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Tiew PY, Meldrum OW, Chotirmall SH. Applying Next-Generation Sequencing and Multi-Omics in Chronic Obstructive Pulmonary Disease. Int J Mol Sci 2023; 24:ijms24032955. [PMID: 36769278 PMCID: PMC9918109 DOI: 10.3390/ijms24032955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Microbiomics have significantly advanced over the last decade, driven by the widespread availability of next-generation sequencing (NGS) and multi-omic technologies. Integration of NGS and multi-omic datasets allow for a holistic assessment of endophenotypes across a range of chronic respiratory disease states, including chronic obstructive pulmonary disease (COPD). Valuable insight has been attained into the nature, function, and significance of microbial communities in disease onset, progression, prognosis, and response to treatment in COPD. Moving beyond single-biome assessment, there now exists a growing literature on functional assessment and host-microbe interaction and, in particular, their contribution to disease progression, severity, and outcome. Identifying specific microbes and/or metabolic signatures associated with COPD can open novel avenues for therapeutic intervention and prognosis-related biomarkers. Despite the promise and potential of these approaches, the large amount of data generated by such technologies can be challenging to analyze and interpret, and currently, there remains a lack of standardized methods to address this. This review outlines the current use and proposes future avenues for the application of NGS and multi-omic technologies in the endophenotyping, prognostication, and treatment of COPD.
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Affiliation(s)
- Pei Yee Tiew
- Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore 169608, Singapore
- Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - Oliver W. Meldrum
- Lee Kong Chian School of Medicine, Nanyang Technological University, Clinical Sciences Building, 11 Mandalay Road, Singapore 308232, Singapore
| | - Sanjay H. Chotirmall
- Lee Kong Chian School of Medicine, Nanyang Technological University, Clinical Sciences Building, 11 Mandalay Road, Singapore 308232, Singapore
- Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Correspondence:
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2
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Odongo R, Demiroglu-Zergeroglu A, Çakır T. A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data. BMC Complement Med Ther 2021; 21:181. [PMID: 34193143 PMCID: PMC8244196 DOI: 10.1186/s12906-021-03340-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 06/02/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines. METHODS Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action. RESULTS The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks. CONCLUSION The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.
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Affiliation(s)
- Regan Odongo
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
- Department of Molecular Biology and Genetics, Gebze Technical University, Gebze, Kocaeli, Turkey
| | | | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
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3
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Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks. PLoS One 2021; 16:e0252901. [PMID: 34161324 PMCID: PMC8221501 DOI: 10.1371/journal.pone.0252901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 05/24/2021] [Indexed: 11/19/2022] Open
Abstract
Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.
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Ma X, Sun P, Gong M. An integrative framework of heterogeneous genomic data for cancer dynamic modules based on matrix decomposition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 19:305-316. [PMID: 32750874 DOI: 10.1109/tcbb.2020.3004808] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Cancer progression is dynamic, and tracking dynamic modules is promising for cancer diagnosis and therapy. Accumulated genomic data provide us an opportunity to investigate the underlying mechanisms of cancers. However, as far as we know, no algorithm has been designed for dynamic modules by integrating heterogeneous omics data. To address this issue, we propose an integrative framework for dynamic module detection based on regularized nonnegative matrix factorization method (DrNMF) by integrating the gene expression and protein interaction network. To remove the heterogeneity of genomic data, we divide the samples of expression profiles into groups to construct gene co-expression networks. To characterize the dynamics of modules, the temporal smoothness framework is adopted, in which the gene co-expression network at the previous stage and protein interaction network are incorporated into the objective function of DrNMF via regularization. The experimental results demonstrate that DrNMF is superior to state-of-the-art methods in terms of accuracy. For breast cancer data, the obtained dynamic modules are more enriched by the known pathways, and can be used to predict the stages of cancers and survival time of patients. The proposed model and algorithm provide an effective integrative analysis of heterogeneous genomic data for cancer progression.
