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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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2
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Mansueto G, Fusco G, Colonna G. A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms. Biomolecules 2024; 14:541. [PMID: 38785948 PMCID: PMC11118181 DOI: 10.3390/biom14050541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/01/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.
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Affiliation(s)
- Gelsomina Mansueto
- Dipartimento di Scienze Mediche e Chirurgiche Avanzate, Università della Campania, L. Vanvitelli, 80138 Naples, Italy;
| | - Giovanna Fusco
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, 80055 Portici, Italy;
| | - Giovanni Colonna
- Medical Informatics AOU, Università della Campania, L. Vanvitelli, 80138 Naples, Italy
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3
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Shortt E, Hackett CG, Stadler RV, Kent RS, Herneisen AL, Ward GE, Lourido S. CDPK2A and CDPK1 form a signaling module upstream of Toxoplasma motility. mBio 2023; 14:e0135823. [PMID: 37610220 PMCID: PMC10653799 DOI: 10.1128/mbio.01358-23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 06/17/2023] [Indexed: 08/24/2023] Open
Abstract
IMPORTANCE This work uncovers interactions between various signaling pathways that govern Toxoplasma gondii egress. Specifically, we compare the function of three canonical calcium-dependent protein kinases (CDPKs) using chemical-genetic and conditional-depletion approaches. We describe the function of a previously uncharacterized CDPK, CDPK2A, in the Toxoplasma lytic cycle, demonstrating that it contributes to parasite fitness through regulation of microneme discharge, gliding motility, and egress from infected host cells. Comparison of analog-sensitive kinase alleles and conditionally depleted alleles uncovered epistasis between CDPK2A and CDPK1, implying a partial functional redundancy. Understanding the topology of signaling pathways underlying key events in the parasite life cycle can aid in efforts targeting kinases for anti-parasitic therapies.
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Affiliation(s)
- Emily Shortt
- Whitehead Institute, Cambridge, Massachusetts, USA
| | | | - Rachel V. Stadler
- Department of Microbiology and Molecular Genetics, University of Vermont Larner College of Medicine, Burlington, Vermont, USA
| | - Robyn S. Kent
- Department of Microbiology and Molecular Genetics, University of Vermont Larner College of Medicine, Burlington, Vermont, USA
| | - Alice L. Herneisen
- Whitehead Institute, Cambridge, Massachusetts, USA
- Biology Department, MIT, Cambridge, Massachusetts, USA
| | - Gary E. Ward
- Department of Microbiology and Molecular Genetics, University of Vermont Larner College of Medicine, Burlington, Vermont, USA
| | - Sebastian Lourido
- Whitehead Institute, Cambridge, Massachusetts, USA
- Biology Department, MIT, Cambridge, Massachusetts, USA
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4
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Rodina A, Xu C, Digwal CS, Joshi S, Patel Y, Santhaseela AR, Bay S, Merugu S, Alam A, Yan P, Yang C, Roychowdhury T, Panchal P, Shrestha L, Kang Y, Sharma S, Almodovar J, Corben A, Alpaugh ML, Modi S, Guzman ML, Fei T, Taldone T, Ginsberg SD, Erdjument-Bromage H, Neubert TA, Manova-Todorova K, Tsou MFB, Young JC, Wang T, Chiosis G. Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation. Nat Commun 2023; 14:3742. [PMID: 37353488 PMCID: PMC10290137 DOI: 10.1038/s41467-023-39241-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 06/05/2023] [Indexed: 06/25/2023] Open
Abstract
Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based 'omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.
