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Hao T, Zhang M, Song Z, Gou Y, Wang B, Sun J. Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method. Curr Issues Mol Biol 2024; 46:7353-7372. [PMID: 39057077 PMCID: PMC11276262 DOI: 10.3390/cimb46070436] [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: 05/26/2024] [Revised: 06/25/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.
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Affiliation(s)
| | | | | | | | - Bin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (M.Z.); (Z.S.); (Y.G.)
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (M.Z.); (Z.S.); (Y.G.)
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2
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Parallel and distributed association rule mining in life science: A novel parallel algorithm to mine genomics data. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2018.07.055] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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3
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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.
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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
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4
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Neighborhoods to Nucleotides - Advances and gaps for an obesity disparities systems epidemiology model. CURR EPIDEMIOL REP 2019; 6:476-485. [PMID: 36643055 PMCID: PMC9839192 DOI: 10.1007/s40471-019-00221-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Purpose of Review Disparities in obesity rates in the US continue to increase. Here we review progress and highlight gaps in understanding disparities in obesity with a focus on the Hispanic/Latino population from a systems epidemiology framework. We review seven domains: environment, behavior, biomarkers, nutrition, microbiome, genomics, and epigenomics/transcriptomics. We focus on recent advances that include at least two or more of these domains, and then provide a real world example of data collection efforts that reflect these domains. Recent Findings Research into DNA methylation related to discrimination and microbiome relating to eating behaviors and food content is furthering understanding of why disparities in obesity persist. Environmental and neighborhood level research is uncovering the importance of exposures such as air and noise pollution and systematic or structural racism for obesity and related outcomes through behaviors such as sleep.
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Wang Q, Shi G, Zhang Y, Lu F, Xie D, Wen C, Huang L. Deciphering the Potential Pharmaceutical Mechanism of GUI-ZHI-FU-LING-WAN on Systemic Sclerosis based on Systems Biology Approaches. Sci Rep 2019; 9:355. [PMID: 30674993 PMCID: PMC6344516 DOI: 10.1038/s41598-018-36314-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 11/08/2018] [Indexed: 12/13/2022] Open
Abstract
Systemic sclerosis (SSc; scleroderma) is a complicated idiopathic connective tissue disease with seldom effective treatment. GUI-ZHI-FU-LING-WAN (GFW) is a classic Traditional Chinese Medicine (TCM) formula widely used for the treatment of SSc. However, the mechanism of how the GFW affects SSc remains unclear. In this study, the system biology approach was utilized to analyze herb compounds and related targets to get the general information of GFW. The KEGG enrichment analysis of 1645 related targets suggested that the formula is involved in the VEGF signaling pathway, the Toll-like receptor signaling pathway, etc. Quantitative and qualitative analysis of the relationship among the 3 subsets (formula targets, drug targets and disease genes) showed that the formula targets overlapped with 38.0% drug targets and 26.0% proteins encoded by disease genes. Through the analysis of SSc related microarray statistics from the GEO database, we also validated the consistent expression behavior among the 3 subsets before and after treatment. To further reveal the mechanism of prescription, we constructed a network among 3 subsets and decomposed it into 24 modules to decipher how GFW interfere in the progress of SSc. The modules indicated that the intervention may come into effect through following pathogenic processes: vasculopathy, immune dysregulation and tissue fibrosis. Vitro experiments confirmed that GFW could suppress the proliferation of fibroblasts and decrease the Th1 cytokine (TNF-α, MIP-2 and IL-6) expression for lipopolysaccharide (LPS) and bleomycin (BLM) stimulation in macrophages, which is consistent with previous conclusion that GFW is able to relieve SSc. The systems biology approach provides a new insight for deepening understanding about TCM.
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Affiliation(s)
- Qiao Wang
- TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, 548 Binwen Road, Hangzhou, Zhejiang, 310000, China
| | - Guoshan Shi
- Department of Integrative Traditional & Western Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, 225001, China
| | - Yun Zhang
- TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, 548 Binwen Road, Hangzhou, Zhejiang, 310000, China
| | - Feilong Lu
- TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, 548 Binwen Road, Hangzhou, Zhejiang, 310000, China
| | - Duoli Xie
- TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, 548 Binwen Road, Hangzhou, Zhejiang, 310000, China
| | - Chengping Wen
- TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, 548 Binwen Road, Hangzhou, Zhejiang, 310000, China.
| | - Lin Huang
- TCM Clinical Basis Institute, Zhejiang Chinese Medicine University, 548 Binwen Road, Hangzhou, Zhejiang, 310000, China.