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5
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Larmuseau M, Verbeke LPC, Marchal K. Associating expression and genomic data using co-occurrence measures. Biol Direct 2019; 14:10. [PMID: 31072345 PMCID: PMC6507230 DOI: 10.1186/s13062-019-0240-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/10/2019] [Indexed: 12/11/2022] Open
Abstract
Abstract Recent technological evolutions have led to an exponential increase in data in all the omics fields. It is expected that integration of these different data sources, will drastically enhance our knowledge of the biological mechanisms behind genomic diseases such as cancer. However, the integration of different omics data still remains a challenge. In this work we propose an intuitive workflow for the integrative analysis of expression, mutation and copy number data taken from the METABRIC study on breast cancer. First, we present evidence that the expression profile of many important breast cancer genes consists of two modes or ‘regimes’, which contain important clinical information. Then, we show how the co-occurrence of these expression regimes can be used as an association measure between genes and validate our findings on the TCGA-BRCA study. Finally, we demonstrate how these co-occurrence measures can also be applied to link expression regimes to genomic aberrations, providing a more complete, integrative view on breast cancer. As a case study, an integrative analysis of the identified MLPH-FOXA1 association is performed, illustrating that the obtained expression associations are intimately linked to the underlying genomic changes. Reviewers This article was reviewed by Dirk Walther, Francisco Garcia and Isabel Nepomuceno. Electronic supplementary material The online version of this article (10.1186/s13062-019-0240-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maarten Larmuseau
- Department of Information Technology, Ghent University - Imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Belgium
| | - Lieven P C Verbeke
- Department of Plant Biotechnology and Bioinformatics, Ghent University - Imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Belgium
| | - Kathleen Marchal
- Department of Plant Biotechnology and Bioinformatics, Ghent University - Imec, Technologiepark-Zwijnaarde 126, 9052, Ghent, Belgium.
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6
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A Boolean network control algorithm guided by forward dynamic programming. PLoS One 2019; 14:e0215449. [PMID: 31048917 PMCID: PMC6497256 DOI: 10.1371/journal.pone.0215449] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 04/02/2019] [Indexed: 11/19/2022] Open
Abstract
Control problem in a biological system is the problem of finding an interventional policy for changing the state of the biological system from an undesirable state, e.g. disease, into a desirable healthy state. Boolean networks are utilized as a mathematical model for gene regulatory networks. This paper provides an algorithm to solve the control problem in Boolean networks. The proposed algorithm is implemented and applied on two biological systems: T-cell receptor network and Drosophila melanogaster network. Results show that the proposed algorithm works faster in solving the control problem over these networks, while having similar accuracy, in comparison to previous exact methods. Source code and a simple web service of the proposed algorithm is available at http://goliaei.ir/net-control/www/.
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7
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Krishnan NM, Katoh H, Palve V, Pareek M, Sato R, Ishikawa S, Panda B. Functional genomics screen with pooled shRNA library and gene expression profiling with extracts of Azadirachta indica identify potential pathways for therapeutic targets in head and neck squamous cell carcinoma. PeerJ 2019; 7:e6464. [PMID: 30842898 PMCID: PMC6398373 DOI: 10.7717/peerj.6464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 01/16/2019] [Indexed: 01/20/2023] Open
Abstract
Tumor suppression by the extracts of Azadirachta indica (neem) works via anti-proliferation, cell cycle arrest, and apoptosis, demonstrated previously using cancer cell lines and live animal models. However, very little is known about the molecular targets and pathways that neem extracts and their associated compounds act through. Here, we address this using a genome-wide functional pooled shRNA screen on head and neck squamous cell carcinoma cell lines treated with crude neem leaf extracts, known for their anti-tumorigenic activity. We analyzed differences in global clonal sizes of the shRNA-infected cells cultured under no treatment and treatment with neem leaf extract conditions, assayed using next-generation sequencing. We found 225 genes affected the cancer cell growth in the shRNA-infected cells treated with neem extract. Pathway enrichment analyses of whole-genome gene expression data from cells temporally treated with neem extract revealed important roles played by the TGF-β pathway and HSF-1-related gene network. Our results indicate that neem extract affects various important molecular signaling pathways in head and neck cancer cells, some of which may be therapeutic targets for this devastating tumor.
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Affiliation(s)
- Neeraja M. Krishnan
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
- Ganit Labs Foundation, New Delhi, India
| | - Hiroto Katoh
- Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- JST, PRESTO, Saitama, Japan
| | - Vinayak Palve
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Manisha Pareek
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
| | - Reiko Sato
- Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shumpei Ishikawa
- Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Binay Panda
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
- Ganit Labs Foundation, New Delhi, India
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Liluashvili V, Kalayci S, Fluder E, Wilson M, Gabow A, Gümüs ZH. iCAVE: an open source tool for visualizing biomolecular networks in 3D, stereoscopic 3D and immersive 3D. Gigascience 2018; 6:1-13. [PMID: 28814063 PMCID: PMC5554349 DOI: 10.1093/gigascience/gix054] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 07/05/2017] [Indexed: 02/02/2023] Open
Abstract
Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field.
