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Trostchansky A, Alarcon M. An Overview of Two Old Friends Associated with Platelet Redox Signaling, the Protein Disulfide Isomerase and NADPH Oxidase. Biomolecules 2023; 13:biom13050848. [PMID: 37238717 DOI: 10.3390/biom13050848] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 12/30/2022] [Accepted: 01/18/2023] [Indexed: 05/28/2023] Open
Abstract
Oxidative stress participates at the baseline of different non-communicable pathologies such as cardiovascular diseases. Excessive formation of reactive oxygen species (ROS), above the signaling levels necessary for the correct function of organelles and cells, may contribute to the non-desired effects of oxidative stress. Platelets play a relevant role in arterial thrombosis, by aggregation triggered by different agonists, where excessive ROS formation induces mitochondrial dysfunction and stimulate platelet activation and aggregation. Platelet is both a source and a target of ROS, thus we aim to analyze both the platelet enzymes responsible for ROS generation and their involvement in intracellular signal transduction pathways. Among the proteins involved in these processes are Protein Disulphide Isomerase (PDI) and NADPH oxidase (NOX) isoforms. By using bioinformatic tools and information from available databases, a complete bioinformatic analysis of the role and interactions of PDI and NOX in platelets, as well as the signal transduction pathways involved in their effects was performed. We focused the study on analyzing whether these proteins collaborate to control platelet function. The data presented in the current manuscript support the role that PDI and NOX play on activation pathways necessary for platelet activation and aggregation, as well as on the platelet signaling imbalance produced by ROS production. Our data could be used to design specific enzyme inhibitors or a dual inhibition for these enzymes with an antiplatelet effect to design promising treatments for diseases involving platelet dysfunction.
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Affiliation(s)
- Andrés Trostchansky
- Departamento de Bioquímica and Center for Free Radical and Biomedical Research, Facultad de Medicina, Universidad de la República, Montevideo 11800, Uruguay
| | - Marcelo Alarcon
- Thrombosis Research Center, Universidad de Talca, Talca 3460000, Chile
- Department of Clinical Biochemistry and Immunohematology, Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile
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Bovo S, Martelli PL, Di Lena P, Casadio R. NETGE-PLUS: Standard and Network-Based Gene Enrichment Analysis in Human and Model Organisms. J Proteome Res 2020; 19:2873-2878. [PMID: 31971806 DOI: 10.1021/acs.jproteome.9b00749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Omics techniques provide a spectrum of information at the genomic level, whose analysis can characterize complex traits at a molecular level. The relationship among genotype and phenotype implies that from genome information the molecular pathways and biological processes underlying a given phenotype are discovered. In dealing with this problem, gene enrichment analysis has become the most widely adopted strategy. Here we present NETGE-PLUS, a Web server for standard and network-based functional interpretation of gene sets of human and of model organisms, including Sus scrofa, Saccharomyces cerevisiae, Escherichia coli, and Arabidopsis thaliana. NETGE-PLUS enables the functional enrichment of both simple and ranked lists of genes, introducing also the possibility of exploring relationships among KEGG pathways. A Web interface makes data retrieval complete and user-friendly. NETGE-PLUS is publicly available at http://net-ge2.biocomp.unibo.it.
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Affiliation(s)
- Samuele Bovo
- Biocomputing Group, Department of Pharmacy and Biotechnology (FABIT), University of Bologna, Via San Giacomo 9/2, 40126 Bologna, Italy.,Department of Agricultural and Food Sciences (DISTAL), Division of Animal Sciences, University of Bologna, Viale Fanin 46, 40127 Bologna, Italy
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology (FABIT), University of Bologna, Via San Giacomo 9/2, 40126 Bologna, Italy
| | - Pietro Di Lena
- Department of Computer Science and Engineering (DISI), University of Bologna, Mura Anteo Zamboni 7, 40126 Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology (FABIT), University of Bologna, Via San Giacomo 9/2, 40126 Bologna, Italy.,Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), 70126 Bari, Italy
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3
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Association study based on topological constraints of protein-protein interaction networks. Sci Rep 2020; 10:10797. [PMID: 32612246 PMCID: PMC7329836 DOI: 10.1038/s41598-020-67875-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 06/15/2020] [Indexed: 12/17/2022] Open
Abstract
The non-random interaction pattern of a protein–protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, modified Zʹ-scores, p-values and Jaccard indices. Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and WebGestalt. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.