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6
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Behinaein B, Rudie K, Sangrar W. Petri net siphon analysis and graph theoretic measures for identifying combination therapies in cancer. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:231-243. [PMID: 28113516 DOI: 10.1109/tcbb.2016.2614301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Epidermal Growth Factor Receptor (EGFR) signaling to the Ras-MAPK pathway is implicated in the development and progression of cancer and is a major focus of targeted combination therapies. Physiochemical models have been used for identifying and testing the signal-inhibiting potential of targeted therapies, however, their application to larger multi-pathway networks is limited by the availability of experimentally-determined rate and concentration parameters. An alternate strategy for identifying and evaluating drug-targetable nodes is proposed. A physiochemical model of EGFR-Ras-MAPK signaling is implemented and calibrated to experimental data. Essential topological features of the model are converted into a Petri net and nodes that behave as siphons-a structural property of Petri nets-are identified. Siphons represent potential drug-targets since they are unrecoverable if their values fall below a threshold. Centrality measures are then used to prioritize siphons identified as candidate drug-targets. Single and multiple drug-target combinations are identified which correspond to clinically relevant drug targets and exhibit inhibition synergy in physiochemical simulations of EGF-induced EGFR-Ras-MAPK signaling. Taken together, these studies suggest that siphons and centrality analyses are a promising computational strategy to identify and rank drug-targetable nodes in larger networks as they do not require knowledge of the dynamics of the system, but rely solely on topology.
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Establishment of a Strong Link Between Smoking and Cancer Pathogenesis through DNA Methylation Analysis. Sci Rep 2017; 7:1811. [PMID: 28500316 PMCID: PMC5431893 DOI: 10.1038/s41598-017-01856-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 04/03/2017] [Indexed: 12/21/2022] Open
Abstract
Smoking is a well-documented risk factor in various cancers, especially lung cancer. In the current study, we tested the hypothesis that abnormal DNAm loci associated with smoking are enriched in genes and pathways that convey a risk of cancer by determining whether smoking-related methylated genes led to enrichment in cancer-related pathways. We analyzed two sets of smoking-related methylated genes from 28 studies originating from blood and buccal samples. By analyzing 320 methylated genes from 26 studies on blood samples (N = 17,675), we found 57 enriched pathways associated with different types of cancer (FDR < 0.05). Of these, 11 were also significantly overrepresented in the 661 methylated genes from two studies of buccal samples (N = 1,002). We further found the aryl hydrocarbon receptor signaling pathway plays an important role in the initiation of smoking-attributable cancer. Finally, we constructed a subnetwork of genes important for smoking-attributable cancer from the 48 non-redundant genes in the 11 oncogenic pathways. Of these, genes such as DUSP4 and AKT3 are well documented as being involved in smoking-related lung cancer. In summary, our findings provide robust and systematic evidence in support of smoking’s impact on the epigenome, which may be an important contributor to cancer.
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8
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Chang JW, Zhou YQ, Ul Qamar MT, Chen LL, Ding YD. Prediction of Protein-Protein Interactions by Evidence Combining Methods. Int J Mol Sci 2016; 17:ijms17111946. [PMID: 27879651 PMCID: PMC5133940 DOI: 10.3390/ijms17111946] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 11/15/2016] [Accepted: 11/15/2016] [Indexed: 12/27/2022] Open
Abstract
Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.
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Affiliation(s)
- Ji-Wei Chang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yan-Qing Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Muhammad Tahir Ul Qamar
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Ling Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Yu-Duan Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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Ivan T, Enkvist E, Viira B, Manoharan GB, Raidaru G, Pflug A, Alam KA, Zaccolo M, Engh RA, Uri A. Bifunctional Ligands for Inhibition of Tight-Binding Protein-Protein Interactions. Bioconjug Chem 2016; 27:1900-10. [PMID: 27389935 DOI: 10.1021/acs.bioconjchem.6b00293] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The acknowledged potential of small-molecule therapeutics targeting disease-related protein-protein interactions (PPIs) has promoted active research in this field. The strategy of using small molecule inhibitors (SMIs) to fight strong (tight-binding) PPIs tends to fall short due to the flat and wide interfaces of PPIs. Here we propose a biligand approach for disruption of strong PPIs. The potential of this approach was realized for disruption of the tight-binding (KD = 100 pM) tetrameric holoenzyme of cAMP-dependent protein kinase (PKA). Supported by X-ray analysis of cocrystals, bifunctional inhibitors (ARC-inhibitors) were constructed that simultaneously associated with both the ATP-pocket and the PPI interface area of the catalytic subunit of PKA (PKAc). Bifunctional inhibitor ARC-1411, possessing a KD value of 3 pM toward PKAc, induced the dissociation of the PKA holoenzyme with a low-nanomolar IC50, whereas the ATP-competitive inhibitor H89 bound to the PKA holoenzyme without disruption of the protein tetramer.