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Affiliation(s)
- Vaja Liluashvili
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Selim Kalayci
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eugene Fluder
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Manda Wilson
- Computational Biology Center, Memorial-Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Aaron Gabow
- Computational Biology Center, Memorial-Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Zeynep H Gümüs
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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9
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Perco P, Mayer G. Endogenous factors and mechanisms of renoprotection and renal repair. Eur J Clin Invest 2018; 48:e12914. [PMID: 29460289 DOI: 10.1111/eci.12914] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 02/14/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND An imbalance between renal damaging molecules and nephroprotective factors contributes to the development and progression of kidney diseases. Molecules with renoprotective properties might serve as biomarkers, drug targets as well as therapeutic options themselves. MATERIALS AND METHODS For this review, we generated a set of renoprotective factors based on GeneRIF (Gene Reference Into Function) information available at NCBI's PubMed. The final set of manually curated renoprotective factors was investigated with respect to tissue-specific expression, subcellular location distribution and involvement in biological processes using information from gene ontology as well as information from protein-protein interaction databases. We furthermore investigated the factors in the context of clinical trials of renal disease and diabetes. RESULTS One hundred and ninety-three factors could be retrieved from the set of GeneRIFs on nephroprotection and renal repair. A large number of factors were either secretory molecules or plasma membrane receptors. Next to the elevated expression in renal tissue, also higher expression in connective tissue and pancreas was observed. The proteins could be assigned to the broad functional categories of cell proliferation and signalling, inflammatory response, apoptosis, blood pressure regulation as well as cellular response to different kinds of insults such as hypoxia, heat or mechanical stimulus. Eight factors are studied in clinical trials with additional ones being targeted by compounds. CONCLUSIONS We have generated a set of renoprotective factors based on the literature information, which was functionally annotated and evaluated with respect to tested compounds in kidney disease and diabetes clinical trials.
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Affiliation(s)
- Paul Perco
- Department of Internal Medicine IV, Medical University of Innsbruck, Innsbruck, Austria
| | - Gert Mayer
- Department of Internal Medicine IV, Medical University of Innsbruck, Innsbruck, Austria
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10
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Li Y, Sahni N, Yi S. Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures. Oncotarget 2018; 7:78841-78849. [PMID: 27791983 PMCID: PMC5346681 DOI: 10.18632/oncotarget.12879] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 10/14/2016] [Indexed: 12/12/2022] Open
Abstract
Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.
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Affiliation(s)
- Yongsheng Li
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Nidhi Sahni
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.,Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Song Yi
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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11
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Korcsmaros T, Schneider MV, Superti-Furga G. Next generation of network medicine: interdisciplinary signaling approaches. Integr Biol (Camb) 2017; 9:97-108. [PMID: 28106223 DOI: 10.1039/c6ib00215c] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.
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Affiliation(s)
- Tamas Korcsmaros
- Earlham Institute, Norwich Research Park, Norwich, UK. and Gut Health and Food Safety Programme, Institute of Food Research, Norwich Research Park, Norwich, UK
| | | | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria and Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
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12
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Covell DG. A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib. PLoS One 2017; 12:e0181991. [PMID: 28792525 PMCID: PMC5549706 DOI: 10.1371/journal.pone.0181991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 07/10/2017] [Indexed: 12/28/2022] Open
Abstract
A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.
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Affiliation(s)
- David G. Covell
- Information Technology Branch, Developmental Therapeutics Program, National Cancer Institute, Frederick, MD, United States of America
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13
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Murrugarra D, Veliz-Cuba A, Aguilar B, Laubenbacher R. Identification of control targets in Boolean molecular network models via computational algebra. BMC SYSTEMS BIOLOGY 2016; 10:94. [PMID: 27662842 PMCID: PMC5035508 DOI: 10.1186/s12918-016-0332-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 08/23/2016] [Indexed: 11/10/2022]
Abstract
BACKGROUND Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. RESULTS This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . CONCLUSIONS This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.
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Affiliation(s)
- David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, 40506-0027, KY, USA.
| | - Alan Veliz-Cuba
- Department of Mathematics, University of Dayton, Dayton, 45469, OH, USA
| | - Boris Aguilar
- Institute for Systems Biology, Seattle, 98109-5263, WA, USA
| | - Reinhard Laubenbacher
- Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, 06030-6033, CT, USA.,Jackson Laboratory for Genomic Medicine, Farmington, 06030, CT, USA
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14
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Choi Y, Park SK, Ahn KJ, Cho H, Kim TH, Yoon HK, Lee YH. Being Overweight or Obese Increases the Risk of Progression in Triple-Negative Breast Cancer after Surgical Resection. J Korean Med Sci 2016; 31:886-91. [PMID: 27247497 PMCID: PMC4853667 DOI: 10.3346/jkms.2016.31.6.886] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/21/2016] [Indexed: 12/31/2022] Open
Abstract
This study aimed to evaluate the association between body mass index (BMI) and progression in triple-negative breast cancer (TNBC). We retrospectively reviewed the medical records of 50 patients with TNBC who underwent breast-conserving surgery or mastectomy between 2007 and 2014. All patients were classified according to BMI (median 23.5 kg/m(2), range 17.2-31.6 kg/m(2)): 31 patients (62%) were classified as being overweight or obese (BMI ≥ 23 kg/m(2)) and 19 patients (38%) were classified as having a normal body weight (BMI < 23 kg/m(2)). The median follow-up for patients was 31.1 months (range, 6.7-101.9 months). Progression occurred in 7 patients (14%), including 5 ipsilateral breast tumor recurrences, 2 regional lymph node metastases, and 5 distant metastases. Progression was significantly correlated with overweight or obese patients (P = 0.035), while none of the normal weight patients showed progression. The 3-year disease-free survival (DFS) and overall survival (OS) rates were 85.0% and 87.7%, respectively. DFS was significantly reduced in overweight or obese patients compared to that in normal weight patients (P = 0.035). However, OS was not significantly compromised by being overweight or obese (P = 0.134). In conclusion, being overweight or obese negatively affects DFS in TNBC patients.