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4
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Blatti C, Emad A, Berry MJ, Gatzke L, Epstein M, Lanier D, Rizal P, Ge J, Liao X, Sobh O, Lambert M, Post CS, Xiao J, Groves P, Epstein AT, Chen X, Srinivasan S, Lehnert E, Kalari KR, Wang L, Weinshilboum RM, Song JS, Jongeneel CV, Han J, Ravaioli U, Sobh N, Bushell CB, Sinha S. Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform. PLoS Biol 2020; 18:e3000583. [PMID: 31971940 PMCID: PMC6977717 DOI: 10.1371/journal.pbio.3000583] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 12/19/2019] [Indexed: 12/19/2022] Open
Abstract
We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.
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Affiliation(s)
- Charles Blatti
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Amin Emad
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Electrical and Computer Engineering, McGill University, Montreal, Canada
| | - Matthew J. Berry
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Lisa Gatzke
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Milt Epstein
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Daniel Lanier
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Pramod Rizal
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jing Ge
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Xiaoxia Liao
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Omar Sobh
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Mike Lambert
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Corey S. Post
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jinfeng Xiao
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Peter Groves
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Aidan T. Epstein
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Xi Chen
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Subhashini Srinivasan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Erik Lehnert
- Seven Bridges Genomics, Charlestown, Massachusetts, United States of America
| | - Krishna R. Kalari
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Jun S. Song
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - C. Victor Jongeneel
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jiawei Han
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Umberto Ravaioli
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Nahil Sobh
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Colleen B. Bushell
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- * E-mail:
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Li Y, Ding Q, Xiong Z, Wen H, Feng C. Overexpression of steroid sulfotransferase genes is associated with worsened prognosis and with immune exclusion in clear cell-renal cell carcinoma. Aging (Albany NY) 2019; 11:9209-9219. [PMID: 31655797 PMCID: PMC6834411 DOI: 10.18632/aging.102392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 10/21/2019] [Indexed: 12/11/2022]
Abstract
Aim: Steroid sulfotransferase (SULT) plays physiological roles but its role in clear cell-renal cell carcinoma (ccRCC) remains unclear. We therefore investigated genetic alteration of steroid SULT genes in ccRCC. Results: Overexpression of any of SULT genes occurred in ~8% of ccRCC patients. Overexpression of steroid SULT genes was associated with worsened prognosis. Steroid SULT gene-upregulated ccRCC cases showed mutual exclusivity with mutations of VHL, SETD2 and PBRM1, and with focal deletions of 3p and 9p, respectively. Expressions of SULT genes were negatively correlated with that of VHL, SETD2 and PBRM1, respectively. While no cancer-intrinsic pathway was enriched, immune signatures were significantly enriched in SULT gene-overexpressed cases, resulting in significantly fewer infiltration of lymphocytes. Targeting SULT1B1 significantly inhibited growth of ccRCC cells. Conclusion: Steroid SULT genes were associated with worsened prognosis and with immune exclusion in ccRCC. Methods: In silico reproduction of TGGA and GTEx datasets was performed. Data were processed comprehensively using the platforms of cBioPotal, GEPIA, Human Protein Atlas, TIMER, respectively. Functional annotation was analyzed using platforms of NET-GE and GSEA, respectively. In vitro assays were performed for validation.
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Affiliation(s)
- Yuqing Li
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, PR China
| | - Qiang Ding
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, PR China
| | - Zuquan Xiong
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, PR China
| | - Hui Wen
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, PR China
| | - Chenchen Feng
- Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, PR China
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Guo T, Li B, Gu C, Chen X, Han M, Liu X, Xu C. GCN-5/PGC-1α signaling is activated and associated with metabolism in cyclin E1-driven ovarian cancer. Aging (Albany NY) 2019; 11:4890-4899. [PMID: 31313989 PMCID: PMC6682509 DOI: 10.18632/aging.102082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 07/01/2019] [Indexed: 12/24/2022]
Abstract
AIM We previously found Cyclin E1-driven high grade serous ovarian cancer (HGSOC) showed metabolic shift. In this study, we aimed to elucidate signaling pathway therein. METHODS In silico reproduction of TCGA ovarian cancer dataset and pathway analysis were performed. Candidate metabolic pathway was validated using in vitro and in vivo assays. RESULTS We found CCNE1-amplified HGSOC showed significant metabolic alteration besides canonical cell cycle control. Using CCNE1-amplified OVCAR-3 and A2780 OvCa cells, we found that knockdown of CDK2 and GCN5 resulted in decreased G6PC and increased PGC-1α level, and that both genetic and pharmaceutical (MB-3) inhibition of GCN5 resulted in significant decrease in acetylation of PGC-1α. Silencing of CDK2 also resulted in significant decrease in acetylation of both PGC-1α and Rb. GCN5-KD significantly decreased glucose uptake, increased lactate production and decreased SDH activity. Western blots showed hierarchy of the elements indicating Cyclin E1-CDK2/GCN5/PGC-1α regulatory axis from up- to down- stream. Inhibitory effect of Dinaciclib was similar to that of GCN5 silencing, whereas combination therapy further inhibited cell proliferation significantly. Similar findings were noted also in cell cycle arrest, apoptosis, invasion, migration and colony formation assays. Xenograft experiments showed that GCN5-KD alone did not alter tumor growth yet combination therapy of Dinaciclib and GCN5-KD conferred significant inhibition of tumor growth compared with either therapy alone with no toxicity observed. CONCLUSION GCN-5/PGC-1α signaling is activated and associated with metabolism in Cyclin E1-driven HGSOC. Targeting GCN5 hold promise to augment current CDK2-targeting strategy and further studies are warranted for clinical translation.
