing, University College London, Gower Street, WC1E 6BT, London, UK
| | - StJohn Townsend
- Research Department of Genetics, Evolution &Environment and UCL Institute of Healthy Ageing, University College London, Gower Street, WC1E 6BT, London, UK
| | - Jürg Bähler
- Research Department of Genetics, Evolution &Environment and UCL Institute of Healthy Ageing, University College London, Gower Street, WC1E 6BT, London, UK
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38
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Winkler JD, Halweg-Edwards AL, Erickson KE, Choudhury A, Pines G, Gill RT. The Resistome: A Comprehensive Database of Escherichia coli Resistance Phenotypes. ACS Synth Biol 2016; 5:1566-1577. [PMID: 27438180 DOI: 10.1021/acssynbio.6b00150] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The microbial ability to resist stressful environmental conditions and chemical inhibitors is of great industrial and medical interest. Much of the data related to mutation-based stress resistance, however, is scattered through the academic literature, making it difficult to apply systematic analyses to this wealth of information. To address this issue, we introduce the Resistome database: a literature-curated collection of Escherichia coli genotypes-phenotypes containing over 5,000 mutants that resist hundreds of compounds and environmental conditions. We use the Resistome to understand our current state of knowledge regarding resistance and to detect potential synergy or antagonism between resistance phenotypes. Our data set represents one of the most comprehensive collections of genomic data related to resistance currently available. Future development will focus on the construction of a combined genomic-transcriptomic-proteomic framework for understanding E. coli's resistance biology. The Resistome can be downloaded at https://bitbucket.org/jdwinkler/resistome_release/overview .
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Affiliation(s)
- James D. Winkler
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Andrea L. Halweg-Edwards
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Keesha E. Erickson
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Alaksh Choudhury
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Gur Pines
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Ryan T. Gill
- Department
of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States
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39
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Big data mining powers fungal research: recent advances in fission yeast systems biology approaches. Curr Genet 2016; 63:427-433. [PMID: 27730285 DOI: 10.1007/s00294-016-0657-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 10/04/2016] [Accepted: 10/05/2016] [Indexed: 01/05/2023]
Abstract
Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.
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40
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Abstract
During the past 20 years, the studies on genetics or pharmacogenomics of primary hypertension provided interesting results supporting the role of genetics, but no actionable finding ready to be translated into personalized medicine. Two types of approaches have been applied: a "hypothesis-driven" approach on the candidate genes, coding for proteins involved in the biochemical machinery underlying the regulation of BP, and an "unbiased hypothesis-free" approach with GWAS, based on the randomness principles of frequentist statistics. During the past 10-15 years, the application of the latter has overtaken the application of the former leading to an enlargement of the number of previously unknown candidate loci or genes but without any actionable result for the therapy of hypertension. In the present review, we summarize the results of our hypothesis-driven approach based on studies carried out in rats with genetic hypertension and in humans with essential hypertension at the pre-hypertensive and early hypertensive stages. These studies led to the identification of mutant adducin and endogenous ouabain as candidate genetic-molecular mechanisms in both species. Rostafuroxin has been developed for its ability to selectively correct Na(+) pump abnormalities sustained by the two abovementioned mechanisms and to selectively reduce BP in rats and in humans carrying the gene variants underlying the mutant adducin and endogenous ouabain (EO) effects. A clinical trial is ongoing to substantiate these findings. Future studies should apply both the candidate gene and GWAS approaches to fully exploit the potential of genetics in optimizing the personalized therapy.
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41
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Janke R, Kong J, Braberg H, Cantin G, Yates JR, Krogan NJ, Heyer WD. Nonsense-mediated decay regulates key components of homologous recombination. Nucleic Acids Res 2016; 44:5218-30. [PMID: 27001511 PMCID: PMC4914092 DOI: 10.1093/nar/gkw182] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2016] [Revised: 03/08/2016] [Accepted: 03/09/2016] [Indexed: 12/29/2022] Open
Abstract
Cells frequently experience DNA damage that requires repair by homologous recombination (HR). Proteins involved in HR are carefully coordinated to ensure proper and efficient repair without interfering with normal cellular processes. In Saccharomyces cerevisiae, Rad55 functions in the early steps of HR and is regulated in response to DNA damage through phosphorylation by the Mec1 and Rad53 kinases of the DNA damage response. To further identify regulatory processes that target HR, we performed a high-throughput genetic interaction screen with RAD55 phosphorylation site mutants. Genes involved in the mRNA quality control process, nonsense-mediated decay (NMD), were found to genetically interact with rad55 phospho-site mutants. Further characterization revealed that RAD55 transcript and protein levels are regulated by NMD. Regulation of HR by NMD extends to multiple targets beyond RAD55, including RAD51, RAD54 and RAD57 Finally, we demonstrate that loss of NMD results in an increase in recombination rates and resistance to the DNA damaging agent methyl methanesulfonate, suggesting this pathway negatively regulates HR under normal growth conditions.