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Affiliation(s)
- Yunseon Choi
- Department of Radiation Oncology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
- Department of Medicine, Yonsei University College of Medicine, Yonsei Graduate School, Seoul, Korea
| | - Sung Kwang Park
- Department of Radiation Oncology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Ki Jung Ahn
- Department of Radiation Oncology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Heunglae Cho
- Department of Radiation Oncology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Tae Hyun Kim
- Department of Surgery, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Hye Kyoung Yoon
- Department of Pathology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Yun-Han Lee
- Department of Medicine, Yonsei University College of Medicine, Yonsei Graduate School, Seoul, Korea
- Department of Molecular Medicine, College of Medicine, Keimyung University, Daegu, Korea
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15
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Zhou K, Pedersen HK, Dawed AY, Pearson ER. Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery. Nat Rev Endocrinol 2016; 12:337-46. [PMID: 27062931 DOI: 10.1038/nrendo.2016.51] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Genomic studies have greatly advanced our understanding of the multifactorial aetiology of type 2 diabetes mellitus (T2DM) as well as the multiple subtypes of monogenic diabetes mellitus. In this Review, we discuss the existing pharmacogenetic evidence in both monogenic diabetes mellitus and T2DM. We highlight mechanistic insights from the study of adverse effects and the efficacy of antidiabetic drugs. The identification of extreme sulfonylurea sensitivity in patients with diabetes mellitus owing to heterozygous mutations in HNF1A represents a clear example of how pharmacogenetics can direct patient care. However, pharmacogenomic studies of response to antidiabetic drugs in T2DM has yet to be translated into clinical practice, although some moderate genetic effects have now been described that merit follow-up in trials in which patients are selected according to genotype. We also discuss how future pharmacogenomic findings could provide insights into treatment response in diabetes mellitus that, in addition to other areas of human genetics, facilitates drug discovery and drug development for T2DM.
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Affiliation(s)
- Kaixin Zhou
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Helle Krogh Pedersen
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Adem Y Dawed
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Ewan R Pearson
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
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16
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Darrason M. Mechanistic and topological explanations in medicine: the case of medical genetics and network medicine. SYNTHESE 2015; 195:147-173. [PMID: 32214509 PMCID: PMC7089272 DOI: 10.1007/s11229-015-0983-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Accepted: 11/28/2015] [Indexed: 06/10/2023]
Abstract
Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction. In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics. Network medicine is a recent discipline that relies on the analysis of various disease networks (including disease-gene networks) in order to find organizing principles in disease explanation. My aim is to show how topological explanations in network medicine can help solving the conceptual issues that pure mechanistic explanations of the genetics of disease are currently facing, namely the crisis of the concept of genetic disease, the progressive geneticization of diseases and the dissolution of the distinction between monogenic and polygenic diseases. However, I will also argue that topological explanations should not be considered as independent and radically different from mechanistic explanations for at least two reasons. First, in network medicine, topological explanations depend on and use mechanistic information. Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations. Finally, I will insist on the specific contribution of topological explanations in medicine: they push us to develop an explanation of disease in general, instead of focusing on single explanations of individual diseases. This last point may have major consequences for biomedical research.
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Affiliation(s)
- Marie Darrason
- Institut d’Histoire et de Philosophie des Sciences et des Techniques (IHPST - CNRS / Université Paris 1 Panthéon Sorbonne / ENS), 13 rue du Four, 75006 Paris, France
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17
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Murrugarra D, Dimitrova ES. Molecular network control through boolean canalization. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2015; 2015:9. [PMID: 26752585 PMCID: PMC4699631 DOI: 10.1186/s13637-015-0029-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 10/22/2015] [Indexed: 01/12/2023]
Abstract
Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boolean function, has been extensively studied in the context of network modeling where each layer of canalization adds a degree of stability in the dynamics of the network. Recently, dynamic network control approaches have been used for the design of new therapeutic interventions and for other applications such as stem cell reprogramming. This work studies the role of canalization in the control of Boolean molecular networks. It provides a method for identifying the potential edges to control in the wiring diagram of a network for avoiding undesirable state transitions. The method is based on identifying appropriate input-output combinations on undesirable transitions that can be modified using the edges in the wiring diagram of the network. Moreover, a method for estimating the number of changed transitions in the state space of the system as a result of an edge deletion in the wiring diagram is presented. The control methods of this paper were applied to a mutated cell-cycle model and to a p53-mdm2 model to identify potential control targets.
