1
|
Brock K, Alpha KM, Brennan G, De Jong EP, Luke E, Turner CE. A comparative analysis of paxillin and Hic-5 proximity interactomes. Cytoskeleton (Hoboken) 2024. [PMID: 38801098 DOI: 10.1002/cm.21878] [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] [Received: 02/14/2024] [Revised: 04/18/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Focal adhesions serve as structural and signaling hubs, facilitating bidirectional communication at the cell-extracellular matrix interface. Paxillin and the related Hic-5 (TGFβ1i1) are adaptor/scaffold proteins that recruit numerous structural and regulatory proteins to focal adhesions, where they perform both overlapping and discrete functions. In this study, paxillin and Hic-5 were expressed in U2OS osteosarcoma cells as biotin ligase (BioID2) fusion proteins and used as bait proteins for proximity-dependent biotinylation in order to directly compare their respective interactomes. The fusion proteins localized to both focal adhesions and the centrosome, resulting in biotinylation of components of each of these structures. Biotinylated proteins were purified and analyzed by mass spectrometry. The list of proximity interactors for paxillin and Hic-5 comprised numerous shared core focal adhesion proteins that likely contribute to their similar functions in cell adhesion and migration, as well as proteins unique to paxillin and Hic-5 that have been previously localized to focal adhesions, the centrosome, or the nucleus. Western blotting confirmed biotinylation and enrichment of FAK and vinculin, known interactors of Hic-5 and paxillin, as well as several potentially unique proximity interactors of Hic-5 and paxillin, including septin 7 and ponsin, respectively. Further investigation into the functional relationship between the unique interactors and Hic-5 or paxillin may yield novel insights into their distinct roles in cell migration.
Collapse
Affiliation(s)
- Katia Brock
- Department of Cell and Developmental Biology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Kyle M Alpha
- Department of Cell and Developmental Biology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Grant Brennan
- Department of Cell and Developmental Biology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Ebbing P De Jong
- Proteomics Core Facility, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Elizabeth Luke
- Department of Cell and Developmental Biology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - Christopher E Turner
- Department of Cell and Developmental Biology, State University of New York Upstate Medical University, Syracuse, New York, USA
| |
Collapse
|
2
|
Idrees S, Paudel KR, Sadaf T, Hansbro PM. Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods. FEBS Lett 2024; 598:725-742. [PMID: 38439692 DOI: 10.1002/1873-3468.14841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/09/2024] [Accepted: 02/18/2024] [Indexed: 03/06/2024]
Abstract
Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.
Collapse
Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Philip M Hansbro
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| |
Collapse
|
3
|
Xiong D, Qiu Y, Zhao J, Zhou Y, Lee D, Gupta S, Torres M, Lu W, Liang S, Kang JJ, Eng C, Loscalzo J, Cheng F, Yu H. Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.24.538110. [PMID: 37162909 PMCID: PMC10168245 DOI: 10.1101/2023.04.24.538110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.
Collapse
Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY 10032, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dongjin Lee
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Shobhita Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
- Biophysics Program, Cornell University, Ithaca, NY 14853, USA
| | - Mateo Torres
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Jin Joo Kang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| |
Collapse
|
4
|
Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
Collapse
Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | | |
Collapse
|
5
|
Wang Y, Zhou B, Ru J, Meng X, Wang Y, Liu W. Advances in computational methods for identifying cancer driver genes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21643-21669. [PMID: 38124614 DOI: 10.3934/mbe.2023958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.
Collapse
Affiliation(s)
- Ying Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Bohao Zhou
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Jidong Ru
- School of Textile Garment and Design, Changshu Institute of Technology, Changshu 215500, China
| | - Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| | - Yundong Wang
- School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 213032, China
| |
Collapse
|
6
|
Szymborski J, Emad A. RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks. Bioinformatics 2022; 38:3958-3967. [PMID: 35771595 DOI: 10.1093/bioinformatics/btac429] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 04/30/2022] [Accepted: 06/27/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Computational methods for the prediction of protein-protein interactions (PPIs), while important tools for researchers, are plagued by challenges in generalizing to unseen proteins. Datasets used for modelling protein-protein predictions are particularly predisposed to information leakage and sampling biases. RESULTS In this study, we introduce RAPPPID, a method for the Regularized Automatic Prediction of Protein-Protein Interactions using Deep Learning. RAPPPID is a twin Averaged Weight-Dropped Long Short-Term memory network which employs multiple regularization methods during training time to learn generalized weights. Testing on stringent interaction datasets composed of proteins not seen during training, RAPPPID outperforms state-of-the-art methods. Further experiments show that RAPPPID's performance holds regardless of the particular proteins in the testing set and its performance is higher for experimentally supported edges. This study serves to demonstrate that appropriate regularization is an important component of overcoming the challenges of creating models for PPI prediction that generalize to unseen proteins. Additionally, as part of this study, we provide datasets corresponding to several data splits of various strictness, in order to facilitate assessment of PPI reconstruction methods by others in the future. AVAILABILITY AND IMPLEMENTATION Code and datasets are freely available at https://github.com/jszym/rapppid and Zenodo.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Joseph Szymborski
- Department of Electrical and Computer Engineering, McGill University, Montréal, QC H3A 0G4, Canada.,Mila, Québec AI Institute, Montréal, QC H2S 3H1, Canada
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montréal, QC H3A 0G4, Canada.,Mila, Québec AI Institute, Montréal, QC H2S 3H1, Canada.,The Rosalind and Morris Goodman Cancer Institute, Montréal, QC H3A 1A3, Canada
| |
Collapse
|
7
|
Chen S, Liu Y, Zhang Y, Wierbowski SD, Lipkin SM, Wei X, Yu H. A full-proteome, interaction-specific characterization of mutational hotspots across human cancers. Genome Res 2022; 32:135-149. [PMID: 34963661 PMCID: PMC8744679 DOI: 10.1101/gr.275437.121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 11/22/2021] [Indexed: 11/24/2022]
Abstract
Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.
Collapse
Affiliation(s)
- Siwei Chen
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Yuan Liu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Yingying Zhang
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Shayne D Wierbowski
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Steven M Lipkin
- Department of Medicine, Weill Cornell Medicine, New York, New York 10021, USA
| | - Xiaomu Wei
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York 10021, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| |
Collapse
|
8
|
Kotlyar M, Pastrello C, Ahmed Z, Chee J, Varyova Z, Jurisica I. IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis. Nucleic Acids Res 2021; 50:D640-D647. [PMID: 34755877 PMCID: PMC8728267 DOI: 10.1093/nar/gkab1034] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/13/2021] [Accepted: 11/03/2021] [Indexed: 01/02/2023] Open
Abstract
Improved bioassays have significantly increased the rate of identifying new protein-protein interactions (PPIs), and the number of detected human PPIs has greatly exceeded early estimates of human interactome size. These new PPIs provide a more complete view of disease mechanisms but precise understanding of how PPIs affect phenotype remains a challenge. It requires knowledge of PPI context (e.g. tissues, subcellular localizations), and functional roles, especially within pathways and protein complexes. The previous IID release focused on PPI context, providing networks with comprehensive tissue, disease, cellular localization, and druggability annotations. The current update adds developmental stages to the available contexts, and provides a way of assigning context to PPIs that could not be previously annotated due to insufficient data or incompatibility with available context categories (e.g. interactions between membrane and cytoplasmic proteins). This update also annotates PPIs with conservation across species, directionality in pathways, membership in large complexes, interaction stability (i.e. stable or transient), and mutation effects. Enrichment analysis is now available for all annotations, and includes multiple options; for example, context annotations can be analyzed with respect to PPIs or network proteins. In addition to tabular view or download, IID provides online network visualization. This update is available at http://ophid.utoronto.ca/iid.