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Affiliation(s)
- Taavi Ivan
- Institute of Chemistry, University of Tartu , 50410 Tartu, Estonia
| | - Erki Enkvist
- Institute of Chemistry, University of Tartu , 50410 Tartu, Estonia
| | - Birgit Viira
- Institute of Chemistry, University of Tartu , 50410 Tartu, Estonia
| | | | - Gerda Raidaru
- Institute of Chemistry, University of Tartu , 50410 Tartu, Estonia
| | - Alexander Pflug
- The Norwegian Structural Biology Centre, Department of Chemistry, University of Tromsø , N-9019 Tromsø, Norway
| | - Kazi Asraful Alam
- The Norwegian Structural Biology Centre, Department of Chemistry, University of Tromsø , N-9019 Tromsø, Norway
| | - Manuela Zaccolo
- Department of Physiology, Anatomy and Genetics, University of Oxford , OX1 3QX Oxford, United Kingdom
| | - Richard Alan Engh
- The Norwegian Structural Biology Centre, Department of Chemistry, University of Tromsø , N-9019 Tromsø, Norway
| | - Asko Uri
- Institute of Chemistry, University of Tartu , 50410 Tartu, Estonia
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Jeanquartier F, Jean-Quartier C, Kotlyar M, Tokar T, Hauschild AC, Jurisica I, Holzinger A. Machine Learning for In Silico Modeling of Tumor Growth. LECTURE NOTES IN COMPUTER SCIENCE 2016. [DOI: 10.1007/978-3-319-50478-0_21] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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12
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Abascal MF, Besso MJ, Rosso M, Mencucci MV, Aparicio E, Szapiro G, Furlong LI, Vazquez-Levin MH. CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools. Comput Biol Chem 2015; 60:9-20. [PMID: 26674224 DOI: 10.1016/j.compbiolchem.2015.10.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Revised: 10/17/2015] [Accepted: 10/19/2015] [Indexed: 12/13/2022]
Abstract
Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell-cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin, gene: CDH1) is a key molecule in cell-cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database. DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL Databases, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database. This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease.
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Affiliation(s)
- María Florencia Abascal
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
| | - María José Besso
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
| | - Marina Rosso
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
| | - María Victoria Mencucci
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
| | - Evangelina Aparicio
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
| | - Gala Szapiro
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
| | - Laura Inés Furlong
- Research Programme on Biomedical Informatics (GRIB) (IMIM), DCEXS, Universitat Pompeu Fabra, C/Dr Aiguader 88, Zip Code 08003, Barcelona, Spain.
| | - Mónica Hebe Vazquez-Levin
- Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología & Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina; Laboratory of Cell-Cell Interaction in Cancer and Reproduction, Instituto de Biología y Medicina Experimental (IBYME), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Fundación IBYME (FIBYME), Vuelta de Obligado 2490, Zip Code C1428ADN, Buenos Aires, Argentina.
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Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, LeCluyse E, Clewell HJ, Thomas RS, Andersen ME. Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing. Toxicol Sci 2015; 148:121-36. [PMID: 26251325 PMCID: PMC4620046 DOI: 10.1093/toxsci/kfv171] [Citation(s) in RCA: 170] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast efforts expand (ie, Phase II) beyond food-use pesticides toward a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. Environmental Protection Agency (EPA) ExpoCast program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, 3 or 13 chemicals possessed AERs < 1 or < 100, respectively. Diverse bioactivities across a range of assays and concentrations were also noted across the wider chemical space surveyed. The availability of HT exposure estimation and bioactivity screening tools provides an opportunity to incorporate a risk-based strategy for use in testing prioritization.