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Affiliation(s)
- David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, 40506-0027 KY USA
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18
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Tobin PH, Richards DH, Callender RA, Wilson CJ. Protein engineering: a new frontier for biological therapeutics. Curr Drug Metab 2015; 15:743-56. [PMID: 25495737 DOI: 10.2174/1389200216666141208151524] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Revised: 11/27/2014] [Accepted: 12/07/2014] [Indexed: 12/14/2022]
Abstract
Protein engineering holds the potential to transform the metabolic drug landscape through the development of smart, stimulusresponsive drug systems. Protein therapeutics are a rapidly expanding segment of Food and Drug Administration approved drugs that will improve clinical outcomes over the long run. Engineering of protein therapeutics is still in its infancy, but recent general advances in protein engineering capabilities are being leveraged to yield improved control over both pharmacokinetics and pharmacodynamics. Stimulus- responsive protein therapeutics are drugs which have been designed to be metabolized under targeted conditions. Protein engineering is being utilized to develop tailored smart therapeutics with biochemical logic. This review focuses on applications of targeted drug neutralization, stimulus-responsive engineered protein prodrugs, and emerging multicomponent smart drug systems (e.g., antibody-drug conjugates, responsive engineered zymogens, prospective biochemical logic smart drug systems, drug buffers, and network medicine applications).
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Affiliation(s)
| | | | | | - Corey J Wilson
- Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06520-8286, USA.
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19
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Zheng H, Fridkin M, Youdim M. New approaches to treating Alzheimer's disease. PERSPECTIVES IN MEDICINAL CHEMISTRY 2015; 7:1-8. [PMID: 25733799 PMCID: PMC4327405 DOI: 10.4137/pmc.s13210] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Revised: 12/30/2014] [Accepted: 01/01/2015] [Indexed: 01/14/2023]
Abstract
To date, no truly efficacious drugs for Alzheimer’s disease (AD) have been developed; moreover, all new anti-AD drugs developed since 2003 have failed. To succeed where previous ones have failed in drug development, new approaches for AD therapy are needed. Here we discuss the potential application of network medicine as a new approach to AD treatment. Unlike traditional approaches focused on a single target/pathway, network medicine targets and restores disease-disrupted networks through simultaneous modulation of numerous proteins (targets)/pathways involved in AD pathogenesis. We consider several drug candidates under development for AD therapy, including Keap1–Nrf2 regulators, endogenous neurogenic agents, and hypoxia-inducible factor 1 (HIF-1) activators. These drug candidates are multi-target ligands with the potential to further develop as network medicines, since they act as master regulators to initiate a broad range of cellular defense mechanisms/cytoprotective genes that exert their efficacy in a holistic way. We also explore their diverse mechanisms of action and potential disease-modifying effects, which may have profound implications for drug discovery.
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Affiliation(s)
- Hailin Zheng
- Department of Medicinal Chemistry, Intra-cellular Therapies Inc., New York, NY, USA
| | - Mati Fridkin
- Department of Organic Chemistry, Weizmann Institute of Science, Rehovot, Israel
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20
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Havrylov S, Park M. MS/MS-based strategies for proteomic profiling of invasive cell structures. Proteomics 2014; 15:272-86. [PMID: 25303514 DOI: 10.1002/pmic.201400220] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Revised: 08/19/2014] [Accepted: 10/01/2014] [Indexed: 12/29/2022]
Abstract
Acquired capacity of cancer cells to penetrate through the extracellular matrix of surrounding tissues is a prerequisite for tumour metastatic spread - the main source of cancer-associated mortality. Through combined efforts of many research groups, we are beginning to understand that the ability of cells to invade through the extracellular matrix is a multi-faceted phenomenon supported by variety of specialised protrusive cellular structures, primarily pseudopodia, invadopodia and podosomes. Additionally, secreted extracellular vesicles are being increasingly recognised as important mediators of invasive cell phenotypes and therefore may be considered bona fide invasive cell structures. Dissection of the molecular makings underlying biogenesis and function of all of these structures is crucial to identify novel targets for specific anti-metastatic therapies. Rapid advances and growing accessibility of MS/MS-based protein identification made this family of techniques a suitable and appropriate choice for proteomic profiling of invasive cell structures. In this review, we provide a summary of current progress in the characterisation of protein composition and topology of protein interaction networks of pseudopodia, invadopodia, podosomes and extracellular vesicles, as well as outline challenges and perspectives of the field.
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Affiliation(s)
- Serhiy Havrylov
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada; Department of Medicine, McGill University, Montreal, QC, Canada
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21
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Affiliation(s)
- Donal P McLornan
- From King's College Hospital NHS Foundation Trust, London (D.P.M., G.J.M.); and Moffitt Cancer Center, Tampa, FL (A.L.)
