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Jiang Y, Rex DA, Schuster D, Neely BA, Rosano GL, Volkmar N, Momenzadeh A, Peters-Clarke TM, Egbert SB, Kreimer S, Doud EH, Crook OM, Yadav AK, Vanuopadath M, Hegeman AD, Mayta M, Duboff AG, Riley NM, Moritz RL, Meyer JG. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:338-417. [PMID: 39193565 PMCID: PMC11348894 DOI: 10.1021/acsmeasuresciau.3c00068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 08/29/2024]
Abstract
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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Affiliation(s)
- Yuming Jiang
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Devasahayam Arokia
Balaya Rex
- Center for
Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Dina Schuster
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
- Department
of Biology, Institute of Molecular Biology
and Biophysics, ETH Zurich, Zurich 8093, Switzerland
- Laboratory
of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen 5232, Switzerland
| | - Benjamin A. Neely
- Chemical
Sciences Division, National Institute of
Standards and Technology, NIST, Charleston, South Carolina 29412, United States
| | - Germán L. Rosano
- Mass
Spectrometry
Unit, Institute of Molecular and Cellular
Biology of Rosario, Rosario, 2000 Argentina
| | - Norbert Volkmar
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Amanda Momenzadeh
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Trenton M. Peters-Clarke
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California, 94158, United States
| | - Susan B. Egbert
- Department
of Chemistry, University of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada
| | - Simion Kreimer
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Emma H. Doud
- Center
for Proteome Analysis, Indiana University
School of Medicine, Indianapolis, Indiana, 46202-3082, United States
| | - Oliver M. Crook
- Oxford
Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United
Kingdom
| | - Amit Kumar Yadav
- Translational
Health Science and Technology Institute, NCR Biotech Science Cluster 3rd Milestone Faridabad-Gurgaon
Expressway, Faridabad, Haryana 121001, India
| | | | - Adrian D. Hegeman
- Departments
of Horticultural Science and Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota 55108, United States
| | - Martín
L. Mayta
- School
of Medicine and Health Sciences, Center for Health Sciences Research, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
- Molecular
Biology Department, School of Pharmacy and Biochemistry, Universidad Nacional de Rosario, Rosario 2000, Argentina
| | - Anna G. Duboff
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nicholas M. Riley
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Robert L. Moritz
- Institute
for Systems biology, Seattle, Washington 98109, United States
| | - Jesse G. Meyer
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
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2
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Vafaee R, Tavirani MR, Tavirani SR, Razzaghi M. Assessment of cancer prevention effect of exercise. Hum Antibodies 2021; 30:31-36. [PMID: 34459390 DOI: 10.3233/hab-210454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
There are many documents about benefits of exercise on human health. However, evidences indicate to positive effect of exercise on disease prevention, understanding of many aspects of this mechanism need more investigations. Determination of critical genes which effect human health.GSE156249 including 12 gene expression profiles of healthy individual biopsy from vastus lateralis muscle before and after 12-week combined exercise training intervention were extracted from gene expression omnibus (GEO) database. The significant DEGs were included in interactome unit by Cytoscape software and STRING database. The network was analyzed to find the central nodes subnetwork clusters. The nodes of prominent cluster were assessed via gene ontology by using ClueGO. Number of 8 significant DEGs and 100 first neighbors analyzed via network analysis. The network includes 2 clusters and COL3A1, BGN, and LOX were determined as central DEGs. The critical DEGs were involved in cancer prevention process.