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Affiliation(s)
- Barbara A Wetmore
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137;
| | - John F Wambaugh
- United States Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711; and
| | - Brittany Allen
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Stephen S Ferguson
- Life Technologies, ADME/Tox Division of the Primary and Stem Cell Systems Business Unit, Durham, North Carolina 27703
| | - Mark A Sochaski
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
| | - R Woodrow Setzer
- United States Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711; and
| | - Keith A Houck
- United States Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711; and
| | - Cory L Strope
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Katherine Cantwell
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Richard S Judson
- United States Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711; and
| | - Edward LeCluyse
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Harvey J Clewell
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Russell S Thomas
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137; United States Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711; and
| | - Melvin E Andersen
- *The Hamner Institutes for Health Sciences, Institute for Chemical Safety Sciences, Research Triangle Park, North Carolina 27709-2137
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14
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DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks. Sci Rep 2015. [PMID: 26220783 PMCID: PMC4518224 DOI: 10.1038/srep12569] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Biochemical networks are dynamic and multi-dimensional systems, consisting of tens or hundreds of molecular components. Diseases such as cancer commonly arise due to changes in the dynamics of signalling and gene regulatory networks caused by genetic alternations. Elucidating the network dynamics in health and disease is crucial to better understand the disease mechanisms and derive effective therapeutic strategies. However, current approaches to analyse and visualise systems dynamics can often provide only low-dimensional projections of the network dynamics, which often does not present the multi-dimensional picture of the system behaviour. More efficient and reliable methods for multi-dimensional systems analysis and visualisation are thus required. To address this issue, we here present an integrated analysis and visualisation framework for high-dimensional network behaviour which exploits the advantages provided by parallel coordinates graphs. We demonstrate the applicability of the framework, named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC), to a variety of signalling networks ranging in topological wirings and dynamic properties. The framework was proved useful in acquiring an integrated understanding of systems behaviour.
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15
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Dubchak I, Balasubramanian S, Wang S, Meyden C, Sulakhe D, Poliakov A, Börnigen D, Xie B, Taylor A, Ma J, Paciorkowski AR, Mirzaa GM, Dave P, Agam G, Xu J, Al-Gazali L, Mason CE, Ross ME, Maltsev N, Gilliam TC. An integrative computational approach for prioritization of genomic variants. PLoS One 2014; 9:e114903. [PMID: 25506935 PMCID: PMC4266634 DOI: 10.1371/journal.pone.0114903] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 11/15/2014] [Indexed: 12/27/2022] Open
Abstract
An essential step in the discovery of molecular mechanisms contributing to disease phenotypes and efficient experimental planning is the development of weighted hypotheses that estimate the functional effects of sequence variants discovered by high-throughput genomics. With the increasing specialization of the bioinformatics resources, creating analytical workflows that seamlessly integrate data and bioinformatics tools developed by multiple groups becomes inevitable. Here we present a case study of a use of the distributed analytical environment integrating four complementary specialized resources, namely the Lynx platform, VISTA RViewer, the Developmental Brain Disorders Database (DBDB), and the RaptorX server, for the identification of high-confidence candidate genes contributing to pathogenesis of spina bifida. The analysis resulted in prediction and validation of deleterious mutations in the SLC19A placental transporter in mothers of the affected children that causes narrowing of the outlet channel and therefore leads to the reduced folate permeation rate. The described approach also enabled correct identification of several genes, previously shown to contribute to pathogenesis of spina bifida, and suggestion of additional genes for experimental validations. The study demonstrates that the seamless integration of bioinformatics resources enables fast and efficient prioritization and characterization of genomic factors and molecular networks contributing to the phenotypes of interest.
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Affiliation(s)
- Inna Dubchak
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
- Department of Energy Joint Genome Institute, Walnut Creek, California, United States of America
- * E-mail: (ID); (NM)
| | - Sandhya Balasubramanian
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Sheng Wang
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Cem Meyden
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, New York, United States of America
| | - Dinanath Sulakhe
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Computation Institute, University of Chicago/Argonne National Laboratory, Chicago, Illinois, United States of America
| | - Alexander Poliakov
- Department of Energy Joint Genome Institute, Walnut Creek, California, United States of America
| | - Daniela Börnigen
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Bingqing Xie
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois, United States of America
| | - Andrew Taylor
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Jianzhu Ma
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Alex R. Paciorkowski
- Departments of Neurology, Pediatrics, and Biomedical Genetics and Center for Neural Development and Disease, University of Rochester Medical Center, Rochester, New York, United States of America
| | - Ghayda M. Mirzaa
- Seattle Children's Research Institute and Department of Pediatrics, University of Washington, Seattle, Washington, United States of America
| | - Paul Dave
- Computation Institute, University of Chicago/Argonne National Laboratory, Chicago, Illinois, United States of America
| | - Gady Agam
- Department of Computer Science, Illinois Institute of Technology, Chicago, Illinois, United States of America
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, Illinois, United States of America
| | - Lihadh Al-Gazali
- Department of Pediatrics, Faculty of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE
| | - Christopher E. Mason
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, New York, New York, United States of America
| | - M. Elizabeth Ross
- Laboratory of Neurogenetics and Development, Weill Cornell Medical College, New York, New York, United States of America
| | - Natalia Maltsev
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Computation Institute, University of Chicago/Argonne National Laboratory, Chicago, Illinois, United States of America
- * E-mail: (ID); (NM)
| | - T. Conrad Gilliam
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Computation Institute, University of Chicago/Argonne National Laboratory, Chicago, Illinois, United States of America
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16
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Kotlyar M, Pastrello C, Pivetta F, Lo Sardo A, Cumbaa C, Li H, Naranian T, Niu Y, Ding Z, Vafaee F, Broackes-Carter F, Petschnigg J, Mills GB, Jurisicova A, Stagljar I, Maestro R, Jurisica I. In silico prediction of physical protein interactions and characterization of interactome orphans. Nat Methods 2014; 12:79-84. [PMID: 25402006 DOI: 10.1038/nmeth.3178] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Accepted: 08/14/2014] [Indexed: 12/12/2022]
Abstract
Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).