Collapse
Affiliation(s)
- Max Kotlyar
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Zuhaib Ahmed
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Justin Chee
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Zofia Varyova
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada.,Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON M5S 1A4, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| |
Collapse
|
9
|
Arici MK, Tuncbag N. Performance Assessment of the Network Reconstruction Approaches on Various Interactomes. Front Mol Biosci 2021; 8:666705. [PMID: 34676243 PMCID: PMC8523993 DOI: 10.3389/fmolb.2021.666705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.
Collapse
Affiliation(s)
- M Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.,School of Medicine, Koc University, Istanbul, Turkey
| |
Collapse
|
10
|
Mukherjee S, Cogan JD, Newman JH, Phillips JA, Hamid R, Meiler J, Capra JA. Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network. Am J Hum Genet 2021; 108:1946-1963. [PMID: 34529933 PMCID: PMC8546038 DOI: 10.1016/j.ajhg.2021.08.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 08/25/2021] [Indexed: 12/20/2022] Open
Abstract
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.
Collapse
Affiliation(s)
- Souhrid Mukherjee
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Joy D Cogan
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - John H Newman
- Pulmonary Hypertension Center, Division of Allergy, Pulmonary, and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John A Phillips
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Rizwan Hamid
- Department of Pediatrics, Division of Medical Genetics and Genomic Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Institute for Drug Discovery, Leipzig University Medical School, Leipzig 04103, Germany; Department of Chemistry, Leipzig University, Leipzig 04109, Germany; Department of Computer Science, Leipzig University, Leipzig 04109, Germany.
| | - John A Capra
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA.
| |
Collapse
|
11
|
Alanis-Lobato G, Möllmann JS, Schaefer MH, Andrade-Navarro MA. MIPPIE: the mouse integrated protein-protein interaction reference. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2020:5850252. [PMID: 32496562 PMCID: PMC7271249 DOI: 10.1093/database/baaa035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 04/02/2020] [Accepted: 04/29/2020] [Indexed: 12/13/2022]
Abstract
Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.
Collapse
Affiliation(s)
- Gregorio Alanis-Lobato
- Faculty of Biology, Johannes Gutenberg University, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany.,Human Embryo and Stem Cell Laboratory, The Francis Crick Institute, 1 Midland Road, NW1 1AT London, UK
| | - Jannik S Möllmann
- Faculty of Biology, Johannes Gutenberg University, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| | - Martin H Schaefer
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Via Adamello 16, 20139 Milan, Italy
| | - Miguel A Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg University, Biozentrum I, Hans-Dieter-Hüsch-Weg 15, 55128 Mainz, Germany
| |
Collapse
|
12
|
Azadian S, Zahiri J, Shahriar Arab S, Hassan Sajedi R. Reconstruction of Intercellular Signaling Network by Cytokine-Receptor Interactions. IRANIAN JOURNAL OF BIOTECHNOLOGY 2021; 19:e2560. [PMID: 34179188 PMCID: PMC8217541 DOI: 10.30498/ijb.2021.2560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background: The immune system function depends on the coordination activity of the components of system and communications between them
which leads to the formation of a complex communication network between immune cells.
In this network, cytokines have an important role in the communication between immune cells through the interaction to their specific receptors.
These molecules cause to cellular communications and normal function of a tissue. Reconstruction of such a complex network
can be a way to provide a better understanding of cytokines’ function. Objective: Our main goal from reconstructing such a network was investigation of expressed cytokines and cytokines receptors in various
lineage and tissues of immune cells and identifying the lineage and tissue with the highest expression of cytokines and their receptors. Materials and Methods: In this study, gene expression data related to part of the Immunological Genome Project (ImmGen) and receptor-ligand interactions
dataset were used to reconstruct the immune network in mouse. In next step, the topological properties of reconstructed network,
expression specificity of cytokines and their receptors and interactions specificity were analyzed. Results: The results of the network analysis were indicated that non- hematopoietic stromal cells have the highest expression of cytokines and cytokine receptors and interactions specificity is very high. Our results show that chemokine receptor of Ccr1 receives the largest number of signals between receptors and only expressed in three hematopoietic lineages. Conclusions: The most of the network communications belonged to non-hematopoietic stromal and macrophage cells. The relationships between stromal
cells and macrophages are necessary to create an appropriate environment for differentiation of immune cells.
Studying the cellular expression specificity of receptor and ligand genes reveal the high degree of specificity of these genes that
indicate non-random transfer of information between cells in multicellular organisms.
Collapse
Affiliation(s)
- Somayeh Azadian
- Department of Biophysics, Bioinformatics and Computational Omics Lab (BioCOOL), Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Javad Zahiri
- Department of Biophysics, Bioinformatics and Computational Omics Lab (BioCOOL), Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Seyed Shahriar Arab
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Reza Hassan Sajedi
- Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
13
|
Rohani N, Eslahchi C. Classifying Breast Cancer Molecular Subtypes by Using Deep Clustering Approach. Front Genet 2020; 11:553587. [PMID: 33324444 PMCID: PMC7723873 DOI: 10.3389/fgene.2020.553587] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 08/25/2020] [Indexed: 01/07/2023] Open
Abstract
Cancer is a complex disease with a high rate of mortality. The characteristics of tumor masses are very heterogeneous; thus, the appropriate classification of tumors is a critical point in the effective treatment. A high level of heterogeneity has also been observed in breast cancer. Therefore, detecting the molecular subtypes of this disease is an essential issue for medicine that could be facilitated using bioinformatics. This study aims to discover the molecular subtypes of breast cancer using somatic mutation profiles of tumors. Nonetheless, the somatic mutation profiles are very sparse. Therefore, a network propagation method is used in the gene interaction network to make the mutation profiles dense. Afterward, the deep embedded clustering (DEC) method is used to classify the breast tumors into four subtypes. In the next step, gene signature of each subtype is obtained using Fisher's exact test. Besides the enrichment of gene signatures in numerous biological databases, clinical and molecular analyses verify that the proposed method using mutation profiles can efficiently detect the molecular subtypes of breast cancer. Finally, a supervised classifier is trained based on the discovered subtypes to predict the molecular subtype of a new patient. The code and material of the method are available at: https://github.com/nrohani/MolecularSubtypes.
Collapse
Affiliation(s)
- Narjes Rohani
- Department of Computer and Data Sciences, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| |
Collapse
|
14
|
Mei S, Huang X, Xie C, Mora A. GREG-studying transcriptional regulation using integrative graph databases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5735477. [PMID: 32055858 PMCID: PMC7018612 DOI: 10.1093/database/baz162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/08/2019] [Accepted: 12/31/2019] [Indexed: 01/08/2023]
Abstract
A gene regulatory process is the result of the concerted action of transcription factors, co-factors, regulatory non-coding RNAs (ncRNAs) and chromatin interactions. Therefore, the combination of protein-DNA, protein-protein, ncRNA-DNA, ncRNA-protein and DNA-DNA data in a single graph database offers new possibilities regarding generation of biological hypotheses. GREG (The Gene Regulation Graph Database) is an integrative database and web resource that allows the user to visualize and explore the network of all above-mentioned interactions for a query transcription factor, long non-coding RNA, genomic range or DNA annotation, as well as extracting node and interaction information, identifying connected nodes and performing advanced graphical queries directly on the regulatory network, in a simple and efficient way. In this article, we introduce GREG together with some application examples (including exploratory research of Nanog's regulatory landscape and the etiology of chronic obstructive pulmonary disease), which we use as a demonstration of the advantages of using graph databases in biomedical research. Database URL: https://mora-lab.github.io/projects/greg.html, www.moralab.science/GREG/.