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Affiliation(s)
- Reza Vafaee
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Rezaei Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sina Rezaei Tavirani
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Razzaghi
- Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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3
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Chen Y, Liu Q, Liu J, Wei P, Li B, Wang N, Liu Z, Wang Z. Revealing the Modular Similarities and Differences Among Alzheimer's Disease, Vascular Dementia, and Parkinson's Disease in Genomic Networks. Neuromolecular Med 2021; 24:125-138. [PMID: 34117614 DOI: 10.1007/s12017-021-08670-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 05/31/2021] [Indexed: 02/05/2023]
Abstract
Alzheimer's disease (AD), vascular dementia (VD), and Parkinson's disease (PD) exert increasingly lethal or disabling effects on humans, but the associations among these diseases at the molecular level remain unclear. In our research, lists of genes related to these three diseases were acquired from public databases. We constructed gene-gene networks of the lists of disease-related genes using the STRING database and selected the plug-in MCODE as the most suitable method to divide the three disease-associated networks into modules through an entropy calculation. Notably, 1173 AD-related, 203 VD-related, and 722 PD-related genes as well as 72 overlapping genes were observed among the three diseases. By dividing the modules from the gene network, we divided the AD-related gene network into 27 modules, the VD-related gene network into 8 modules, and the PD-related gene network into 17 modules. After the enrichment analysis of each disease-related gene, 146 overlapping biological processes and 32 overlapping pathways were identified. Ultimately, through similarity analysis of the genes, biological processes, and pathways, we found that AD and VD were the most closely related at the biological process and pathway levels, with similarity coefficients of 0.2784 and 0.3626, respectively. After analyzing the overlapping gene network, we found that INS might play an important role in the network and that insulin and its signaling pathways may play a key role in these neurodegenerative diseases. Our research illustrates a new method for in-depth research on the three diseases, which may accelerate the progress of developing new therapeutics and may be applied to prevent neurodegenerative diseases.
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Affiliation(s)
- Yafei Chen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Penglu Wei
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Bing Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Nongyun Wang
- State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Sichuan, China
| | - Zhenquan Liu
- National Institute of TCM Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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4
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Arjmand B, Vafaee R, Razzaghi M, Rezaei-Tavirani M, Ahmadzadeh A, Rezaei-Tavirani S, Hamdieh M. Central Proteins of Plasma in Response to Low-Level Laser Therapy Involve in Body Hemostasis and Wound Repair. J Lasers Med Sci 2020; 11:S55-S59. [PMID: 33995970 DOI: 10.34172/jlms.2020.s9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Introduction: Low-level laser therapy (LLLT) is accompanied by protein expression change in the body. There are many efforts to find a clear relationship between the differentially expressed proteins. This study aims to find the central differentiated expressed proteins of plasma after LLLT. Methods: Six proteins are extracted from a proteomics study and the network including these query proteins plus 100 first neighbors was constructed. The central proteins were determined based on degree value, betweenness centrality, closeness centrality (CC), and stress (The centrality parameters). Results: Among 106 nodes of the network, 10 proteins were characterized with the most values of degree, betweenness centrality, CC, and stress. These proteins were determined as central proteins in response to LLLT in plasma. Conclusion: Three query proteins, AHSG, FGG, and SERPINA1, plus 7 first neighbors, namely FGA, ALB, KNG1, FN1, APP, TIMP1, and F5, were identified as central proteins which were dysregulated.
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Affiliation(s)
- Babak Arjmand
- Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Vafaee
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohhamadreza Razzaghi
- Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Rezaei-Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Ahmadzadeh
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sina Rezaei-Tavirani
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Hamdieh
- Department of Psychosomatic, Taleghani Hospital, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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5
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Development of New Antiproliferative Compound against Human Tumor Cells from the Marine Microalgae Nannochloropsis gaditana by Applied Proteomics. Int J Mol Sci 2020; 22:ijms22010096. [PMID: 33374179 PMCID: PMC7795124 DOI: 10.3390/ijms22010096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 12/15/2020] [Accepted: 12/22/2020] [Indexed: 12/18/2022] Open
Abstract