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Affiliation(s)
- Anna Rodina
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chao Xu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chander S Digwal
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Suhasini Joshi
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yogita Patel
- Department of Biochemistry, Groupe de Recherche Axé sur la Structure des Protéines, McGill University, Montreal, QC, H3G 0B1, Canada
| | - Anand R Santhaseela
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sadik Bay
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Swathi Merugu
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Aftab Alam
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Pengrong Yan
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Chenghua Yang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Palak Panchal
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Liza Shrestha
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yanlong Kang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sahil Sharma
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Justina Almodovar
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Adriana Corben
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Maimonides Medical Center, Brooklyn, NY, USA
| | - Mary L Alpaugh
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Rowan University, Glassboro, NJ, USA
| | - Shanu Modi
- Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Monica L Guzman
- Department of Medicine, Division of Hematology Oncology, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Teng Fei
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Tony Taldone
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen D Ginsberg
- Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA
| | - Hediye Erdjument-Bromage
- Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Thomas A Neubert
- Department of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Katia Manova-Todorova
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Meng-Fu Bryan Tsou
- Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Jason C Young
- Department of Biochemistry, Groupe de Recherche Axé sur la Structure des Protéines, McGill University, Montreal, QC, H3G 0B1, Canada
| | - Tai Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
- Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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Martins ILF, Almeida FVDS, Souza KPD, Brito FCFD, Rodrigues GD, Scaramello CBV. Reviewing Atrial Fibrillation Pathophysiology from a Network Medicine Perspective: The Relevance of Structural Remodeling, Inflammation, and the Immune System. Life (Basel) 2023; 13:1364. [PMID: 37374146 DOI: 10.3390/life13061364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/03/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The numerous gaps concerning the knowledge of its mechanism make improving clinical management difficult. As omics technologies allow more comprehensive insight into biology and disease at a molecular level, bioinformatics encompasses valuable tools for studying systems biology, as well as combining and modeling multi-omics data and networks. Network medicine is a subarea of network biology where disease traits are considered perturbations within the interactome. With this approach, potential disease drivers can be revealed, and the effect of drugs, novel or repurposed, used alone or in combination, may be studied. Thus, this work aims to review AF pathology from a network medicine perspective, helping researchers to comprehend the disease more deeply. Essential concepts involved in network medicine are highlighted, and specific research applying network medicine to study AF is discussed. Additionally, data integration through literature mining and bioinformatics tools, with network building, is exemplified. Together, all of the data show the substantial role of structural remodeling, the immune system, and inflammation in this disease etiology. Despite this, there are still gaps to be filled about AF.
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Affiliation(s)
- Ivis Levy Fernandes Martins
- Research Nucleus on Plasticity, Epidemiology and In-Silico Studies (NUPPEESI), Fluminense Federal University, Niteroi 24020-141, Rio de Janeiro, Brazil
| | - Flávia Valéria Dos Santos Almeida
- Research Nucleus on Plasticity, Epidemiology and In-Silico Studies (NUPPEESI), Fluminense Federal University, Niteroi 24020-141, Rio de Janeiro, Brazil
| | - Karyne Pollo de Souza
- Research Nucleus on Plasticity, Epidemiology and In-Silico Studies (NUPPEESI), Fluminense Federal University, Niteroi 24020-141, Rio de Janeiro, Brazil
| | | | - Gabriel Dias Rodrigues
- Experimental and Applied Physiology Lab (LAFEA), Fluminense Federal University, Niteroi 24020-141, Rio de Janeiro, Brazil
- Department of Clinical Sciences and Community Health, University of Milan, 20126 Milan, Milan, Italy
| | - Christianne Bretas Vieira Scaramello
- Research Nucleus on Plasticity, Epidemiology and In-Silico Studies (NUPPEESI), Fluminense Federal University, Niteroi 24020-141, Rio de Janeiro, Brazil
- Experimental Pharmacology Lab (LAFE), Fluminense Federal University, Niteroi 24020-141, Rio de Janeiro, Brazil
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Wimalagunasekara SS, Weeraman JWJK, Tirimanne S, Fernando PC. Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa). J Genet Eng Biotechnol 2023; 21:69. [PMID: 37246172 DOI: 10.1186/s43141-023-00515-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/06/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND The root system is vital to plant growth and survival. Therefore, genetic improvement of the root system is beneficial for developing stress-tolerant and improved plant varieties. This requires the identification of proteins that significantly contribute to root development. Analyzing protein-protein interaction (PPI) networks is vastly beneficial in studying developmental phenotypes, such as root development, because a phenotype is an outcome of several interacting proteins. PPI networks can be analyzed to identify modules and get a global understanding of important proteins governing the phenotypes. PPI network analysis for root development in rice has not been performed before and has the potential to yield new findings to improve stress tolerance. RESULTS Here, the network module for root development was extracted from the global Oryza sativa PPI network retrieved from the STRING database. Novel protein candidates were predicted, and hub proteins and sub-modules were identified from the extracted module. The validation of the predictions yielded 75 novel candidate proteins, 6 sub-modules, 20 intramodular hubs, and 2 intermodular hubs. CONCLUSIONS These results show how the PPI network module is organized for root development and can be used for future wet-lab studies for producing improved rice varieties.