Collapse
Affiliation(s)
- Songqing Mei
- School of Basic Medical Sciences, Guangzhou Medical University, Panyu Campus of Guangzhou Medical University, Xinzao, 511436 Guangzhou, P.R. China
| | - Xiaowei Huang
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Panyu Campus of Guangzhou Medical University, Xinzao, 511436 Guangzhou, P.R. China
| | - Chengshu Xie
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Panyu Campus of Guangzhou Medical University, Xinzao, 511436 Guangzhou, P.R. China
| | - Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Panyu Campus of Guangzhou Medical University, Xinzao, 511436 Guangzhou, P.R. China
| |
Collapse
|
15
|
Abstract
iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .
Collapse
|
16
|
Chen S, Wang J, Cicek E, Roeder K, Yu H, Devlin B. De novo missense variants disrupting protein-protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types. Mol Autism 2020; 11:76. [PMID: 33032641 PMCID: PMC7545940 DOI: 10.1186/s13229-020-00386-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/01/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Whole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses. METHODS Utilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein-protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an "ASD disrupted network." Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner. RESULTS Extending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types-excitatory, inhibitory, and neural progenitor-we implicate several hundred genes in risk (FDR [Formula: see text]0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system. LIMITATIONS Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional. CONCLUSIONS Disrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD.
Collapse
Affiliation(s)
- Siwei Chen
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14853, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, 15213, USA
| | - Ercument Cicek
- Department of Computer Engineering, Bilkent University, 06800, Ankara, Turkey
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Kathryn Roeder
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA.
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA.
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA.
| |
Collapse
|
17
|
Yugandhar K, Wang TY, Wierbowski SD, Shayhidin EE, Yu H. Structure-based validation can drastically underestimate error rate in proteome-wide cross-linking mass spectrometry studies. Nat Methods 2020; 17:985-988. [PMID: 32994567 PMCID: PMC7534832 DOI: 10.1038/s41592-020-0959-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 08/20/2020] [Indexed: 12/18/2022]
Abstract
Thorough quality assessment of novel interactions identified by proteome-wide cross-linking mass spectrometry (XL-MS) studies is critical. Almost all current XL-MS studies have validated cross-links against known 3D structures of representative protein complexes. Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets, and propose a comprehensive set of four data-quality metrics to address this issue. The current standard approach for estimating error in proteome-scale crosslinking-mass spectrometry datasets has severe limitations. A proposed set of data-quality metrics provides a more accurate assessment of error rate.
Collapse
Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Ting-Yi Wang
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Shayne D Wierbowski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Elnur Elyar Shayhidin
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY, USA. .,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
| |
Collapse
|
18
|
Han Y, Cheng L, Sun W. Analysis of Protein-Protein Interaction Networks through Computational Approaches. Protein Pept Lett 2020; 27:265-278. [PMID: 31692419 DOI: 10.2174/0929866526666191105142034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 05/08/2019] [Accepted: 09/26/2019] [Indexed: 01/02/2023]
Abstract
The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.
Collapse
Affiliation(s)
- Ying Han
- Cardiovascular Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weiju Sun
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| |
Collapse
|
19
|
Milewska A, Ner‐Kluza J, Dabrowska A, Bodzon‐Kulakowska A, Pyrc K, Suder P. MASS SPECTROMETRY IN VIROLOGICAL SCIENCES. MASS SPECTROMETRY REVIEWS 2020; 39:499-522. [PMID: 31876329 PMCID: PMC7228374 DOI: 10.1002/mas.21617] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 12/15/2019] [Indexed: 05/24/2023]
Abstract
Virology, as a branch of the life sciences, discovered mass spectrometry (MS) to be the pivotal tool around two decades ago. The technique unveiled the complex network of interactions between the living world of pro- and eukaryotes and viruses, which delivered "a piece of bad news wrapped in protein" as defined by Peter Medawar, Nobel Prize Laureate, in 1960. However, MS is constantly evolving, and novel approaches allow for a better understanding of interactions in this micro- and nanoworld. Currently, we can investigate the interplay between the virus and the cell by analyzing proteomes, interactomes, virus-cell interactions, and search for the compounds that build viral structures. In addition, by using MS, it is possible to look at the cell from the broader perspective and determine the role of viral infection on the scale of the organism, for example, monitoring the crosstalk between infected tissues and the immune system. In such a way, MS became one of the major tools for the modern virology, allowing us to see the infection in the context of the whole cell or the organism. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.
Collapse
Affiliation(s)
- Aleksandra Milewska
- Malopolska Centre of BiotechnologyJagiellonian UniversityGronostajowa 7A30‐387KrakowPoland
| | - Joanna Ner‐Kluza
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and CeramicsAGH University of Science and TechnologyMickiewicza 30 Ave.30‐059KrakowPoland
| | - Agnieszka Dabrowska
- Malopolska Centre of BiotechnologyJagiellonian UniversityGronostajowa 7A30‐387KrakowPoland
- Faculty of Biochemistry, Biophysics and BiotechnologyJagiellonian UniversityGronostajowa 730‐387KrakowPoland
| | - Anna Bodzon‐Kulakowska
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and CeramicsAGH University of Science and TechnologyMickiewicza 30 Ave.30‐059KrakowPoland
| | - Krzysztof Pyrc
- Malopolska Centre of BiotechnologyJagiellonian UniversityGronostajowa 7A30‐387KrakowPoland
| | - Piotr Suder
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and CeramicsAGH University of Science and TechnologyMickiewicza 30 Ave.30‐059KrakowPoland
| |
Collapse
|
20
|
A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice. Proc Natl Acad Sci U S A 2020; 117:11836-11842. [PMID: 32398372 DOI: 10.1073/pnas.1918068117] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.
Collapse
|
21
|
Bajpai AK, Davuluri S, Tiwary K, Narayanan S, Oguru S, Basavaraju K, Dayalan D, Thirumurugan K, Acharya KK. Systematic comparison of the protein-protein interaction databases from a user's perspective. J Biomed Inform 2020; 103:103380. [DOI: 10.1016/j.jbi.2020.103380] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 11/08/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023]
|
22
|
Yugandhar K, Wang TY, Leung AKY, Lanz MC, Motorykin I, Liang J, Shayhidin EE, Smolka MB, Zhang S, Yu H. MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity. Mol Cell Proteomics 2020; 19:554-568. [PMID: 31839598 PMCID: PMC7050104 DOI: 10.1074/mcp.tir119.001847] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Indexed: 11/06/2022] Open
Abstract
Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.
Collapse
Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Ting-Yi Wang
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Alden King-Yung Leung
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Michael Charles Lanz
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
| | - Ievgen Motorykin
- Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853
| | - Jin Liang
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Elnur Elyar Shayhidin
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Marcus Bustamante Smolka
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
| | - Sheng Zhang
- Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853.
| |
Collapse
|
23
|
Barreto CAV, Baptista SJ, Preto AJ, Matos-Filipe P, Mourão J, Melo R, Moreira I. Prediction and targeting of GPCR oligomer interfaces. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 169:105-149. [PMID: 31952684 DOI: 10.1016/bs.pmbts.2019.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.