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22
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Masuda M, Yamada T. Signaling pathway profiling by reverse-phase protein array for personalized cancer medicine. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2014; 1854:651-7. [PMID: 25448010 DOI: 10.1016/j.bbapap.2014.10.014] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Revised: 10/01/2014] [Accepted: 10/20/2014] [Indexed: 11/28/2022]
Abstract
Deregulation of intracellular signaling through accumulation of genetic alterations is a hallmark of cancer. In the past few decades, concerted and systematic efforts have been made to identify key genetic alterations and to develop therapeutic agents targeting active signaling molecules. However, the efficacy of molecular therapeutics often varies among individuals, and precise mapping of active molecules in individual patients is now considered an essential for therapy optimization. Reverse-phase protein array or microarray (RPPA or RPPM) is an emerging antibody-based highly quantitative proteomic technology, especially suitable for profiling of expression and modification of signaling proteins in low abundance. Because the supply of clinical materials is often limited, RPPA technology is highly advantageous for clinical proteomics in view of its high sensitivity as well as accurate quantification. RPPA has now begun to be incorporated into various clinical trials employing molecular-targeted therapeutics. In this article we review and discuss the application of RPPA technology in the fields of basic, preclinical, and clinical research. The RPPA Global Workshop was recently launched to accelerate the exchange of rapidly expanding knowledge of this fascinating technology among academic laboratories and industries worldwide. This article is part of a Special Issue entitled: Medical Proteomics.
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Affiliation(s)
- Mari Masuda
- Division of Chemotherapy and Clinical Research, Translational Research Group, National Cancer Center Research Institute, Tokyo, Japan
| | - Tesshi Yamada
- Division of Chemotherapy and Clinical Research, Translational Research Group, National Cancer Center Research Institute, Tokyo, Japan.
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23
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Lianos GD, Bali CD, Glantzounis GK, Katsios C, Roukos DH. BMI and lymph node ratio may predict clinical outcomes of gastric cancer. Future Oncol 2014; 10:249-55. [PMID: 24490611 DOI: 10.2217/fon.13.188] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
AIM BMI and the lymph node (LN) ratio can affect short- and long-term outcomes of patients with gastric cancer. PATIENTS & METHODS This study includes 104 consecutive patients with gastric adenocarcinoma who underwent curative gastrectomy divided in two groups: overweight group (group A) and normal weight group (group B). RESULTS We found that 53.4% of our patients were overweight (group A). The overall rate of postoperative complications was 16.3%, while mortality was 1%. Statistical analyses revealed that postoperative morbidity was significantly higher in group A (p < 0.05). Long-term survival was significantly higher in group B. Cox regression showed a statistically significant correlation between higher BMI and poor long-term survival after curative gastrectomy. Multivariate analysis has identified age and the LN ratios as independent prognostic factors of survival. CONCLUSION In this retrospective analysis, BMI and LN ratio were independently associated with survival in patients with gastric cancer. Further studies are needed to confirm our findings.
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Affiliation(s)
- Georgios D Lianos
- Department of Surgery, University Hospital of Ioannina, St. Niarchou Av, Ioannina 451 10, Greece
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24
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Cornish AJ, Markowetz F. SANTA: quantifying the functional content of molecular networks. PLoS Comput Biol 2014; 10:e1003808. [PMID: 25210953 PMCID: PMC4161294 DOI: 10.1371/journal.pcbi.1003808] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 07/15/2014] [Indexed: 12/31/2022] Open
Abstract
Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.
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Affiliation(s)
- Alex J. Cornish
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom
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25
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Teschendorff AE, Sollich P, Kuehn R. Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 2014; 67:282-93. [PMID: 24675401 DOI: 10.1016/j.ymeth.2014.03.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2013] [Revised: 01/03/2014] [Accepted: 03/13/2014] [Indexed: 12/27/2022] Open
Abstract
A key challenge in systems biology is the elucidation of the underlying principles, or fundamental laws, which determine the cellular phenotype. Understanding how these fundamental principles are altered in diseases like cancer is important for translating basic scientific knowledge into clinical advances. While significant progress is being made, with the identification of novel drug targets and treatments by means of systems biological methods, our fundamental systems level understanding of why certain treatments succeed and others fail is still lacking. We here advocate a novel methodological framework for systems analysis and interpretation of molecular omic data, which is based on statistical mechanical principles. Specifically, we propose the notion of cellular signalling entropy (or uncertainty), as a novel means of analysing and interpreting omic data, and more fundamentally, as a means of elucidating systems-level principles underlying basic biology and disease. We describe the power of signalling entropy to discriminate cells according to differentiation potential and cancer status. We further argue the case for an empirical cellular entropy-robustness correlation theorem and demonstrate its existence in cancer cell line drug sensitivity data. Specifically, we find that high signalling entropy correlates with drug resistance and further describe how entropy could be used to identify the achilles heels of cancer cells. In summary, signalling entropy is a deep and powerful concept, based on rigorous statistical mechanical principles, which, with improved data quality and coverage, will allow a much deeper understanding of the systems biological principles underlying normal and disease physiology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, 320 Yue Yang Road, Shanghai 200031, China; Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London WC1E 6BT, UK.