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Affiliation(s)
| | - Janith W J K Weeraman
- Department of Plant Sciences, Faculty of Science, University of Colombo, Colombo, Sri Lanka.
| | - Shamala Tirimanne
- Department of Plant Sciences, Faculty of Science, University of Colombo, Colombo, Sri Lanka
| | - Pasan C Fernando
- Department of Plant Sciences, Faculty of Science, University of Colombo, Colombo, Sri Lanka
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Wu I, Wang X. A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer. Sci Rep 2023; 13:8223. [PMID: 37217594 DOI: 10.1038/s41598-023-35165-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/13/2023] [Indexed: 05/24/2023] Open
Abstract
Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.
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Affiliation(s)
- Isabella Wu
- Choate Rosemary Hall, Wallingford, 06492, USA.
| | - Xin Wang
- Electrical Engineering, Stony Brook University, Stony Brook, 11790, USA
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8
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Kuepfer L, Fuellen G, Stahnke T. Quantitative systems pharmacology of the eye: Tools and data for ocular QSP. CPT Pharmacometrics Syst Pharmacol 2023; 12:288-299. [PMID: 36708082 PMCID: PMC10014063 DOI: 10.1002/psp4.12918] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/21/2022] [Accepted: 01/02/2023] [Indexed: 01/29/2023] Open
Abstract
Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.
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Affiliation(s)
- Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Aging Research (IBIMA), Rostock University Medical Center, Rostock, Germany
| | - Thomas Stahnke
- Institute for ImplantTechnology and Biomaterials e.V., Rostock, Germany.,Department of Ophthalmology, Rostock University Medical Center, Rostock, Germany
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Santorelli L, Caterino M, Costanzo M. Dynamic Interactomics by Cross-Linking Mass Spectrometry: Mapping the Daily Cell Life in Postgenomic Era. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:633-649. [PMID: 36445175 DOI: 10.1089/omi.2022.0137] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
The majority of processes that occur in daily cell life are modulated by hundreds to thousands of dynamic protein-protein interactions (PPI). The resulting protein complexes constitute a tangled network that, with its continuous remodeling, builds up highly organized functional units. Thus, defining the dynamic interactome of one or more proteins allows determining the full range of biological activities these proteins are capable of. This conceptual approach is poised to gain further traction and significance in the current postgenomic era wherein the treatment of severe diseases needs to be tackled at both genomic and PPI levels. This also holds true for COVID-19, a multisystemic disease affecting biological networks across the biological hierarchy from genome to proteome to metabolome. In this overarching context and the current historical moment of the COVID-19 pandemic where systems biology increasingly comes to the fore, cross-linking mass spectrometry (XL-MS) has become highly relevant, emerging as a powerful tool for PPI discovery and characterization. This expert review highlights the advanced XL-MS approaches that provide in vivo insights into the three-dimensional protein complexes, overcoming the static nature of common interactomics data and embracing the dynamics of the cell proteome landscape. Many XL-MS applications based on the use of diverse cross-linkers, MS detection methods, and predictive bioinformatic tools for single proteins or proteome-wide interactions were shown. We conclude with a future outlook on XL-MS applications in the field of structural proteomics and ways to sustain the remarkable flexibility of XL-MS for dynamic interactomics and structural studies in systems biology and planetary health.
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Affiliation(s)
- Lucia Santorelli
- Department of Oncology and Hematology-Oncology, University of Milano, Milan, Italy.,IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Marianna Caterino
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy.,CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Michele Costanzo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy.,CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
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10
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Barbosa-Silva A, Magalhães M, da Silva GF, da Silva FAB, Carneiro FRG, Carels N. A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers. Cancers (Basel) 2022; 14:2325. [PMID: 35565454 PMCID: PMC9103663 DOI: 10.3390/cancers14092325] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/04/2022] [Accepted: 03/12/2022] [Indexed: 02/05/2023] Open
Abstract
The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.