Collapse
Affiliation(s)
- Carlos A V Barreto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Salete J Baptista
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - António José Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Rita Melo
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - Irina Moreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Science and Technology Faculty, University of Coimbra, Coimbra, Portugal.
| |
Collapse
|
24
|
Alanis-Lobato G, Schaefer MH. Generation and Interpretation of Context-Specific Human Protein-Protein Interaction Networks with HIPPIE. Methods Mol Biol 2020; 2074:135-144. [PMID: 31583636 DOI: 10.1007/978-1-4939-9873-9_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
High-throughput techniques for the detection of protein-protein interactions (PPIs) have enabled a systems approach for the study of the living cell. However, the increasing amount of protein interaction data, the varying quality of these measurements, and the lack of context information make it difficult to construct meaningful and reliable protein networks.The Human Integrated Protein-Protein Interaction rEference (HIPPIE) is a web tool that integrates and annotates experimentally supported human PPIs from a heterogeneous set of data sources. In HIPPIE, one can query for the interactors of one or more proteins and generate high-quality and context-specific networks. This chapter highlights HIPPIE's most important features and exemplifies its functionality through a proposed use case.
Collapse
Affiliation(s)
| | - Martin H Schaefer
- Department of Experimental Oncology, European Institute of Oncology, Milan, Italy.
| |
Collapse
|
25
|
Balashanmugam MV, Shivanandappa TB, Nagarethinam S, Vastrad B, Vastrad C. Analysis of Differentially Expressed Genes in Coronary Artery Disease by Integrated Microarray Analysis. Biomolecules 2019; 10:biom10010035. [PMID: 31881747 PMCID: PMC7022900 DOI: 10.3390/biom10010035] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 12/13/2019] [Accepted: 12/20/2019] [Indexed: 12/31/2022] Open
Abstract
Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the superpathway of steroid hormone biosynthesis, ABC transporters, oxidative ethanol degradation III and Complement and coagulation cascades. Similarly, geneontology enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the forebrain neuron differentiation, filopodium membrane, platelet degranulation and blood microparticle. In the PPI network and modules (up and down regulated), MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74 and VHL were hub genes. In the target genes-miRNA regulatory network and target genes—TF regulatory network (up and down regulated), TAOK1, KHSRP, HSD17B11 and PAH were target genes. In conclusion, the pathway and GO ontology enriched by DEGs may reveal the molecular mechanism of CAD. Its hub and target genes, MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74, VHL, TAOK1, KHSRP, HSD17B11 and PAH were expected to be new targets for CAD. Our finding provided clues for exploring molecular mechanism and developing new prognostics, diagnostic and therapeutic strategies for CAD.
Collapse
Affiliation(s)
- Meenashi Vanathi Balashanmugam
- Department of Biomedical Sciences, College of Pharmacy, Shaqra University, Al Dawadmi 11911, Saudi Arabia; (M.V.B.); (T.B.S.); (S.N.)
| | - Thippeswamy Boreddy Shivanandappa
- Department of Biomedical Sciences, College of Pharmacy, Shaqra University, Al Dawadmi 11911, Saudi Arabia; (M.V.B.); (T.B.S.); (S.N.)
| | - Sivagurunathan Nagarethinam
- Department of Biomedical Sciences, College of Pharmacy, Shaqra University, Al Dawadmi 11911, Saudi Arabia; (M.V.B.); (T.B.S.); (S.N.)
| | - Basavaraj Vastrad
- Department of Pharmaceutics, SET’S College of Pharmacy, Dharwad, Karnataka 580002, India;
| | - Chanabasayya Vastrad
- Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka
- Correspondence: ; Tel.: +91-9480-073398
| |
Collapse
|
26
|
Wreczycka K, Franke V, Uyar B, Wurmus R, Bulut S, Tursun B, Akalin A. HOT or not: examining the basis of high-occupancy target regions. Nucleic Acids Res 2019; 47:5735-5745. [PMID: 31114922 PMCID: PMC6582337 DOI: 10.1093/nar/gkz460] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 05/02/2019] [Accepted: 05/13/2019] [Indexed: 01/16/2023] Open
Abstract
High-occupancy target (HOT) regions are segments of the genome with unusually high number of transcription factor binding sites. These regions are observed in multiple species and thought to have biological importance due to high transcription factor occupancy. Furthermore, they coincide with house-keeping gene promoters and consequently associated genes are stably expressed across multiple cell types. Despite these features, HOT regions are solely defined using ChIP-seq experiments and shown to lack canonical motifs for transcription factors that are thought to be bound there. Although, ChIP-seq experiments are the golden standard for finding genome-wide binding sites of a protein, they are not noise free. Here, we show that HOT regions are likely to be ChIP-seq artifacts and they are similar to previously proposed ‘hyper-ChIPable’ regions. Using ChIP-seq data sets for knocked-out transcription factors, we demonstrate presence of false positive signals on HOT regions. We observe sequence characteristics and genomic features that are discriminatory of HOT regions, such as GC/CpG-rich k-mers, enrichment of RNA–DNA hybrids (R-loops) and DNA tertiary structures (G-quadruplex DNA). The artificial ChIP-seq enrichment on HOT regions could be associated to these discriminatory features. Furthermore, we propose strategies to deal with such artifacts for the future ChIP-seq studies.
Collapse
Affiliation(s)
- Katarzyna Wreczycka
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Vedran Franke
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Bora Uyar
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Ricardo Wurmus
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Selman Bulut
- Gene Regulation and Cell Fate Decision in C. elegans, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Baris Tursun
- Gene Regulation and Cell Fate Decision in C. elegans, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Berlin, Germany
| |
Collapse
|
27
|
Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
Collapse
Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| |
Collapse
|
28
|
Dincer C, Kaya T, Keskin O, Gursoy A, Tuncbag N. 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients. PLoS Comput Biol 2019; 15:e1006789. [PMID: 31527881 PMCID: PMC6782092 DOI: 10.1371/journal.pcbi.1006789] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 10/08/2019] [Accepted: 07/31/2019] [Indexed: 02/06/2023] Open
Abstract
Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.
Collapse
Affiliation(s)
- Cansu Dincer
- Department of Health Informatics, Graduate School of Informatics, METU, Ankara, Turkey
| | - Tugba Kaya
- Department of Health Informatics, Graduate School of Informatics, METU, Ankara, Turkey
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
- Research Center for Translational Medicine (KUTTAM), Koc University, Istanbul, Turkey
| | - Attila Gursoy
- Research Center for Translational Medicine (KUTTAM), Koc University, Istanbul, Turkey
- Department of Computer Engineering, Koc University, Istanbul, Turkey
| | - Nurcan Tuncbag
- Department of Health Informatics, Graduate School of Informatics, METU, Ankara, Turkey
- Cancer Systems Biology Laboratory (CanSyL-METU), Ankara, Turkey
- * E-mail:
| |
Collapse
|
29
|
Uretmen Kagiali ZC, Sanal E, Karayel Ö, Polat AN, Saatci Ö, Ersan PG, Trappe K, Renard BY, Önder TT, Tuncbag N, Şahin Ö, Ozlu N. Systems-level Analysis Reveals Multiple Modulators of Epithelial-mesenchymal Transition and Identifies DNAJB4 and CD81 as Novel Metastasis Inducers in Breast Cancer. Mol Cell Proteomics 2019; 18:1756-1771. [PMID: 31221721 PMCID: PMC6731077 DOI: 10.1074/mcp.ra119.001446] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 05/21/2019] [Indexed: 01/01/2023] Open
Abstract
Epithelial-mesenchymal transition (EMT) is driven by complex signaling events that induce dramatic biochemical and morphological changes whereby epithelial cells are converted into cancer cells. However, the underlying molecular mechanisms remain elusive. Here, we used mass spectrometry based quantitative proteomics approach to systematically analyze the post-translational biochemical changes that drive differentiation of human mammary epithelial (HMLE) cells into mesenchymal. We identified 314 proteins out of more than 6,000 unique proteins and 871 phosphopeptides out of more than 7,000 unique phosphopeptides as differentially regulated. We found that phosphoproteome is more unstable and prone to changes during EMT compared with the proteome and multiple alterations at proteome level are not thoroughly represented by transcriptional data highlighting the necessity of proteome level analysis. We discovered cell state specific signaling pathways, such as Hippo, sphingolipid signaling, and unfolded protein response (UPR) by modeling the networks of regulated proteins and potential kinase-substrate groups. We identified two novel factors for EMT whose expression increased on EMT induction: DnaJ heat shock protein family (Hsp40) member B4 (DNAJB4) and cluster of differentiation 81 (CD81). Suppression of DNAJB4 or CD81 in mesenchymal breast cancer cells resulted in decreased cell migration in vitro and led to reduced primary tumor growth, extravasation, and lung metastasis in vivo Overall, we performed the global proteomic and phosphoproteomic analyses of EMT, identified and validated new mRNA and/or protein level modulators of EMT. This work also provides a unique platform and resource for future studies focusing on metastasis and drug resistance.
