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Jiang Y, Li J, Liu Y, Shen X, Li J, Zhi F, Xu J, Li X, Shao T, Xu Y. Open a new epoch of arsenic trioxide investigation: ATOdb. Comput Biol Med 2023; 165:107465. [PMID: 37699323 DOI: 10.1016/j.compbiomed.2023.107465] [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: 06/27/2023] [Revised: 08/30/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023]
Abstract
Arsenic trioxide (ATO) is a great discovery in the treatment of acute promyelocytic leukemia (APL), which has been used in an increasing number of malignant diseases. Systematic integrative analysis will help to precisely understand the mechanism of ATO and find new combined drugs. Therefore, we developed a one-stop comprehensive database of ATO named ATOdb by manually compiling a wealth of experimentally supported ATO-related data from 3479 articles, and integrated analysis tools. The current version of ATOdb contains 8373 associations among 2300 ATO targets, 80 conditions and 262 combined drugs. Each entry in ATOdb contains detailed information on ATO targets, therapeutic/side effects, systems, cell names, cell types, regulations, detection methods, brief descriptions, references, etc. Furthermore, ATOdb also provides data visualization and analysis results such as the drug similarities, protein-protein interactions, and miRNA-mRNA relationships. An easy-to-use web interface was deployed in ATOdb for users to easily browse, search and download the data. In conclusion, ATOdb will serve as a valuable resource for in-depth study of the mechanism of ATO, discovery of new drug combination strategies, promotion of rational drug use and individualized treatments. ATOdb is freely available at http://bio-bigdata.hrbmu.edu.cn/ATOdb/index.jsp.
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Affiliation(s)
- Yanan Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China; Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin 150081, China
| | - Jianing Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yujie Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xiuyun Shen
- Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China
| | - Junyi Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Fengnan Zhi
- Department of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tingting Shao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yingqi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Hohhot Mongolian Medicine of Traditional Chinese Medicine Hospital, Hohhot, 010110, China.
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2
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El Hadi C, Ayoub G, Bachir Y, Haykal M, Jalkh N, Kourie HR. Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer. Expert Rev Mol Diagn 2022; 22:427-438. [PMID: 35400274 DOI: 10.1080/14737159.2022.2065195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. AREAS COVERED The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress. EXPERT OPINION In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.
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Affiliation(s)
| | - George Ayoub
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Yara Bachir
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Michèle Haykal
- Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Nadine Jalkh
- Medical Genetics Unit, Technology and Health division, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - Hampig Raphael Kourie
- Department of Hematology-Oncology, Hotel Dieu de France University Hospital, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
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3
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Agbo L, Blanchet SA, Kougnassoukou Tchara PE, Fradet-Turcotte A, Lambert JP. Comprehensive Interactome Mapping of Nuclear Receptors Using Proximity Biotinylation. Methods Mol Biol 2022; 2456:223-240. [PMID: 35612745 DOI: 10.1007/978-1-0716-2124-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Nuclear receptors, including hormone receptors, perform their cellular activities by modulating their protein-protein interactions. They engage with specific ligands and translocate to the nucleus, where they bind the DNA and activate extensive transcriptional programs. Therefore, gaining a comprehensive overview of the protein-protein interactions they establish requires methods that function effectively throughout the cell with fast dynamics and high reproducibility. Focusing on estrogen receptor alpha (ESR1), the founding member of the nuclear receptor family, this chapter describes a new lentiviral system that allows the expression of TurboID-hemagglutinin (HA)-2 × Strep tagged proteins in mammalian cells to perform fast proximity biotinylation assays. Key validation steps for these reagents and their use in interactome mapping experiments in two distinct breast cancer cell lines are described. Our protocol enabled the quantification of ESR1 interactome generated by cellular contexts that were hormone-sensitive or not.
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Affiliation(s)
- Lynda Agbo
- Department of Molecular Medicine, Cancer Research Center and Big Data Research Center, Université Laval, Québec, QC, Canada
- Endocrinology and Nephrology Division, CHU de Québec-Université Laval Research Center, Québec, QC, Canada
| | - Sophie Anne Blanchet
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec, QC, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Cancer Research Center, Université Laval, Québec, QC, Canada
| | - Pata-Eting Kougnassoukou Tchara
- Department of Molecular Medicine, Cancer Research Center and Big Data Research Center, Université Laval, Québec, QC, Canada
- Endocrinology and Nephrology Division, CHU de Québec-Université Laval Research Center, Québec, QC, Canada
| | - Amélie Fradet-Turcotte
- Oncology Division, CHU de Québec-Université Laval Research Center, Québec, QC, Canada.