Proteomics is a crucial tool for unravelling the molecular dynamics of essential biological processes, becoming a pivotal technique for basic and applied research. Diverse bioinformatic tools are required to manage and explore the huge amount of information obtained from a single proteomics experiment. Thus, functional annotation and protein-protein interactions are evaluated in depth leading to the biological conclusions that best fit the proteomic response in the system under study. To gain insight into potential applications of the identified proteins, a novel approach named "Applied Proteomics" has been developed by comparing the obtained protein information with the existing patents database. The development of massive sequencing technology and mass spectrometry (MS/MS) improvements has allowed the application of proteomics nonmodel microorganisms, which have been deeply described as a novel source of metabolites. Between them, Nannochloropsis gaditana has been pointed out as an alternative source of biomolecules. Recently, our research group has reported the first complete proteome analysis of this microalga, which was analysed using the applied proteomics concept with the identification of 488 proteins with potential industrial applications. To validate our approach, we selected the UCA01 protein from the prohibitin family. The recombinant version of this protein showed antiproliferative activity against two tumor cell lines, Caco2 (colon adenocarcinoma) and HepG-2 (hepatocellular carcinoma), proving that proteome data have been transformed into relevant biotechnological information. From Nannochloropsis gaditana has been developed a new tool against cancer-the protein named UCA01. This protein has selective effects inhibiting the growth of tumor cells, but does not show any effect on control cells. This approach describes the first practical approach to transform proteome information in a potential industrial application, named "applied proteomics". It is based on a novel bioalgorithm, which is able to identify proteins with potential industrial applications. From hundreds of proteins described in the proteome of N. gaditana, the bioalgorithm identified over 400 proteins with potential uses; one of them was selected as UCA01, "in vitro" and its potential was demonstrated against cancer. This approach has great potential, but the applications are potentially numerous and undefined.
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6
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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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7
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Grigorieva J, Asmellash S, Oliveira C, Roder H, Net L, Roder J. Application of protein set enrichment analysis to correlation of protein functional sets with mass spectral features and multivariate proteomic tests. CLINICAL MASS SPECTROMETRY 2020. [DOI: 10.1016/j.clinms.2019.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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8
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Meysman P, Saeys Y, Sabaghian E, Bittremieux W, Van de Peer Y, Goethals B, Laukens K. Mining the Enriched Subgraphs for Specific Vertices in a Biological Graph. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1496-1507. [PMID: 27295680 DOI: 10.1109/tcbb.2016.2576440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, we present a subgroup discovery method to find subgraphs in a graph that are associated with a given set of vertices. The association between a subgraph pattern and a set of vertices is defined by its significant enrichment based on a Bonferroni-corrected hypergeometric probability value. This interestingness measure requires a dedicated pruning procedure to limit the number of subgraph matches that must be calculated. The presented mining algorithm to find associated subgraph patterns in large graphs is therefore designed to efficiently traverse the search space. We demonstrate the operation of this method by applying it on three biological graph data sets and show that we can find associated subgraphs for a biologically relevant set of vertices and that the found subgraphs themselves are biologically interesting.
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9
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Glazko G, Zybailov B, Emmert-Streib F, Baranova A, Rahmatallah Y. Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study. PLoS One 2019; 14:e0221444. [PMID: 31437237 PMCID: PMC6705791 DOI: 10.1371/journal.pone.0221444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 08/06/2019] [Indexed: 01/10/2023] Open
Abstract
Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.
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Affiliation(s)
- Galina Glazko
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
| | - Boris Zybailov
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
| | - Frank Emmert-Streib
- Computational Medicine and Statistical Learning Laboratory, Tampere University of Technology, Korkeakoulunkatu, Tampere, Finland FI
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Manassas VA, United States of America
- Research Center for Medical Genetics, Moscow, Russia
| | - Yasir Rahmatallah
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States of America
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10
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Zhao L, Chi W, Cao H, Cui W, Meng W, Guo W, Wang B. Screening and clinical significance of tumor markers in head and neck squamous cell carcinoma through bioinformatics analysis. Mol Med Rep 2018; 19:143-154. [PMID: 30431092 PMCID: PMC6297764 DOI: 10.3892/mmr.2018.9639] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 10/17/2018] [Indexed: 12/26/2022] Open
Abstract
In order to identify potential diagnostic and prognostic biomarkers, and treatment targets for head and neck squamous cell carcinoma (HNSCC), the present study obtained the gene expression profiles in HNSCC through public data mining, and core genes were identified using a series of bioinformatics analysis methods and databases. A total of nine hub genes (SPP1, ITGA6, TMPRSS11D, MMP1, LAMC2, FAT1, ACTA1, SERPINE1 and CEACAM1) were identified to be significantly correlated with HNSCC. Furthermore, overall survival analysis demonstrated that the expression values of hub genes were associated with overall survival in HNSCC. Furthermore, certain of the identified genes, including, TMPRSS11D, ACTA1 and CEACAM1, have not been thoroughly investigated in HNSCC previously. Taken together, the nine hub genes obtained by screening in the present study may serve as potential tumor markers and important prognostic indicators for HNSCC.