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Affiliation(s)
- Ryan Janke
- Department of Microbiology & Molecular Genetics, University of California, Davis, CA 95616-8665, USA
| | - Jeremy Kong
- Department of Microbiology & Molecular Genetics, University of California, Davis, CA 95616-8665, USA
| | - Hannes Braberg
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158-2517, USA
| | - Greg Cantin
- Department of Cell Biology, SR-11, Scripps Research institute, La Jolla, CA 92307, USA
| | - John R Yates
- Department of Cell Biology, SR-11, Scripps Research institute, La Jolla, CA 92307, USA
| | - Nevan J Krogan
- Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158-2517, USA California Institute for Quantitative Biosciences, QB3, San Francisco, CA 94158-2517, USA J. David Gladstone Institute, San Francisco, CA, 94158-2517, USA
| | - Wolf-Dietrich Heyer
- Department of Microbiology & Molecular Genetics, University of California, Davis, CA 95616-8665, USA Department of Molecular & Cellular Biology University of California, Davis, CA 95616-8665, USA
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42
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Charitou T, Bryan K, Lynn DJ. Using biological networks to integrate, visualize and analyze genomics data. Genet Sel Evol 2016; 48:27. [PMID: 27036106 PMCID: PMC4818439 DOI: 10.1186/s12711-016-0205-1] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 03/16/2016] [Indexed: 12/22/2022] Open
Abstract
Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene’s network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide ‘omics’ data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.
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Affiliation(s)
- Theodosia Charitou
- EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000, Australia.,Systems Biology Ireland, University College Dublin, Belfield 4, Ireland.,Teagasc, The Agriculture and Food Development Authority, Co Meath, Ireland
| | - Kenneth Bryan
- EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000, Australia
| | - David J Lynn
- EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000, Australia. .,School of Medicine, Flinders University, Bedford Park, SA, 5042, Australia.
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43
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Lee J, Lee D. Association analysis of the perturbation of interactions in biological pathways and anticancer drug activity. Biochem Biophys Res Commun 2016; 470:137-143. [PMID: 26772881 DOI: 10.1016/j.bbrc.2016.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Accepted: 01/03/2016] [Indexed: 11/25/2022]
Abstract
Understanding how different genomic mutational landscapes in patients with cancer lead to different responses to anticancer drugs is an important challenge for realizing precision medicine for cancer. Many studies have analyzed the comprehensive anticancer drug-response profiles and genomic profiles of cancer cell lines to identify the relationship between the anticancer drug response and genomic alternations. However, few studies have focused on interpreting these profiles with a network perspective. In this work, we analyzed genomic alterations in cancer cell lines by considering which interactions in the signaling pathway were perturbed by mutations. With our interaction-centric approach, we identified novel interaction/drug response associations for two drugs (afatinib and ixabepilone) for which no gene-centric association could be found. When we compared the performance of classifiers for predicting the responses to 164 drugs, the classifiers trained with interaction-centric features outperformed the classifiers trained with gene-centric features, despite the smaller number of features (p-value = 2.0 × 10(-3)). By incorporating the interaction information from signaling pathways, we revealed associations between genomic alterations and drug responses that could be missed when using a gene-centric approach.
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Affiliation(s)
- Junehawk Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Department of Convergence Technology Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Bio-Synergy Research Center, Daejeon, Republic of Korea.
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Rakesh R, Srinivasan N. Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins. Methods Mol Biol 2016; 1415:193-209. [PMID: 27115634 DOI: 10.1007/978-1-4939-3572-7_10] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Cryo-Electron Microscopy (cryo-EM) has become an important technique to obtain structural insights into large macromolecular assemblies. However the resolution of the density maps do not allow for its interpretation at atomic level. Hence they are combined with high resolution structures along with information from other experimental or bioinformatics techniques to obtain pseudo-atomic models. Here, we describe the use of evolutionary conservation of residues as obtained from protein structures and alignments of homologous proteins to detect errors in the fitting of atomic structures as well as improve accuracy of the protein-protein interfacial regions in the cryo-EM density maps.
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Affiliation(s)
- Ramachandran Rakesh
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
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45
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Gligorijević V, Malod-Dognin N, Pržulj N. Fuse: multiple network alignment via data fusion. Bioinformatics 2015; 32:1195-203. [DOI: 10.1093/bioinformatics/btv731] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 10/09/2015] [Indexed: 02/07/2023] Open
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46
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Faisal FE, Meng L, Crawford J, Milenković T. The post-genomic era of biological network alignment. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2015; 2015:3. [PMID: 28194172 PMCID: PMC5270500 DOI: 10.1186/s13637-015-0022-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 05/18/2015] [Indexed: 11/10/2022]
Abstract
Biological network alignment aims to find regions of topological and functional (dis)similarities between molecular networks of different species. Then, network alignment can guide the transfer of biological knowledge from well-studied model species to less well-studied species between conserved (aligned) network regions, thus complementing valuable insights that have already been provided by genomic sequence alignment. Here, we review computational challenges behind the network alignment problem, existing approaches for solving the problem, ways of evaluating their alignment quality, and the approaches' biomedical applications. We discuss recent innovative efforts of improving the existing view of network alignment. We conclude with open research questions in comparative biological network research that could further our understanding of principles of life, evolution, disease, and therapeutics.