Collapse
Affiliation(s)
| | - Erdem Sanal
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Özge Karayel
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Ayse Nur Polat
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Özge Saatci
- §Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC 29208
| | - Pelin Gülizar Ersan
- ¶Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06800 Ankara, Turkey
| | - Kathrin Trappe
- ‖Bioinformatics Unit (MF1), Robert Koch Institute, 13353 Berlin, Germany
| | - Bernhard Y Renard
- ‖Bioinformatics Unit (MF1), Robert Koch Institute, 13353 Berlin, Germany
| | - Tamer T Önder
- **Koç University Research Center for Translational Medicine (KUTTAM), 34450 Istanbul, Turkey; ‡‡School of Medicine, Koç University, 34450 Istanbul, Turkey
| | - Nurcan Tuncbag
- §§Graduate School of Informatics, Department of Health Informatics, METU, 06800 Ankara, Turkey; ¶¶Cancer Systems Biology Laboratory (CanSyL), METU, 06800 Ankara, Turkey
| | - Özgür Şahin
- §Department of Drug Discovery and Biomedical Sciences, University of South Carolina, Columbia, SC 29208; ¶Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06800 Ankara, Turkey
| | - Nurhan Ozlu
- ‡Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey; **Koç University Research Center for Translational Medicine (KUTTAM), 34450 Istanbul, Turkey.
| |
Collapse
|
30
|
Link clustering explains non-central and contextually essential genes in protein interaction networks. Sci Rep 2019; 9:11672. [PMID: 31406201 PMCID: PMC6690968 DOI: 10.1038/s41598-019-48273-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 08/01/2019] [Indexed: 01/29/2023] Open
Abstract
Recent studies have shown that many essential genes (EGs) change their essentiality across various contexts. Finding contextual EGs in pathogenic conditions may facilitate the identification of therapeutic targets. We propose link clustering as an indicator of contextual EGs that are non-central in protein-protein interaction (PPI) networks. In various human and yeast PPI networks, we found that 29–47% of EGs were better characterized by link clustering than by centrality. Importantly, non-central EGs were prone to change their essentiality across different human cell lines and between species. Compared with central EGs and non-EGs, non-central EGs had intermediate levels of expression and evolutionary conservation. In addition, non-central EGs exhibited a significant impact on communities at lower hierarchical levels, suggesting that link clustering is associated with contextual essentiality, as it depicts locally important nodes in network structures.
Collapse
|
31
|
Hu Y, Vinayagam A, Nand A, Comjean A, Chung V, Hao T, Mohr SE, Perrimon N. Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data. Nucleic Acids Res 2019; 46:D567-D574. [PMID: 29155944 PMCID: PMC5753374 DOI: 10.1093/nar/gkx1116] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 10/25/2017] [Indexed: 12/16/2022] Open
Abstract
Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.
Collapse
Affiliation(s)
- Yanhui Hu
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.,Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Arunachalam Vinayagam
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Ankita Nand
- Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Aram Comjean
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.,Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Verena Chung
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.,Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Tong Hao
- Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Stephanie E Mohr
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.,Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.,Howard Hughes Medical Institute, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| |
Collapse
|
32
|
Hu LZ, Goebels F, Tan JH, Wolf E, Kuzmanov U, Wan C, Phanse S, Xu C, Schertzberg M, Fraser AG, Bader GD, Emili A. EPIC: software toolkit for elution profile-based inference of protein complexes. Nat Methods 2019; 16:737-742. [PMID: 31308550 PMCID: PMC7995176 DOI: 10.1038/s41592-019-0461-4] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/15/2019] [Indexed: 11/08/2022]
Abstract
Protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We and others previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (chromatographic fractionation-mass spectrometry, CF-MS), but the resulting data are challenging to process and interpret. Here, we describe EPIC (elution profile-based inference of complexes), a software toolkit for automated scoring of large-scale CF-MS data to define high-confidence multi-component macromolecules from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 612 putative worm protein complexes linked to diverse biological processes. These included novel subunits and assemblies unique to nematodes that we validated using orthogonal methods. The open source EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/).
Collapse
Affiliation(s)
- Lucas ZhongMing Hu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Florian Goebels
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - June H Tan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Eric Wolf
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Uros Kuzmanov
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Cuihong Wan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- School of Life Science, Central China Normal University, Wuhan, China
| | - Sadhna Phanse
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Changjiang Xu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Mike Schertzberg
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Andrew G Fraser
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Gary D Bader
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Andrew Emili
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- Departments of Biochemistry and Biology, Boston University, Boston, MA, USA.
| |
Collapse
|
33
|
Kondelin J, Salokas K, Saarinen L, Ovaska K, Rauanheimo H, Plaketti RM, Hamberg J, Liu X, Yadav L, Gylfe AE, Cajuso T, Hänninen UA, Palin K, Ristolainen H, Katainen R, Kaasinen E, Tanskanen T, Aavikko M, Taipale M, Taipale J, Renkonen-Sinisalo L, Lepistö A, Koskensalo S, Böhm J, Mecklin JP, Ongen H, Dermitzakis ET, Kilpivaara O, Vahteristo P, Turunen M, Hautaniemi S, Tuupanen S, Karhu A, Välimäki N, Varjosalo M, Pitkänen E, Aaltonen LA. Comprehensive evaluation of coding region point mutations in microsatellite-unstable colorectal cancer. EMBO Mol Med 2019; 10:emmm.201708552. [PMID: 30108113 PMCID: PMC6402450 DOI: 10.15252/emmm.201708552] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Microsatellite instability (MSI) leads to accumulation of an excessive number of mutations in the genome, mostly small insertions and deletions. MSI colorectal cancers (CRCs), however, also contain more point mutations than microsatellite‐stable (MSS) tumors, yet they have not been as comprehensively studied. To identify candidate driver genes affected by point mutations in MSI CRC, we ranked genes based on mutation significance while correcting for replication timing and gene expression utilizing an algorithm, MutSigCV. Somatic point mutation data from the exome kit‐targeted area from 24 exome‐sequenced sporadic MSI CRCs and respective normals, and 12 whole‐genome‐sequenced sporadic MSI CRCs and respective normals were utilized. The top 73 genes were validated in 93 additional MSI CRCs. The MutSigCV ranking identified several well‐established MSI CRC driver genes and provided additional evidence for previously proposed CRC candidate genes as well as shortlisted genes that have to our knowledge not been linked to CRC before. Two genes, SMARCB1 and STK38L, were also functionally scrutinized, providing evidence of a tumorigenic role, for SMARCB1 mutations in particular.