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Affiliation(s)
- Lei Zhao
- Department of Otorhinolaryngology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
| | - Weiwei Chi
- Department of Otorhinolaryngology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei 050031, P.R. China
| | - Huan Cao
- Department of Otorhinolaryngology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
| | - Weina Cui
- Department of Otorhinolaryngology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
| | - Wenxia Meng
- Department of Otorhinolaryngology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
| | - Wei Guo
- Department of Laboratory of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050019, P.R. China
| | - Baoshan Wang
- Department of Otorhinolaryngology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
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11
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Norman KC, Moore BB, Arnold KB, O’Dwyer DN. Proteomics: Clinical and research applications in respiratory diseases. Respirology 2018; 23:993-1003. [PMID: 30105802 PMCID: PMC6234509 DOI: 10.1111/resp.13383] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 07/05/2018] [Accepted: 07/19/2018] [Indexed: 12/27/2022]
Abstract
The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue-specific expression of proteins and the varied post-translational modifications, splice variants and protein-protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.
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Affiliation(s)
- Katy C. Norman
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, USA
| | - Bethany B. Moore
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, USA
| | - Kelly B. Arnold
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, USA
| | - David N. O’Dwyer
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan Medical School, Ann Arbor, USA
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12
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CEA: Combination-based gene set functional enrichment analysis. Sci Rep 2018; 8:13085. [PMID: 30166636 PMCID: PMC6117355 DOI: 10.1038/s41598-018-31396-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 08/10/2018] [Indexed: 02/08/2023] Open
Abstract
Functional enrichment analysis is a fundamental and challenging task in bioinformatics. Most of the current enrichment analysis approaches individually evaluate functional terms and often output a list of enriched terms with high similarity and redundancy, which makes it difficult for downstream studies to extract the underlying biological interpretation. In this paper, we proposed a novel framework to assess the performance of combination-based enrichment analysis. Using this framework, we formulated the enrichment analysis as a multi-objective combinatorial optimization problem and developed the CEA (Combination-based Enrichment Analysis) method. CEA provides the whole landscape of term combinations; therefore, it is a good benchmark for evaluating the current state-of-the-art combination-based functional enrichment methods in a comprehensive manner. We tested the effectiveness of CEA on four published microarray datasets. Enriched functional terms identified by CEA not only involve crucial biological processes of related diseases, but also have much less redundancy and can serve as a preferable representation for the enriched terms found by traditional single-term-based methods. CEA has been implemented in the R package CopTea and is available at http://github.com/wulingyun/CopTea/.
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13
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In vitro assessment of hepatotoxicity by metabolomics: a review. Arch Toxicol 2018; 92:3007-3029. [DOI: 10.1007/s00204-018-2286-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 08/13/2018] [Indexed: 02/08/2023]
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14
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Meysman P, Titeca K, Eyckerman S, Tavernier J, Goethals B, Martens L, Valkenborg D, Laukens K. Protein complex analysis: From raw protein lists to protein interaction networks. MASS SPECTROMETRY REVIEWS 2017; 36:600-614. [PMID: 26709718 DOI: 10.1002/mas.21485] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Accepted: 11/17/2015] [Indexed: 06/05/2023]
Abstract
The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.