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Affiliation(s)
- Max Kotlyar
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Chiara Pastrello
- 1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Centro Riferimento Oncologico, Istituto Nazionale Tumori, Aviano, Italy
| | - Flavia Pivetta
- Centro Riferimento Oncologico, Istituto Nazionale Tumori, Aviano, Italy
| | | | - Christian Cumbaa
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Han Li
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Taline Naranian
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Yun Niu
- 1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Zhiyong Ding
- Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Fatemeh Vafaee
- 1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Fiona Broackes-Carter
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Julia Petschnigg
- Donnelly Centre, Departments of Molecular Genetics and Biochemistry, University of Toronto, Toronto, Ontario, Canada
| | - Gordon B Mills
- Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Andrea Jurisicova
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Igor Stagljar
- Donnelly Centre, Departments of Molecular Genetics and Biochemistry, University of Toronto, Toronto, Ontario, Canada
| | - Roberta Maestro
- Centro Riferimento Oncologico, Istituto Nazionale Tumori, Aviano, Italy
| | - Igor Jurisica
- 1] Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. [2] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. [4] TECHNA Institute for the Advancement of Technology for Health, Toronto, Ontario, Canada
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17
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Newman RH, Zhang J, Zhu H. Toward a systems-level view of dynamic phosphorylation networks. Front Genet 2014; 5:263. [PMID: 25177341 PMCID: PMC4133750 DOI: 10.3389/fgene.2014.00263] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Accepted: 07/16/2014] [Indexed: 11/13/2022] Open
Abstract
To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks.
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Affiliation(s)
- Robert H Newman
- Department of Biology, North Carolina Agricultural and Technical State University Greensboro, NC, USA
| | - Jin Zhang
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine Baltimore, MD, USA ; The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine Baltimore, MD, USA ; Department of Oncology, Johns Hopkins University School of Medicine Baltimore, MD, USA ; Department of Chemical and Biomolecular Engineering, Johns Hopkins University School of Medicine Baltimore, MD, USA
| | - Heng Zhu
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine Baltimore, MD, USA ; High-Throughput Biology Center, Institute for Basic Biomedical Sciences, Johns Hopkins University Baltimore, MD, USA
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18
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Holzinger A, Dehmer M, Jurisica I. Knowledge Discovery and interactive Data Mining in Bioinformatics--State-of-the-Art, future challenges and research directions. BMC Bioinformatics 2014; 15 Suppl 6:I1. [PMID: 25078282 PMCID: PMC4140208 DOI: 10.1186/1471-2105-15-s6-i1] [Citation(s) in RCA: 134] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- Andreas Holzinger
- Research Unit Human-Computer Interaction, Austrian IBM Watson Think Group, Institute for Medical Informatics, Statistics & Documentation, Medical University Graz, Austria
- Institute of Information Systems and Computer Media, Graz University of Technology, Austria
| | - Matthias Dehmer
- Institute for Bioinformatics and Translational Research, UMIT Tyrol, Austria
| | - Igor Jurisica
- Departments of Medical Biophysics and Computer Science, University of Toronto, Ontario, Canada
- Princess Margaret Cancer Centre and Techna Institute for the Advancement of Technology for Health, University Health Network, IBM Life Sciences Discovery Centre, Ontario, Canada
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19
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Stagljar I. Editorial for ''advances in OMICs-based disciplines". Biochem Biophys Res Commun 2014; 445:681-682. [PMID: 24691180 DOI: 10.1016/j.bbrc.2014.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Affiliation(s)
- Igor Stagljar
- Donnelly Centre, Department of Molecular Genetics, Department of Biochemistry, University of Toronto, 160 College Street, Room 1204, Toronto, Ontario M5S 3E1, Canada.
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20
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Holzinger A, Jurisica I. Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions. INTERACTIVE KNOWLEDGE DISCOVERY AND DATA MINING IN BIOMEDICAL INFORMATICS 2014. [DOI: 10.1007/978-3-662-43968-5_1] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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