Collapse
Affiliation(s)
- Johanna Kondelin
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Kari Salokas
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.,Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Lilli Saarinen
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Kristian Ovaska
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Heli Rauanheimo
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Roosa-Maria Plaketti
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Jiri Hamberg
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Xiaonan Liu
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.,Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Leena Yadav
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.,Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Alexandra E Gylfe
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Tatiana Cajuso
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Ulrika A Hänninen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Kimmo Palin
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Heikki Ristolainen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Riku Katainen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Eevi Kaasinen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Tomas Tanskanen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Mervi Aavikko
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Minna Taipale
- Division of Functional Genomics, Department of Medical Biochemistry and Biophysics (MBB), Karolinska Institutet, Stockholm, Sweden
| | - Jussi Taipale
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland.,Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden.,Science for Life Center, Huddinge, Sweden
| | - Laura Renkonen-Sinisalo
- Department of Surgery, Helsinki University Central Hospital, Hospital District of Helsinki and Uusimaa, Helsinki, Finland
| | - Anna Lepistö
- Department of Surgery, Helsinki University Central Hospital, Hospital District of Helsinki and Uusimaa, Helsinki, Finland
| | - Selja Koskensalo
- The HUCH Gastrointestinal Clinic, Helsinki University Central Hospital, Helsinki, Finland
| | - Jan Böhm
- Department of Pathology, Jyväskylä Central Hospital, Jyväskylä, Finland
| | - Jukka-Pekka Mecklin
- Department of Surgery, Jyväskylä Central Hospital, University of Eastern Finland, Jyväskylä, Finland.,Department Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Halit Ongen
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Outi Kilpivaara
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Pia Vahteristo
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Mikko Turunen
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Sampsa Hautaniemi
- Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Sari Tuupanen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Auli Karhu
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Niko Välimäki
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Markku Varjosalo
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland.,Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Esa Pitkänen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland.,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Lauri A Aaltonen
- Medicum/Department of Medical and Clinical Genetics, University of Helsinki, Helsinki, Finland .,Genome-Scale Biology Research Program, Research Programs Unit, University of Helsinki, Helsinki, Finland
| |
Collapse
|
34
|
Kim YJ, Kim K, Lee YY, Choo OS, Jang JH, Choung YH. Downregulated UCHL1 Accelerates Gentamicin-Induced Auditory Cell Death via Autophagy. Mol Neurobiol 2019; 56:7433-7447. [PMID: 31041655 DOI: 10.1007/s12035-019-1598-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 04/02/2019] [Indexed: 01/02/2023]
Abstract
The clinical use of aminoglycoside antibiotics is partly limited by their ototoxicity. The pathogenesis of aminoglycoside-induced ototoxicity still remains unknown. Here, RNA-sequencing was conducted to identify differentially expressed genes in rat cochlear organotypic cultures treated with gentamicin (GM), and 232 and 43 genes were commonly up- and downregulated, respectively, at day 1 and 2 after exposure. Ubiquitin carboxyl-terminal hydrolase isozyme L1 (Uchl1) was one of the downregulated genes whose expression was prominent in spiral ganglion cells (SGCs), lateral walls, as well as efferent nerve terminal and nerve fibers. We further investigated if a deficit of Uchl1 in organotypic cochlea and the House Ear Institute-Organ of Corti 1 (HEI-OC1) cells accelerates ototoxicity. We found that a deficit in Uchl1 accelerated GM-induced ototoxicity by showing a decreased number of SGCs and nerve fibers in organotypic cochlear cultures and HEI-OC1 cells. Furthermore, Uchl1-depleted HEI-OC1 cells revealed an increased number of autophagosomes accompanied by decreased lysosomal fusion. These data indicate that the downregulation of Uchl1 following GM treatment is deleterious to auditory cell survival, which results from the impaired autophagic flux. Our results provide evidence that UCHL1-dependent autophagic flux may have a potential as an otoprotective target for the treatment of GM-induced auditory cell death.
Collapse
Affiliation(s)
- Yeon Ju Kim
- Department of Otolaryngology, Ajou University School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea
| | - Kyung Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yun Yeong Lee
- Department of Otolaryngology, Ajou University School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea
| | - Oak-Sung Choo
- Department of Otolaryngology, Ajou University School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea.,Department of Medical Sciences, Ajou University Graduate School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea
| | - Jeong Hun Jang
- Department of Otolaryngology, Ajou University School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea
| | - Yun-Hoon Choung
- Department of Otolaryngology, Ajou University School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea. .,Department of Medical Sciences, Ajou University Graduate School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea. .,BK21 Plus Research Center for Biomedical Sciences, Ajou University Graduate School of Medicine, San 5 Woncheon-dong, Yeongtong-gu, Suwon, 443-721, Republic of Korea.
| |
Collapse
|
35
|
Myllymäki SM, Kämäräinen UR, Liu X, Cruz SP, Miettinen S, Vuorela M, Varjosalo M, Manninen A. Assembly of the β4-Integrin Interactome Based on Proximal Biotinylation in the Presence and Absence of Heterodimerization. Mol Cell Proteomics 2019; 18:277-293. [PMID: 30404858 PMCID: PMC6356083 DOI: 10.1074/mcp.ra118.001095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 11/01/2018] [Indexed: 01/19/2023] Open
Abstract
Integrin-mediated laminin adhesions mediate epithelial cell anchorage to basement membranes and are critical regulators of epithelial cell polarity. Integrins assemble large multiprotein complexes that link to the cytoskeleton and convey signals into the cells. Comprehensive proteomic analyses of actin network-linked focal adhesions (FA) have been performed, but the molecular composition of intermediate filament-linked hemidesmosomes (HD) remains incompletely characterized. Here we have used proximity-dependent biotin identification (BioID) technology to label and characterize the interactome of epithelia-specific β4-integrin that, as α6β4-heterodimer, forms the core of HDs. The analysis identified ∼150 proteins that were specifically labeled by BirA-tagged integrin-β4. In addition to known HDs proteins, the interactome revealed proteins that may indirectly link integrin-β4 to actin-connected protein complexes, such as FAs and dystrophin/dystroglycan complexes. The specificity of the screening approach was validated by confirming the HD localization of two candidate β4-interacting proteins, utrophin (UTRN) and ELKS/Rab6-interacting/CAST family member 1 (ERC1). Interestingly, although establishment of functional HDs depends on the formation of α6β4-heterodimers, the assembly of β4-interactome was not strictly dependent on α6-integrin expression. Our survey to the HD interactome sets a precedent for future studies and provides novel insight into the mechanisms of HD assembly and function of the β4-integrin.