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Affiliation(s)
- Pieter Meysman
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - Kevin Titeca
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Sven Eyckerman
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Jan Tavernier
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Bart Goethals
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - Lennart Martens
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- IBioStat, Hasselt University, Hasselt, Belgium
- CFP-CeProMa, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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15
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Santana da Silva F, Jansen L, Freitas F, Schulz S. Ontological interpretation of biomedical database content. J Biomed Semantics 2017. [PMID: 28651575 PMCID: PMC5485580 DOI: 10.1186/s13326-017-0127-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Background Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. Methods By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). Results IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Conclusion Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models. Electronic supplementary material The online version of this article (doi:10.1186/s13326-017-0127-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Filipe Santana da Silva
- Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, 50.740-560, Recife, Brazil.,Núcleo de Telessaúde, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, 50670-420, Recife, Brazil
| | - Ludger Jansen
- Institut für Philosophie, Universität Rostock, D-18051, Rostock, Germany
| | - Fred Freitas
- Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, 50.740-560, Recife, Brazil
| | - Stefan Schulz
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria.
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16
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Dick JM. Chemical composition and the potential for proteomic transformation in cancer, hypoxia, and hyperosmotic stress. PeerJ 2017; 5:e3421. [PMID: 28603672 PMCID: PMC5463988 DOI: 10.7717/peerj.3421] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 05/16/2017] [Indexed: 12/19/2022] Open
Abstract
The changes of protein expression that are monitored in proteomic experiments are a type of biological transformation that also involves changes in chemical composition. Accompanying the myriad molecular-level interactions that underlie any proteomic transformation, there is an overall thermodynamic potential that is sensitive to microenvironmental conditions, including local oxidation and hydration potential. Here, up- and down-expressed proteins identified in 71 comparative proteomics studies were analyzed using the average oxidation state of carbon (ZC) and water demand per residue (\documentclass[12pt]{minimal}
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}{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O), calculated using elemental abundances and stoichiometric reactions to form proteins from basis species. Experimental lowering of oxygen availability (hypoxia) or water activity (hyperosmotic stress) generally results in decreased ZC or \documentclass[12pt]{minimal}
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}{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of up-expressed compared to down-expressed proteins. This correspondence of chemical composition with experimental conditions provides evidence for attraction of the proteomes to a low-energy state. An opposite compositional change, toward higher average oxidation or hydration state, is found for proteomic transformations in colorectal and pancreatic cancer, and in two experiments for adipose-derived stem cells. Calculations of chemical affinity were used to estimate the thermodynamic potentials for proteomic transformations as a function of fugacity of O2 and activity of H2O, which serve as scales of oxidation and hydration potential. Diagrams summarizing the relative potential for formation of up- and down-expressed proteins have predicted equipotential lines that cluster around particular values of oxygen fugacity and water activity for similar datasets. The changes in chemical composition of proteomes are likely linked with reactions among other cellular molecules. A redox balance calculation indicates that an increase in the lipid to protein ratio in cancer cells by 20% over hypoxic cells would generate a large enough electron sink for oxidation of the cancer proteomes. The datasets and computer code used here are made available in a new R package, canprot.
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17
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GFD-Net: A novel semantic similarity methodology for the analysis of gene networks. J Biomed Inform 2017; 68:71-82. [PMID: 28274758 DOI: 10.1016/j.jbi.2017.02.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 02/08/2017] [Accepted: 02/22/2017] [Indexed: 02/06/2023]
Abstract
Since the popularization of biological network inference methods, it has become crucial to create methods to validate the resulting models. Here we present GFD-Net, the first methodology that applies the concept of semantic similarity to gene network analysis. GFD-Net combines the concept of semantic similarity with the use of gene network topology to analyze the functional dissimilarity of gene networks based on Gene Ontology (GO). The main innovation of GFD-Net lies in the way that semantic similarity is used to analyze gene networks taking into account the network topology. GFD-Net selects a functionality for each gene (specified by a GO term), weights each edge according to the dissimilarity between the nodes at its ends and calculates a quantitative measure of the network functional dissimilarity, i.e. a quantitative value of the degree of dissimilarity between the connected genes. The robustness of GFD-Net as a gene network validation tool was demonstrated by performing a ROC analysis on several network repositories. Furthermore, a well-known network was analyzed showing that GFD-Net can also be used to infer knowledge. The relevance of GFD-Net becomes more evident in Section "GFD-Net applied to the study of human diseases" where an example of how GFD-Net can be applied to the study of human diseases is presented. GFD-Net is available as an open-source Cytoscape app which offers a user-friendly interface to configure and execute the algorithm as well as the ability to visualize and interact with the results(http://apps.cytoscape.org/apps/gfdnet).