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Affiliation(s)
- Ting Guo
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
| | - Bin Li
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
| | - Chao Gu
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
| | - Xiuying Chen
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
| | - Mengxin Han
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
| | - Xiaocheng Liu
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
| | - Congjian Xu
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, PR China
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7
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Li S, Cao Y, Li L, Zhang H, Lu X, Bo C, Kong X, Liu Z, Chen L, Liu P, Jiao Y, Wang J, Ning S, Wang L. Building the drug-GO function network to screen significant candidate drugs for myasthenia gravis. PLoS One 2019; 14:e0214857. [PMID: 30947317 PMCID: PMC6448860 DOI: 10.1371/journal.pone.0214857] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 03/22/2019] [Indexed: 12/17/2022] Open
Abstract
Myasthenia gravis (MG) is an autoimmune disease. In recent years, considerable evidence has indicated that Gene Ontology (GO) functions, especially GO-biological processes, have important effects on the mechanisms and treatments of different diseases. However, the roles of GO functions in the pathogenesis and treatment of MG have not been well studied. This study aimed to uncover the potential important roles of risk-related GO functions and to screen significant candidate drugs related to GO functions for MG. Based on MG risk genes, 238 risk GO functions and 42 drugs were identified. Through constructing a GO function network, we discovered that positive regulation of NF-kappaB transcription factor activity (GO:0051092) may be one of the most important GO functions in the mechanism of MG. Furthermore, we built a drug-GO function network to help evaluate the latent relationship between drugs and GO functions. According to the drug-GO function network, 5 candidate drugs showing promise for treating MG were identified. Indeed, 2 out of 5 candidate drugs have been investigated to treat MG. Through functional enrichment analysis, we found that the mechanisms between 5 candidate drugs and associated GO functions may involve two vital pathways, specifically hsa05332 (graft-versus-host disease) and hsa04940 (type I diabetes mellitus). More interestingly, most of the processes in these two pathways were consistent. Our study will not only reveal a new perspective on the mechanisms and novel treatment strategies of MG, but also will provide strong support for research on GO functions.
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Affiliation(s)
- Shuang Li
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yuze Cao
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Lei Li
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Huixue Zhang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xiaoyu Lu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Chunrui Bo
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xiaotong Kong
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Zhaojun Liu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Lixia Chen
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Peifang Liu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Yang Jiao
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Jianjian Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
- * E-mail: (LW); (SN); (JW)
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China
- * E-mail: (LW); (SN); (JW)
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
- * E-mail: (LW); (SN); (JW)
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Glaab E. Computational systems biology approaches for Parkinson's disease. Cell Tissue Res 2018; 373:91-109. [PMID: 29185073 PMCID: PMC6015628 DOI: 10.1007/s00441-017-2734-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/06/2017] [Indexed: 12/26/2022]
Abstract
Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.