Collapse
Affiliation(s)
- Satu-Marja Myllymäki
- Oulu Center for Cell-Matrix Research, Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland;.
| | - Ulla-Reetta Kämäräinen
- Oulu Center for Cell-Matrix Research, Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland
| | - Xiaonan Liu
- Institute of Biotechnology and Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Sara Pereira Cruz
- Oulu Center for Cell-Matrix Research, Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland
| | - Sini Miettinen
- Institute of Biotechnology and Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Mikko Vuorela
- Oulu Center for Cell-Matrix Research, Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland
| | - Markku Varjosalo
- Institute of Biotechnology and Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Aki Manninen
- Oulu Center for Cell-Matrix Research, Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Finland;.
| |
Collapse
|
36
|
Kotlyar M, Pastrello C, Malik Z, Jurisica I. IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Res 2019; 47:D581-D589. [PMID: 30407591 PMCID: PMC6323934 DOI: 10.1093/nar/gky1037] [Citation(s) in RCA: 131] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 10/15/2018] [Accepted: 10/28/2018] [Indexed: 12/11/2022] Open
Abstract
Knowing the set of physical protein-protein interactions (PPIs) that occur in a particular context-a tissue, disease, or other condition-can provide valuable insights into key research questions. However, while the number of identified human PPIs is expanding rapidly, context information remains limited, and for most non-human species context-specific networks are completely unavailable. The Integrated Interactions Database (IID) provides one of the most comprehensive sets of context-specific human PPI networks, including networks for 133 tissues, 91 disease conditions, and many other contexts. Importantly, it also provides context-specific networks for 17 non-human species including model organisms and domesticated animals. These species are vitally important for drug discovery and agriculture. IID integrates interactions from multiple databases and datasets. It comprises over 4.8 million PPIs annotated with several types of context: tissues, subcellular localizations, diseases, and druggability information (the latter three are new annotations not available in the previous version). This update increases the number of species from 6 to 18, the number of PPIs from ∼1.5 million to ∼4.8 million, and the number of tissues from 30 to 133. IID also now supports topology and enrichment analyses of returned networks. IID is available at http://ophid.utoronto.ca/iid.
Collapse
Affiliation(s)
- Max Kotlyar
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Chiara Pastrello
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Zara Malik
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Igor Jurisica
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON M5S 1A4, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| |
Collapse
|
37
|
Halder AK, Dutta P, Kundu M, Basu S, Nasipuri M. Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions. Brief Funct Genomics 2018; 17:381-391. [PMID: 29028879 PMCID: PMC7109800 DOI: 10.1093/bfgp/elx026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.
Collapse
Affiliation(s)
- Anup Kumar Halder
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Pritha Dutta
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mahantapas Kundu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, India
| |
Collapse
|
38
|
Webber JT, Kaushik S, Bandyopadhyay S. Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics. Cell Syst 2018; 7:526-536.e6. [PMID: 30414925 DOI: 10.1016/j.cels.2018.10.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 08/01/2018] [Accepted: 10/04/2018] [Indexed: 02/08/2023]
Abstract
Leveraging insights from genomic studies of patient tumors is limited by the discordance between these tumors and the cell line models used for functional studies. We integrate omics datasets using functional networks to identify gene modules reflecting variation between tumors and show that the structure of these modules can be evaluated in cell lines to discover clinically relevant biomarkers of therapeutic responses. Applied to breast cancer, we identify 219 gene modules that capture recurrent alterations and subtype patients and quantitate various cell types within the tumor microenvironment. Comparison of modules between tumors and cell lines reveals that many modules composed primarily of gene expression and methylation are poorly preserved. In contrast, preserved modules are highly predictive of drug responses in a manner that is robust and clinically relevant. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.
Collapse
Affiliation(s)
- James T Webber
- Department of Bioengineering and Therapeutic Sciences, Institute for Computational Health Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Swati Kaushik
- Department of Bioengineering and Therapeutic Sciences, Institute for Computational Health Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Sourav Bandyopadhyay
- Department of Bioengineering and Therapeutic Sciences, Institute for Computational Health Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|
39
|
Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
Collapse
Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| |
Collapse
|
40
|
An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders. Nat Genet 2018; 50:1032-1040. [PMID: 29892012 DOI: 10.1038/s41588-018-0130-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 04/06/2018] [Indexed: 01/20/2023]
Abstract
Identifying disease-associated missense mutations remains a challenge, especially in large-scale sequencing studies. Here we establish an experimentally and computationally integrated approach to investigate the functional impact of missense mutations in the context of the human interactome network and test our approach by analyzing ~2,000 de novo missense mutations found in autism subjects and their unaffected siblings. Interaction-disrupting de novo missense mutations are more common in autism probands, principally affect hub proteins, and disrupt a significantly higher fraction of hub interactions than in unaffected siblings. Moreover, they tend to disrupt interactions involving genes previously implicated in autism, providing complementary evidence that strengthens previously identified associations and enhances the discovery of new ones. Importantly, by analyzing de novo missense mutation data from six disorders, we demonstrate that our interactome perturbation approach offers a generalizable framework for identifying and prioritizing missense mutations that contribute to the risk of human disease.
Collapse
|
41
|
Dutta P, Basu S, Kundu M. Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:839-849. [PMID: 28371781 DOI: 10.1109/tcbb.2017.2689762] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction affinity. Existing semantic similarity measures make use of the GO graph structure, the information content of GO terms, or a combination of both. In this paper, we present a hybrid approach which utilizes both the topological features of the GO graph and information contents of the GO terms. More specifically, we 1) consider a fuzzy clustering of the GO graph based on the level of association of the GO terms, 2) estimate the GO term memberships to each cluster center based on the respective shortest path lengths, and 3) assign weightage to GO term pairs on the basis of their dissimilarity with respect to the cluster centers. We test the performance of our semantic similarity measure against seven other previously published similarity measures using benchmark protein-protein interaction datasets of Homo sapiens and Saccharomyces cerevisiae based on sequence similarity, Pfam similarity, area under ROC curve, and measure.
Collapse
|
42
|
NIPS, a 3D network-integrated predictor of deleterious protein SAPs, and its application in cancer prognosis. Sci Rep 2018; 8:6021. [PMID: 29662108 PMCID: PMC5902451 DOI: 10.1038/s41598-018-24286-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 03/27/2018] [Indexed: 12/20/2022] Open
Abstract
Identifying deleterious mutations remains a challenge in cancer genome sequencing projects, reflecting the vast number of candidate mutations per tumour and the existence of interpatient heterogeneity. Based on a 3D protein interaction network profiled via large-scale cross-linking mass spectrometry, we propose a weighted average formula involving the combination of three types of information into a 'meta-score'. We assume that a single amino acid polymorphism (SAP) may have a deleterious effect if the mutation rarely occurs naturally during evolution, if it inhibits binding between a pair of interacting proteins when located at their interface, or if it plays an important role in a protein interaction (PPI) network. Cross-validation indicated that this new method presents an AUC value of 0.93 and outperforms other widely used tools. The application of this method to the CPTAC colorectal cancer dataset enabled the accurate identification of validated deleterious mutations and yielded insights into their potential pathogenesis. Survival analysis showed that the accumulation of deleterious SAPs is significantly associated with a poor prognosis. The new method provides an alternative method to identifying and ranking deleterious cancer SAPs based on a 3D PPI network and will contribute to the understanding of pathogenesis and the discovery of prognostic biomarkers.
Collapse
|
43
|
Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 2018; 6:484-495.e5. [PMID: 29605183 DOI: 10.1016/j.cels.2018.03.001] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/19/2017] [Accepted: 02/28/2018] [Indexed: 12/27/2022]
Abstract
Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.