| | - Peter Sollich
- Department of Mathematics, King's College London, London WC2R 2LS, UK
| | - Reimer Kuehn
- Department of Mathematics, King's College London, London WC2R 2LS, UK
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26
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Roukos DH. Cancer heterogeneity and signaling network-based drug target. Pharmacogenomics 2014; 14:1243-6. [PMID: 23930670 DOI: 10.2217/pgs.13.113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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27
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Wang J, Peng X, Peng W, Wu FX. Dynamic protein interaction network construction and applications. Proteomics 2014; 14:338-52. [DOI: 10.1002/pmic.201300257] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2013] [Revised: 10/23/2013] [Accepted: 11/27/2013] [Indexed: 12/22/2022]
Affiliation(s)
- Jianxin Wang
- School of Information Science and Engineering; Central South University; Changsha P. R. China
| | - Xiaoqing Peng
- School of Information Science and Engineering; Central South University; Changsha P. R. China
| | - Wei Peng
- School of Information Science and Engineering; Central South University; Changsha P. R. China
| | - Fang-Xiang Wu
- Department of Mechanical Engineering and Division of Biomedical Engineering; University of Saskatchewan; Saskatoon Canada
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28
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Abstract
Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.
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Affiliation(s)
- Mete Civelek
- 1] Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles. [2] Department of Human Genetics, University of California, Los Angeles. [3] Department of Medicine, A2-237 Center for Health Sciences, University of California, Los Angeles, California 90095-1679, USA
| | - Aldons J Lusis
- 1] Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles. [2] Department of Human Genetics, University of California, Los Angeles. [3] Department of Medicine, A2-237 Center for Health Sciences, University of California, Los Angeles, California 90095-1679, USA
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29
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Roukos DH, Baltogiannis GG, Baltogiannis G. Mapping inherited and somatic variation in regulatory DNA: new roadmap for common disease clinical discoveries. Expert Rev Mol Diagn 2013; 13:519-22. [PMID: 23895121 DOI: 10.1586/14737159.2013.811908] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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30
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Seo H, Kim W, Lee J, Youn B. Network-based approaches for anticancer therapy (Review). Int J Oncol 2013; 43:1737-44. [PMID: 24085339 DOI: 10.3892/ijo.2013.2114] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Accepted: 08/23/2013] [Indexed: 12/16/2022] Open
Abstract
Cancer is a complex disease resulting from alterations of multiple signaling networks. Cancer networks have been identified as scale-free networks and may contain a functionally important key player called a hub that is linked to a large number of interactors. Since a hub can serve as a biological marker in a given network, targeting the hub could be an effective strategy for enhancing the efficacy of cancer treatment. Chemotherapies and radiotherapies are generally used to treat tumors not amenable to resection, and target single or multiple molecules associated with hubs. However, these therapies may unexpectedly induce the resistance of cancer cells to drugs and radiation. Cancer cells can overcome therapy-induced damage via the activation of back-up signaling pathways and flexible modulation of affected networks. These activities are considered to be the main reasons for chemoresistance and radioresistance, and subsequent failure of cancer therapies. Much effort is required to identify the key molecules that control the modulation of signaling networks in response to drugs and radiation. Network-based therapy that affects network flexibility, including rewired network structures and hub molecules in these networks, could minimize the occurrence of side-effects and be a promising strategy for enhancing the therapeutic efficacy of cancer treatments. This review is intended to offer an overview of current research efforts including ones focused on cancer-associated complex networks, their modulation in response to cancer therapy, and further strategies targeting networks that may improve cancer treatment efficacy.
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Affiliation(s)
- Hyunjeong Seo
- Department of Biological Sciences, College of Natural Sciences, Pusan National University, Busan 609-735, Republic of Korea
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31
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Katsouras CS, Baltogiannis GG, Naka KK, Roukos DH, Michalis LK. Decoding coronary artery disease: somatic mosaicism and genomics for personal and population risk prediction. Biomark Med 2013; 7:189-92. [PMID: 23547811 DOI: 10.2217/bmm.13.4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
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32
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Lemons D, Maurya MR, Subramaniam S, Mercola M. Developing microRNA screening as a functional genomics tool for disease research. Front Physiol 2013; 4:223. [PMID: 23986717 PMCID: PMC3753477 DOI: 10.3389/fphys.2013.00223] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Accepted: 08/02/2013] [Indexed: 02/04/2023] Open
Abstract
Originally discovered as regulators of developmental timing in C. elegans, microRNAs (miRNAs) have emerged as modulators of nearly every cellular process, from normal development to pathogenesis. With the advent of whole genome libraries of miRNA mimics suitable for high throughput screening, it is possible to comprehensively evaluate the function of each member of the miRNAome in cell-based assays. Since the relatively few microRNAs in the genome are thought to directly regulate a large portion of the proteome, miRNAome screening, coupled with the identification of the regulated proteins, might be a powerful new approach to gaining insight into complex biological processes.