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Affiliation(s)
- Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
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Pita-Juárez Y, Altschuler G, Kariotis S, Wei W, Koler K, Green C, Tanzi RE, Hide W. The Pathway Coexpression Network: Revealing pathway relationships. PLoS Comput Biol 2018; 14:e1006042. [PMID: 29554099 PMCID: PMC5875878 DOI: 10.1371/journal.pcbi.1006042] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 03/29/2018] [Accepted: 02/19/2018] [Indexed: 02/02/2023] Open
Abstract
A goal of genomics is to understand the relationships between biological processes. Pathways contribute to functional interplay within biological processes through complex but poorly understood interactions. However, limited functional references for global pathway relationships exist. Pathways from databases such as KEGG and Reactome provide discrete annotations of biological processes. Their relationships are currently either inferred from gene set enrichment within specific experiments, or by simple overlap, linking pathway annotations that have genes in common. Here, we provide a unifying interpretation of functional interaction between pathways by systematically quantifying coexpression between 1,330 canonical pathways from the Molecular Signatures Database (MSigDB) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways valid in a broad context using a curated collection of 3,207 microarrays from 72 normal human tissues. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions rather than with similar annotations. We demonstrate that PCxN provides novel insight into mechanisms of complex diseases using an Alzheimer’s Disease (AD) case study. PCxN retrieved pathways significantly correlated with an expert curated AD gene list. These pathways have known associations with AD and were significantly enriched for genes independently associated with AD. As a further step, we show how PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways, and by identifying additional highly correlated pathways. PCxN revealed that correlated pathways from an AD expression profiling study include functional clusters involved in cell adhesion and oxidative stress. PCxN provides expanded connections to pathways from the extracellular matrix. PCxN provides a powerful new framework for interrogation of global pathway relationships. Comprehensive exploration of PCxN can be performed at http://pcxn.org/. Genes do not function alone, but interact within pathways to carry out specific biological processes. Pathways, in turn, interact at a higher level to affect major cellular activities such as motility, growth and development. We present a pathway coexpression network (PCxN) that systematically maps and quantifies these high-level interactions and establishes a unifying reference for pathway relationships. The method uses 3,207 human microarrays from 72 normal human tissues and 1,330 of the most well established pathway annotations to describe global relationships between pathways. PCxN accounts for shared genes to estimate correlations between pathways with related functions rather than with redundant pathway definitions. PCxN can be used to discover and explore pathways correlated with a pathway of interest. We applied PCxN to identify key processes related to Alzheimer’s disease (AD), interpreting a mixed genetic association and experimental derived set of disease genes in the context of gene co-expression. We expand the known relationships between pathways identified by gene set enrichment analysis in brain tissues affected with AD. PCxN provides a high-level overview of pathway relationships. PCxN is available as a webtool at http://pcxn.org/, and as a Bioconductor package at http://bioconductor.org/packages/pcxn/.
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Affiliation(s)
- Yered Pita-Juárez
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States of America
| | - Gabriel Altschuler
- Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Sokratis Kariotis
- Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Wenbin Wei
- Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Katjuša Koler
- Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Claire Green
- Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Rudolph E. Tanzi
- Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Winston Hide
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States of America
- Sheffield Institute for Translational Neuroscience, Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
- Harvard Stem Cell Institute, Cambridge, Massachusetts, United States of America
- National Institute Health Research, Sheffield Biomedical Research Centre, Sheffield, United Kingdom
- * E-mail:
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10
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Hu Y, Fang Z, Yang Y, Fan T, Wang J. Analyzing the pathways enriched in genes associated with nicotine dependence in the context of human protein-protein interaction network. J Biomol Struct Dyn 2018; 37:1177-1188. [PMID: 29546796 DOI: 10.1080/07391102.2018.1453377] [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: 10/17/2022]
Abstract
Nicotine dependence is the primary addictive stage of cigarette smoking. Although a lot of studies have been performed to explore the molecular mechanism underlying nicotine dependence, our understanding on this disorder is still far from complete. Over the past decades, an increasing number of candidate genes involved in nicotine dependence have been identified by different technical approaches, including the genetic association analysis. In this study, we performed a comprehensive collection of candidate genes reported to be genetically associated with nicotine dependence. Then, the biochemical pathways enriched in these genes were identified by considering the gene's propensity to be related to nicotine dependence. One of the most widely used pathway enrichment analysis approach, over-representation analysis, ignores the function non-equivalence of genes in candidate gene set and may have low discriminative power in identifying some dysfunctional pathways. To overcome such drawbacks, we constructed a comprehensive human protein-protein interaction network, and then assigned a function weighting score to each candidate gene based on their network topological features. Evaluation indicated the function weighting score scheme was consistent with available evidence. Finally, the function weighting scores of the candidate genes were incorporated into pathway analysis to identify the dysfunctional pathways involved in nicotine dependence, and the interactions between pathways was detected by pathway crosstalk analysis. Compared to conventional over-representation-based pathway analysis tool, the modified method exhibited improved discriminative power and detected some novel pathways potentially underlying nicotine dependence. In summary, we conducted a comprehensive collection of genes associated with nicotine dependence and then detected the biochemical pathways enriched in these genes using a modified pathway enrichment analysis approach with function weighting score of candidate genes integrated. Our results may provide insight into the molecular mechanism underlying nicotine dependence.