Collapse
|
44
|
Karayel Ö, Şanal E, Giese SH, Üretmen Kagıalı ZC, Polat AN, Hu CK, Renard BY, Tuncbag N, Özlü N. Comparative phosphoproteomic analysis reveals signaling networks regulating monopolar and bipolar cytokinesis. Sci Rep 2018; 8:2269. [PMID: 29396449 PMCID: PMC5797227 DOI: 10.1038/s41598-018-20231-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/05/2018] [Indexed: 01/21/2023] Open
Abstract
The successful completion of cytokinesis requires the coordinated activities of diverse cellular components including membranes, cytoskeletal elements and chromosomes that together form partly redundant pathways, depending on the cell type. The biochemical analysis of this process is challenging due to its dynamic and rapid nature. Here, we systematically compared monopolar and bipolar cytokinesis and demonstrated that monopolar cytokinesis is a good surrogate for cytokinesis and it is a well-suited system for global biochemical analysis in mammalian cells. Based on this, we established a phosphoproteomic signature of cytokinesis. More than 10,000 phosphorylation sites were systematically monitored; around 800 of those were up-regulated during cytokinesis. Reconstructing the kinase-substrate interaction network revealed 31 potentially active kinases during cytokinesis. The kinase-substrate network connects proteins between cytoskeleton, membrane and cell cycle machinery. We also found consensus motifs of phosphorylation sites that can serve as biochemical markers specific to cytokinesis. Beyond the kinase-substrate network, our reconstructed signaling network suggests that combination of sumoylation and phosphorylation may regulate monopolar cytokinesis specific signaling pathways. Our analysis provides a systematic approach to the comparison of different cytokinesis types to reveal alternative ways and a global overview, in which conserved genes work together and organize chromatin and cytoplasm during cytokinesis.
Collapse
Affiliation(s)
- Özge Karayel
- Department of Molecular Biology and Genetics, Koç University, Istanbul, Turkey
| | - Erdem Şanal
- Department of Molecular Biology and Genetics, Koç University, Istanbul, Turkey
| | - Sven H Giese
- Bioinformatics Division (MF1), Robert Koch Institute, Berlin, Germany
- Chair of Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany
| | | | - Ayşe Nur Polat
- Department of Molecular Biology and Genetics, Koç University, Istanbul, Turkey
| | - Chi-Kuo Hu
- Department of Genetics, Stanford University, School of Medicine, CA, USA
| | - Bernhard Y Renard
- Bioinformatics Division (MF1), Robert Koch Institute, Berlin, Germany
| | - Nurcan Tuncbag
- Graduate School of Informatics, Department of Health Informatics, METU, Ankara, Turkey
- Cancer Systems Biology Laboratory (CanSyL), METU, Ankara, Turkey
| | - Nurhan Özlü
- Department of Molecular Biology and Genetics, Koç University, Istanbul, Turkey.
- Koç University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey.
| |
Collapse
|
45
|
Meyer MJ, Beltrán JF, Liang S, Fragoza R, Rumack A, Liang J, Wei X, Yu H. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 2018; 15:107-114. [PMID: 29355848 PMCID: PMC6026581 DOI: 10.1038/nmeth.4540] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 10/22/2017] [Indexed: 02/07/2023]
Abstract
We present Interactome INSIDER, a tool to link genomic variant information with
structural protein-protein interactomes. Underlying this tool is the application of
machine learning to predict protein interaction interfaces for 185,957 protein
interactions with previously unresolved interfaces, in human and 7 model organisms,
including the entire experimentally determined human binary interactome. Predicted
interfaces exhibit similar functional properties as known interfaces, including enrichment
for disease mutations and recurrent cancer mutations. Through 2,164 de
novo mutagenesis experiments, we show that mutations of predicted and known
interface residues disrupt interactions at a similar rate, and much more frequently than
mutations outside of predicted interfaces. To spur functional genomic studies, Interactome
INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether
variants or disease mutations are enriched in known and predicted interaction interfaces
at various resolutions. Users may explore known population variants, disease mutations,
and somatic cancer mutations, or upload their own set of mutations for this purpose.
Collapse
Affiliation(s)
- Michael J Meyer
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA
| | - Juan Felipe Beltrán
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Siqi Liang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Robert Fragoza
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
| | - Aaron Rumack
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Jin Liang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Xiaomu Wei
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Department of Medicine, Weill Cornell College of Medicine, New York, New York, USA
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| |
Collapse
|
46
|
De Las Rivas J, Alonso-López D, Arroyo MM. Human Interactomics: Comparative Analysis of Different Protein Interaction Resources and Construction of a Cancer Protein–Drug Bipartite Network. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2018; 111:263-282. [DOI: 10.1016/bs.apcsb.2017.09.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
47
|
Crosara KTB, Moffa EB, Xiao Y, Siqueira WL. Merging in - silico and in vitro salivary protein complex partners using the STRING database: A tutorial. J Proteomics 2018; 171:87-94. [DOI: 10.1016/j.jprot.2017.08.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2017] [Revised: 07/28/2017] [Accepted: 08/01/2017] [Indexed: 12/20/2022]
|
48
|
Delahaye-Duriez A, Srivastava P, Shkura K, Langley SR, Laaniste L, Moreno-Moral A, Danis B, Mazzuferi M, Foerch P, Gazina EV, Richards K, Petrou S, Kaminski RM, Petretto E, Johnson MR. Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery. Genome Biol 2016; 17:245. [PMID: 27955713 PMCID: PMC5154105 DOI: 10.1186/s13059-016-1097-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 11/02/2016] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy. RESULTS We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons. CONCLUSIONS Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy.
Collapse
Affiliation(s)
- Andree Delahaye-Duriez
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK.
- MRC Clinical Sciences Centre, Imperial College London, London, UK.
- Université Paris 13, Sorbonne Paris Cité, UFR de Santé, Médecine et Biologie Humaine, Paris, France.
- PROTECT, INSERM, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
| | - Prashant Srivastava
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
| | - Kirill Shkura
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
| | - Sarah R Langley
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
- Duke-NUS Medical School, 8 College Road, 169857, Singapore, Republic of Singapore
| | - Liisi Laaniste
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK
| | - Aida Moreno-Moral
- MRC Clinical Sciences Centre, Imperial College London, London, UK
- Duke-NUS Medical School, 8 College Road, 169857, Singapore, Republic of Singapore
| | - Bénédicte Danis
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Manuela Mazzuferi
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Patrik Foerch
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Elena V Gazina
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Kay Richards
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Steven Petrou
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
- The Centre for Neural Engineering, The Department of Electrical Engineering, The University of Melbourne, Parkville, Victoria, 3052, Australia
- The Australian Research Council Centre of Excellence for Integrative Brain Function, Parkville, Victoria, 3052, Australia
| | - Rafal M Kaminski
- Neuroscience TA, UCB Pharma, S.A, Allée de la Recherche, 60, 1070, Brussels, Belgium
| | - Enrico Petretto
- MRC Clinical Sciences Centre, Imperial College London, London, UK.
- Duke-NUS Medical School, 8 College Road, 169857, Singapore, Republic of Singapore.
| | - Michael R Johnson
- Division of Brain Sciences, Imperial College Faculty of Medicine, London, UK.
| |
Collapse
|
49
|
Dimitrakopoulos CM, Beerenwinkel N. Computational approaches for the identification of cancer genes and pathways. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 9. [PMID: 27863091 PMCID: PMC5215607 DOI: 10.1002/wsbm.1364] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 07/26/2016] [Accepted: 08/23/2016] [Indexed: 12/27/2022]
Abstract
High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364 For further resources related to this article, please visit the WIREs website.
Collapse
Affiliation(s)
- Christos M Dimitrakopoulos
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| |
Collapse
|
50
|
Affiliation(s)
- Mohamed Helmy
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | | | - Gary D. Bader
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
- * E-mail:
| |
Collapse
|