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18
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Islam MT, Mohamedali A, Ahn SB, Nawar I, Baker MS, Ranganathan S. A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes. Methods Mol Biol 2017; 1549:163-176. [PMID: 27975291 DOI: 10.1007/978-1-4939-6740-7_13] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.
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Affiliation(s)
- Mohammad Tawhidul Islam
- Department of Chemistry and Biomolecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia
| | - Abidali Mohamedali
- Department of Chemistry and Biomolecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia.,Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Seong Beom Ahn
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Ishmam Nawar
- Department of Chemistry and Biomolecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia.
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19
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Giorgianni F, Beranova-Giorgianni S. Phosphoproteome Discovery in Human Biological Fluids. Proteomes 2016; 4:proteomes4040037. [PMID: 28248247 PMCID: PMC5260970 DOI: 10.3390/proteomes4040037] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 11/11/2016] [Accepted: 11/23/2016] [Indexed: 01/07/2023] Open
Abstract
Phosphorylation plays a critical role in regulating protein function and thus influences a vast spectrum of cellular processes. With the advent of modern bioanalytical technologies, examination of protein phosphorylation on a global scale has become one of the major research areas. Phosphoproteins are found in biological fluids and interrogation of the phosphoproteome in biological fluids presents an exciting opportunity for discoveries that hold great potential for novel mechanistic insights into protein function in health and disease, and for translation to improved diagnostic and therapeutic approaches for the clinical setting. This review focuses on phosphoproteome discovery in selected human biological fluids: serum/plasma, urine, cerebrospinal fluid, saliva, and bronchoalveolar lavage fluid. Bioanalytical workflows pertinent to phosphoproteomics of biological fluids are discussed with emphasis on mass spectrometry-based approaches, and summaries of studies on phosphoproteome discovery in major fluids are presented.
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Affiliation(s)
- Francesco Giorgianni
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Sarka Beranova-Giorgianni
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
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20
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Fasano M, Monti C, Alberio T. A systems biology-led insight into the role of the proteome in neurodegenerative diseases. Expert Rev Proteomics 2016; 13:845-55. [PMID: 27477319 DOI: 10.1080/14789450.2016.1219254] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases. AREAS COVERED Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.
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Affiliation(s)
- Mauro Fasano
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| | - Chiara Monti
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| | - Tiziana Alberio
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
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21
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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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22
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Sanchez-Lucas R, Mehta A, Valledor L, Cabello-Hurtado F, Romero-Rodrıguez MC, Simova-Stoilova L, Demir S, Rodriguez-de-Francisco LE, Maldonado-Alconada AM, Jorrin-Prieto AL, Jorrín-Novo JV. A year (2014-2015) of plants in Proteomics journal. Progress in wet and dry methodologies, moving from protein catalogs, and the view of classic plant biochemists. Proteomics 2016; 16:866-76. [PMID: 26621614 DOI: 10.1002/pmic.201500351] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 10/26/2015] [Accepted: 11/04/2015] [Indexed: 12/23/2022]
Abstract
The present review is an update of the previous one published in Proteomics 2015 Reviews special issue [Jorrin-Novo, J. V. et al., Proteomics 2015, 15, 1089-1112] covering the July 2014-2015 period. It has been written on the bases of the publications that appeared in Proteomics journal during that period and the most relevant ones that have been published in other high-impact journals. Methodological advances and the contribution of the field to the knowledge of plant biology processes and its translation to agroforestry and environmental sectors will be discussed. This review has been organized in four blocks, with a starting general introduction (literature survey) followed by sections focusing on the methodology (in vitro, in vivo, wet, and dry), proteomics integration with other approaches (systems biology and proteogenomics), biological information, and knowledge (cell communication, receptors, and signaling), ending with a brief mention of some other biological and translational topics to which proteomics has made some contribution.