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Affiliation(s)
- Derek Lemons
- Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA ; Muscle Development and Regeneration Program, Sanford-Burnham Medical Research Institute La Jolla, CA, USA
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33
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Liu Z, Wang Y, Xue Y. Phosphoproteomics-based network medicine. FEBS J 2013; 280:5696-704. [PMID: 23751130 DOI: 10.1111/febs.12380] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2013] [Revised: 05/10/2013] [Accepted: 06/05/2013] [Indexed: 11/29/2022]
Abstract
One of the major tasks of phosphoproteomics is providing potential biomarkers for either diagnosis or drug targets in medical applications. Because most complex diseases are due to the actions of multiple genes/proteins, the identification of complex phospho-signatures containing multiple phosphorylation events within phosphoproteomics-based networks generates more efficient and robust biomarkers than a single, differentially phosphorylated substrate or site. Here, we briefly summarize the current efforts and progress in this newly emerging field of phosphoproteomics-based network medicine by reviewing the computational (re)construction of phosphorylation-mediated signaling networks from unannotated phosphoproteomic data, the discovery of robust network phospho-signatures and the application of these signatures for classifying cancers and predicting drug responses. The challenges as well as the potential advantages are evaluated and discussed. Although the current techniques are at present far from mature, we believe that such a systematic approach as we describe can generate more useful and robust biomarkers for biomedical usage, even at the current stage of development.
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Affiliation(s)
- Zexian Liu
- Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
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34
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 511] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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35
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Roukos DH, Katsouras CS, Baltogiannis GG, Naka KK, Michalis LK. Network-based drugs: promise and clinical challenges in cardiovascular disease. Expert Rev Proteomics 2013; 10:119-22. [DOI: 10.1586/epr.13.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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36
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Creixell P, Schoof EM, Erler JT, Linding R. Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 2013; 30:842-8. [PMID: 22965061 DOI: 10.1038/nbt.2345] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies.
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Affiliation(s)
- Pau Creixell
- Cellular Signal Integration Group (C-SIG), Center for Biological Sequence Analysis (CBS), Department of Systems Biology, Technical University of Denmark (DTU), Lyngby, Denmark
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37
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Medina MÁ. Systems biology for molecular life sciences and its impact in biomedicine. Cell Mol Life Sci 2013; 70:1035-53. [PMID: 22903296 PMCID: PMC11113420 DOI: 10.1007/s00018-012-1109-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2012] [Revised: 07/24/2012] [Accepted: 07/25/2012] [Indexed: 01/02/2023]
Abstract
Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.
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Affiliation(s)
- Miguel Ángel Medina
- Department of Molecular Biology and Biochemistry, University of Málaga, Malaga, Spain.
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38
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Experimental and computational methods for the analysis and modeling of signaling networks. N Biotechnol 2013; 30:327-32. [DOI: 10.1016/j.nbt.2012.11.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Accepted: 11/05/2012] [Indexed: 01/30/2023]
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Affiliation(s)
- Nagasuma Chandra
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India ,
| | - Jyothi Padiadpu
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India
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40
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Rappsilber J. Cross-linking/mass spectrometry as a new field and the proteomics information mountain of tomorrow. Expert Rev Proteomics 2012; 9:485-7. [PMID: 23194264 PMCID: PMC3926187 DOI: 10.1586/epr.12.44] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
The European Proteomics Association (EuPA) 2012 Scientific Congress 'New Horizons and Applications for Proteomics', hosted by the British Society for Proteome Research (BSPR) Glasgow, Scotland, UK, 12 July 2012 Cross-linking/mass spectrometry ended decades of method developments and entered the era of applications at this year's European Proteomics Association meeting. The train has started moving, with successful applications of this tool by multiple pioneering laboratories addressing biological and structural problems. Proteomics, on the other side, sees ever increasing data volumes, leading to questions as to how to store the data mountain publically, use it and convert it into testable hypotheses. The European Proteomics Association meeting has been complementary to the American Society for Mass Spectrometry meeting in many ways, also thanks to its more manageable size and the vision of the organizers in inviting some of Europe's best emerging minds.
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Affiliation(s)
- Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, University of Edinburgh, King's Buildings, Edinburgh EH9 3JR, UK.
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