- Department of Molecular Biology, Medical Biochemistry and Pathology, Cancer Research Center, Université Laval, Québec, QC, Canada.
| | - Jean-Philippe Lambert
- Department of Molecular Medicine, Cancer Research Center and Big Data Research Center, Université Laval, Québec, QC, Canada.
- Endocrinology and Nephrology Division, CHU de Québec-Université Laval Research Center, Québec, QC, Canada.
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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.
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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
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Shao M, Yang S, Dong S. High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma. PeerJ 2021; 9:e10560. [PMID: 33604163 PMCID: PMC7866887 DOI: 10.7717/peerj.10560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/22/2020] [Indexed: 12/25/2022] Open
Abstract
Backgrounds Lung adenocarcinoma is a complex disease that results in over 1.8 million deaths a year. Recent advancements in treating and managing lung adenocarcinoma have led to modest decreases in associated mortality rates, owing in part to the multifactorial etiology of the disease. Novel prognostic biomarkers are needed to accurately stage the disease and act as the basis of adjuvant treatments. Material and Methods The microarray datasets GSE75037, GSE31210 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify prognostic biomarkers for lung adenocarcinoma and therapy. The differentially expressed genes (DEGs) were identified by GEO2R. Functional and pathway enrichment analysis were performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology (GO). Validation was performed based on 72 pairs of lung adenocarcinoma and adjacent normal lung tissues. Results Results showed that the DEGs were mainly focused on cell cycle and DNA replication initiation. Forty-one hub genes were identified and further analyzed by CytoScape. Here, we provide evidence which suggests MCM10 is a potential target with prognostic, diagnostic and therapeutic value. We base this on an integrated approach of comprehensive bioinformatics analysis and in vitro validation using the A549 lung adenocarcinoma cell line. We show that MCM10 overexpression correlates with a poor prognosis, while silencing of this gene decreases aberrant growth by 2-fold. Finally, evaluation of 72 clinical biopsy samples suggests that overexpression of MCM10 in the lung adenocarcinoma highly correlates with larger tumor size. Together, this work suggests that MCM10 may be a clinically relevant gene with both predictive and therapeutic value in lung adenocarcinoma.
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Affiliation(s)
- Mingrui Shao
- Department of Thoracic Surgery, The first hospital of China Medical University, Shenyang, Liaoning, China
| | - Shize Yang
- Department of Thoracic Surgery, The first hospital of China Medical University, Shenyang, Liaoning, China
| | - Siyuan Dong
- Department of Thoracic Surgery, The first hospital of China Medical University, Shenyang, Liaoning, China
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Li Y, Burgman B, Khatri IS, Pentaparthi SR, Su Z, McGrail DJ, Li Y, Wu E, Eckhardt SG, Sahni N, Yi SS. e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks. Nucleic Acids Res 2021; 49:e2. [PMID: 33211847 PMCID: PMC7797045 DOI: 10.1093/nar/gkaa1015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 10/07/2020] [Accepted: 10/20/2020] [Indexed: 02/06/2023] Open
Abstract
Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.
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Affiliation(s)
- Yongsheng Li
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Burgman
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ishaani S Khatri
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
| | - Sairahul R Pentaparthi
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Zhe Su
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA
| | - Daniel J McGrail
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yang Li
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Science Park, Smithville, TX 78957, USA
| | - Erxi Wu
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Neuroscience Institute and Department of Neurosurgery, Baylor Scott & White Health, Temple, TX 76502, USA.,Department of Surgery, Texas A & M University Health Science Center, College of Medicine, Temple, TX 76508, USA.,Department of Pharmaceutical Sciences, Texas A & M University Health Science Center, College of Pharmacy, College Station, TX 77843, USA
| | - S Gail Eckhardt
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Nidhi Sahni
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Science Park, Smithville, TX 78957, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.,Program in Quantitative and Computational Biosciences (QCB), Baylor College of Medicine, Houston, TX 77030, USA
| | - S Stephen Yi
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA.,Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX 78712, USA.,Interdisciplinary Life Sciences Graduate Programs (ILSGP), College of Natural Sciences, The University of Texas at Austin, Austin, TX 78712, USA.,Department of Biomedical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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Zhang Y, Li W, Zhang Y, Hu E, Rong Z, Ge L, Deng G, He Y, Lv J, Chen L, He W. Network-based integration method for potential breast cancer gene identification. J Cell Physiol 2020; 235:7960-7969. [PMID: 31943201 DOI: 10.1002/jcp.29450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 01/03/2020] [Indexed: 11/11/2022]
Abstract
Breast cancer is the most common female death-causing cancer worldwide. A network-based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top-ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.