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Affiliation(s)
- Fazle E Faisal
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556 USA
- Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN, 46556 USA
- ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556 USA
| | - Lei Meng
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556 USA
| | - Joseph Crawford
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556 USA
- Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN, 46556 USA
- ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556 USA
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556 USA
- Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, IN, 46556 USA
- ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556 USA
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47
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Abstract
Next-generation sequencing approaches have considerably advanced our understanding of genome function and regulation. However, the knowledge of gene function and complex cellular processes remains a challenge and bottleneck in biological research. Phenomics is a rapidly emerging area, which seeks to rigorously characterize all phenotypes associated with genes or gene variants. Such high-throughput phenotyping under different conditions can be a potent approach toward gene function. The fission yeast Schizosaccharomyces pombe (S. pombe) is a proven eukaryotic model organism that is increasingly used for genomewide screens and phenomic assays. In this review, we highlight current large-scale, cell-based approaches used with S. pombe, including computational colony-growth measurements, genetic interaction screens, parallel profiling using barcodes, microscopy-based cell profiling, metabolomic methods and transposon mutagenesis. These diverse methods are starting to offer rich insights into the relationship between genotypes and phenotypes.
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Affiliation(s)
- Charalampos Rallis
- a Research Department of Genetics , Evolution and Environment and UCL Institute of Healthy Ageing, University College London , London , UK
| | - Jürg Bähler
- a Research Department of Genetics , Evolution and Environment and UCL Institute of Healthy Ageing, University College London , London , UK
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48
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Porta-Pardo E, Garcia-Alonso L, Hrabe T, Dopazo J, Godzik A. A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces. PLoS Comput Biol 2015; 11:e1004518. [PMID: 26485003 PMCID: PMC4616621 DOI: 10.1371/journal.pcbi.1004518] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 08/21/2015] [Indexed: 12/19/2022] Open
Abstract
Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated. Until now, most efforts in cancer genomics have focused on identifying genes and pathways driving tumor development. Although this has been unquestionably a success, as evidenced by the fact that we now have an extensive catalogue of cancer driver genes and pathways, there is still a poor understanding of why patients with the same affected driver genes may have different disease outcomes or drug responses. This is precisely the aim of this work-to show how by considering proteins as multifunctional factories instead of monolithic black boxes, it is possible to identify novel cancer driver genes and propose molecular hypotheses to explain such heterogeneity. To that end we have mapped the mutation profiles of 5,989 cancer patients from TCGA to more than 10,000 protein structures, leading us to identify 103 protein interaction interfaces enriched in somatic mutations. Finally, we have integrated clinical annotations as well as proteomics data to show how tumors apparently driven by the same gene can display different behaviors, including patient outcomes, depending on which specific interfaces are mutated.
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Affiliation(s)
- Eduard Porta-Pardo
- Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Luz Garcia-Alonso
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, United Kingdom
| | - Thomas Hrabe
- Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Functional Genomics Node, (INB) at CIPF, Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
- * E-mail: (JD); (AG)
| | - Adam Godzik
- Bioinformatics and Systems Biology Program, Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
- * E-mail: (JD); (AG)
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49
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Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc Natl Acad Sci U S A 2015; 112:E5486-95. [PMID: 26392535 DOI: 10.1073/pnas.1516373112] [Citation(s) in RCA: 155] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg2+, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.
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50
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Cheng S, Fu HL, Cui DX. Characteristics Analyses and Comparisons of the Protein Structure Networks Constructed by Different Methods. Interdiscip Sci 2015; 8:65-74. [PMID: 26297308 DOI: 10.1007/s12539-015-0106-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Revised: 04/21/2014] [Accepted: 05/21/2014] [Indexed: 10/23/2022]
Abstract
Protein structure networks (PSNs) were widely used in analyses of protein structure and function. In this work, we analyzed and compared the characters of PSNs by different methods. The degrees of the different types of the nodes were found to be associated with the amino acid characters, including SAS, secondary structure, hydropathy and the volume of amino acids. It showed that PSNs by the methods of CA10, SC10 and AT5 inherited more amino acid characters and had higher correlations with the original protein structures. And PSNs by these three methods would be powerful tools in understanding the characters of protein structures.
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Affiliation(s)
- Shangli Cheng
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Chinese National Center for Translational Medicine, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China
| | - Hua-Lin Fu
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Chinese National Center for Translational Medicine, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China
| | - Da-Xiang Cui
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Chinese National Center for Translational Medicine, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China.
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