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Affiliation(s)
- Rosa Sanchez-Lucas
- Agroforestry and Plant Biochemistry and Proteomics Research Group, Department of Biochemistry and Molecular Biology, University of Córdoba-CeiA3, Córdoba, Spain
| | - Angela Mehta
- Embrapa Recursos Genéticos e Biotecnologia (CENARGEN), Brasília, DF, Brazil
| | - Luis Valledor
- Department of Biology of Organisms and Systems (BOS), University of Oviedo, Oviedo, Spain
| | | | - M Cristina Romero-Rodrıguez
- Centro Multidisciplinario de Investigaciones Tecnológicas, and Departamento de Fitoquímica, Facultad de Ciencias Químicas, Universidad Nacional de Asunción, San Lorenzo, Paraguay
| | - Lyudmila Simova-Stoilova
- Plant Molecular Biology Department, Institute of Plant Physiology and Genetics, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Sekvan Demir
- Agroforestry and Plant Biochemistry and Proteomics Research Group, Department of Biochemistry and Molecular Biology, University of Córdoba-CeiA3, Córdoba, Spain
| | - Luis E Rodriguez-de-Francisco
- Agroforestry and Plant Biochemistry and Proteomics Research Group, Department of Biochemistry and Molecular Biology, University of Córdoba-CeiA3, Córdoba, Spain.,INTEC-Sto. Domingo, Santo Domingo, República Dominicana
| | - Ana M Maldonado-Alconada
- Agroforestry and Plant Biochemistry and Proteomics Research Group, Department of Biochemistry and Molecular Biology, University of Córdoba-CeiA3, Córdoba, Spain
| | - Ana L Jorrin-Prieto
- Agroforestry and Plant Biochemistry and Proteomics Research Group, Department of Biochemistry and Molecular Biology, University of Córdoba-CeiA3, Córdoba, Spain
| | - Jesus V Jorrín-Novo
- Agroforestry and Plant Biochemistry and Proteomics Research Group, Department of Biochemistry and Molecular Biology, University of Córdoba-CeiA3, Córdoba, Spain
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23
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Di Lena P, Martelli PL, Fariselli P, Casadio R. NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases. BMC Genomics 2015; 16 Suppl 8:S6. [PMID: 26110971 PMCID: PMC4480278 DOI: 10.1186/1471-2164-16-s8-s6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background Enrichment analysis is a widely applied procedure for shedding light on the molecular mechanisms and functions at the basis of phenotypes, for enlarging the dataset of possibly related genes/proteins and for helping interpretation and prioritization of newly determined variations. Several standard and Network-based enrichment methods are available. Both approaches rely on the annotations that characterize the genes/proteins included in the input set; network based ones also include in different ways physical and functional relationships among different genes or proteins that can be extracted from the available biological networks of interactions. Results Here we describe a novel procedure based on the extraction from the STRING interactome of sub-networks connecting proteins that share the same Gene Ontology(GO) terms for Biological Process (BP). Enrichment analysis is performed by mapping the protein set to be analyzed on the sub-networks, and then by collecting the corresponding annotations. We test the ability of our enrichment method in finding annotation terms disregarded by other enrichment methods available. We benchmarked 244 sets of proteins associated to different Mendelian diseases, according to the OMIM web resource. In 143 cases (58%), the network-based procedure extracts GO terms neglected by the standard method, and in 86 cases (35%), some of the newly enriched GO terms are not included in the set of annotations characterizing the input proteins. We present in detail six cases where our network-based enrichment provides an insight into the biological basis of the diseases, outperforming other freely available network-based methods. Conclusions Considering a set of proteins in the context of their interaction network can help in better defining their functions. Our novel method exploits the information contained in the STRING database for building the minimal connecting network containing all the proteins annotated with the same GO term. The enrichment procedure is performed considering the GO-specific network modules and, when tested on the OMIM-derived benchmark sets, it is able to extract enrichment terms neglected by other methods. Our procedure is effective even when the size of the input protein set is small, requiring at least two input proteins.
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