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Affiliation(s)
- Yue Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yihua Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Erqiang Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Zherou Rong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Luanfeng Ge
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Gui Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, Heilongjiang, China
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Stacey RG, Skinnider MA, Chik JHL, Foster LJ. Context-specific interactions in literature-curated protein interaction databases. BMC Genomics 2018; 19:758. [PMID: 30340458 PMCID: PMC6194712 DOI: 10.1186/s12864-018-5139-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 10/03/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Databases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study. RESULTS Using raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate. CONCLUSIONS We recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.
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Affiliation(s)
- R. Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 Canada
| | - Michael A. Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 Canada
| | - Jenny H. L. Chik
- Current Address: International Collaboration On Repair Discoveries (ICORD), Vancouver Coastal Health Research Institute and Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
| | - Leonard J. Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, V6T 1Z4 Canada
- Department of Biochemistry, University of British Columbia, Vancouver, V6T 1Z3 Canada
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Ghatge M, Nair J, Sharma A, Vangala RK. Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR. Mol Med Rep 2018; 17:4253-4264. [PMID: 29328373 PMCID: PMC5802197 DOI: 10.3892/mmr.2018.8393] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 11/14/2017] [Indexed: 12/27/2022] Open
Abstract
Coronary artery disease (CAD) is a major cause of mortality in India, more importantly the young Indians. Combinatorial and integrative approaches to evaluate pathways and genes to gain an improved understanding and potential biomarkers for risk assessment are required. Therefore, 608 genes from the CADgene database version 2.0, classified into 12 functional classes representing the atherosclerotic disease process, were analyzed. Homology analysis of the unique list of gene ontologies (GO) from each functional class gave 8 GO terms represented in 11 and 10 functional classes. Using disease ontology analysis 80 genes belonging to 8 GO terms, using FunDO suggested that 29 of them were identified to be associated with CAD. Extended network analysis of these genes using STRING version 9.1 gave 328 nodes and 4,525 interactions of which the top 5% had a node degree of ≥75 associated with pathways including the ErbB signaling pathway with epidermal growth factor receptor (EGFR) gene as the central hub. Evaluation of EFGR protein levels in age and gender-matched 342 CAD patients vs. 342 control subjects demonstrated significant differences [controls=149.76±2.47 pg/ml and CAD patients stratified into stable angina (SA)=161.65±3.40 pg/ml and myocardial infarction (MI)=171.51±4.26 pg/ml]. Logistic regression analysis suggested that increased EGFR levels exhibit 3-fold higher risk of CAD [odds ratio (OR) 3.51, 95% confidence interval [CI] 1.96–6.28, P≤0.001], upon adjustment for hypertension, diabetes and smoking. A unit increase in EGFR levels increased the risk by 2-fold for SA (OR 2.58, 95% CI 1.25–5.33, P=0.01) and 3.8-fold for MI (OR 3.82, 95% CI 1.94–7.52, P≤0.001) following adjustment. Thus, the use of ontology mapping and network analysis in an integrative manner aids in the prioritization of biomarkers of complex disease.
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Affiliation(s)
- Madankumar Ghatge
- Tata Proteomics and Coagulation Unit, Thrombosis Research Institute, Narayana Hrudayalaya Hospital, Bengaluru, Karnataka 560099, India
| | - Jiny Nair
- Mary and Garry Weston Functional Genomics Unit, Thrombosis Research Institute, Bengaluru, Karnataka 560099, India
| | - Ankit Sharma
- Manipal University, Manipal, Karnataka 576104, India
| | - Rajani Kanth Vangala
- Tata Proteomics and Coagulation Unit, Thrombosis Research Institute, Narayana Hrudayalaya Hospital, Bengaluru, Karnataka 560099, India
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Abstract
A complete understanding of human cancer variants requires new methods to systematically and efficiently assess the functional effects of genomic mutations at a large scale. Here, we describe a set of tools to rapidly clone and stratify thousands of cancer mutations at base resolution. This protocol provides a massively parallel pipeline to achieve high stringency and throughput. The approach includes high-throughput generation of mutant clones by Gateway, confirmation of variant identity by barcoding and next-generation sequencing, and stratification of cancer variants by multiplexed interaction profiling. Compared with alternative site-directed mutagenesis methods, our protocol requires less sequencing effort and enables robust statistical calling of allele-specific effects. To ensure the precision of variant interaction profiling, we further describe two complementary methods-a high-throughput enhanced yeast two-hybrid (HT-eY2H) assay and a mammalian-cell-based Gaussia princeps luciferase protein-fragment complementation assay (GPCA). These independent assays with standard controls validate mutational interaction profiles with high quality. This protocol provides experimentally derived guidelines for classifying candidate cancer alleles emerging from whole-genome or whole-exome sequencing projects as 'drivers' or 'passengers'. For ∼100 genomic mutations, the protocol-including target primer design, variant library construction, and sequence verification-can be completed within as little as 2-3 weeks, and cancer variant stratification can be completed within 2 weeks.
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