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Affiliation(s)
- Ying Hu
- a School of Biomedical Engineering , Tianjin Medical University , Tianjin 300070 , China
| | - Zhonghai Fang
- a School of Biomedical Engineering , Tianjin Medical University , Tianjin 300070 , China
| | - Yichen Yang
- a School of Biomedical Engineering , Tianjin Medical University , Tianjin 300070 , China
| | - Ting Fan
- a School of Biomedical Engineering , Tianjin Medical University , Tianjin 300070 , China
| | - Ju Wang
- a School of Biomedical Engineering , Tianjin Medical University , Tianjin 300070 , China
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Babbi G, Martelli PL, Profiti G, Bovo S, Savojardo C, Casadio R. eDGAR: a database of Disease-Gene Associations with annotated Relationships among genes. BMC Genomics 2017; 18:554. [PMID: 28812536 PMCID: PMC5558190 DOI: 10.1186/s12864-017-3911-3] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis. RESULTS We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes. CONCLUSIONS eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar.biocomp.unibo.it.
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Affiliation(s)
- Giulia Babbi
- Biocomputing Group, BiGeA, University of Bologna, Bologna, Italy
| | | | - Giuseppe Profiti
- Biocomputing Group, BiGeA, University of Bologna, Bologna, Italy
| | - Samuele Bovo
- Biocomputing Group, BiGeA, University of Bologna, Bologna, Italy
| | | | - Rita Casadio
- Biocomputing Group, BiGeA, University of Bologna, Bologna, Italy.,Interdepartmental Center «Giorgio Prodi» for Cancer Research, University of Bologna, Bologna, Italy
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Vella D, Zoppis I, Mauri G, Mauri P, Di Silvestre D. From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2017; 2017:6. [PMID: 28477207 PMCID: PMC5359264 DOI: 10.1186/s13637-017-0059-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 03/09/2017] [Indexed: 12/19/2022]
Abstract
The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.
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Affiliation(s)
- Danila Vella
- Institute for Biomedical Technologies - National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy.,Department of Computer Science, Systems and Communication DiSCo, University of Milano-Bicocca, 336 Viale Sarca, Milan, Italy
| | - Italo Zoppis
- Department of Computer Science, Systems and Communication DiSCo, University of Milano-Bicocca, 336 Viale Sarca, Milan, Italy
| | - Giancarlo Mauri
- Department of Computer Science, Systems and Communication DiSCo, University of Milano-Bicocca, 336 Viale Sarca, Milan, Italy
| | - Pierluigi Mauri
- Institute for Biomedical Technologies - National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy
| | - Dario Di Silvestre
- Institute for Biomedical Technologies - National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy.
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Bovo S, Di Lena P, Martelli PL, Fariselli P, Casadio R. NET-GE: a web-server for NETwork-based human gene enrichment. Bioinformatics 2016; 32:3489-3491. [PMID: 27485441 DOI: 10.1093/bioinformatics/btw508] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 07/18/2016] [Accepted: 07/20/2016] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Gene enrichment is a requisite for the interpretation of biological complexity related to specific molecular pathways and biological processes. Furthermore, when interpreting NGS data and human variations, including those related to pathologies, gene enrichment allows the inclusion of other genes that in the human interactome space may also play important key roles in the emergency of the phenotype. Here, we describe NET-GE, a web server for associating biological processes and pathways to sets of human proteins involved in the same phenotype RESULTS: NET-GE is based on protein-protein interaction networks, following the notion that for a set of proteins, the context of their specific interactions can better define their function and the processes they can be related to in the biological complexity of the cell. Our method is suited to extract statistically validated enriched terms from Gene Ontology, KEGG and REACTOME annotation databases. Furthermore, NET-GE is effective even when the number of input proteins is small. AVAILABILITY AND IMPLEMENTATION NET-GE web server is publicly available and accessible at http://net-ge.biocomp.unibo.it/enrich CONTACT: gigi@biocomp.unibo.itSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Samuele Bovo
- Biocomputing Group, CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy
| | | | - Pier Luigi Martelli
- Biocomputing Group, CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy
| | | | - Rita Casadio
- Biocomputing Group, CIG, Interdepartmental Center «Luigi Galvani» for Integrated Studies of Bioinformatics, Biophysics and Biocomplexity, University of Bologna, Bologna, Italy.,Interdepartmental Center «Giorgio Prodi» for Cancer Research, University of Bologna, Bologna, Italy
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Bromberg Y, Capriotti E. VarI-SIG 2014--From SNPs to variants: interpreting different types of genetic variants. BMC Genomics 2015; 16 Suppl 8:I1. [PMID: 26110281 PMCID: PMC4480323 DOI: 10.1186/1471-2164-16-s8-i1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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