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Affiliation(s)
- Adriano Barbosa-Silva
- Center for Medical Statistics, Informatics and Intelligent Systems, Institute for Artificial Intelligence, Medical University of Vienna, 1090 Vienna, Austria;
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London E14NS, UK
- ITTM S.A.—Information Technology for Translational Medicine, Esch-sur-Alzette, 4354 Luxembourg, Luxembourg
| | - Milena Magalhães
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil; (M.M.); (G.F.d.S.)
| | - Gilberto Ferreira da Silva
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil; (M.M.); (G.F.d.S.)
| | - Fabricio Alves Barbosa da Silva
- Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil;
| | - Flávia Raquel Gonçalves Carneiro
- Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil
- Laboratório Interdisciplinar de Pesquisas Médicas, Instituto Oswaldo Cruz, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil
- Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 20231050, Brazil
| | - Nicolas Carels
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil; (M.M.); (G.F.d.S.)
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11
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Yadav AK, Shukla R, Singh TR. Topological parameters, patterns, and motifs in biological networks. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00012-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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12
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Stivala A, Lomi A. Testing biological network motif significance with exponential random graph models. APPLIED NETWORK SCIENCE 2021; 6:91. [PMID: 34841042 PMCID: PMC8608783 DOI: 10.1007/s41109-021-00434-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
UNLABELLED Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-021-00434-y.
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Affiliation(s)
- Alex Stivala
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| | - Alessandro Lomi
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
- The University of Exeter Business School, Rennes Drive, Exeter, EX4 4PU UK
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13
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Zhuang Y, Hobbs BD, Hersh CP, Kechris K. Identifying miRNA-mRNA Networks Associated With COPD Phenotypes. Front Genet 2021; 12:748356. [PMID: 34777474 PMCID: PMC8581181 DOI: 10.3389/fgene.2021.748356] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/27/2021] [Indexed: 11/21/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA-mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA-mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA-mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA-mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.
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Affiliation(s)
- Yonghua Zhuang
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Brian D Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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14
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Joshi DM, Patel J, Bhatt H. Robust adaptation of PKC ζ-IRS1 insulin signaling pathways through integral feedback control. Biomed Phys Eng Express 2021; 7. [PMID: 34315137 DOI: 10.1088/2057-1976/ac182e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/27/2021] [Indexed: 11/11/2022]
Abstract
Insulin signaling pathways in muscle tissue play a major role in maintaining glucose homeostasis. Dysregulation in these pathways results in the onset of serious metabolic disorders like type 2 diabetes. Robustness is an essential characteristic of insulin signaling pathways that ensures reliable signal transduction in the presence of perturbations as a result of several feedback mechanisms. Integral control, according to control engineering, provides reliable setpoint tracking and disturbance rejection. The presence of negative feedback and integrating process is crucial for biological processes to achieve integral control. The existence of an integral controller leads to the rejection of perturbations which resulted in the robust regulation of biochemical entities within acceptable levels. In the presentin silicoresearch work, the presence of integral control in the protein kinase Cζ- insulin receptor substrate-1 (PKCζ-IRS1) pathway is identified, verified mathematically and model is simulated in Cell Designer. The data is exported to Minitab software and robustness analysis is carried out statistically using the Mann-Whitney test. The p-value of the results obtained with given parameters perturbed by ±1% is greater than the significance level of 0.05 (0.2132 for 1% error in k7(rate constant of IRS1 phosphorylation), 0.2096 for -1% error in k7, 0.9037 for both ±1% error in insulin and 0.9037 for ±1% error in k1(association rate constant of the first molecule of insulin to bind the insulin receptor), indicated that our hypothesis is proved The results satisfactorily indicate that even when perturbations are present, glucose homeostasis in muscle tissue is robust due to the presence of integral regulation in the PKCζ-IRS1 insulin signaling pathways. In this paper, we have analysed the findings from the framework of robust control theory, which has allowed us to examine that how PKCζ-IRS1 insulin signaling pathways produces desired output in presence of perturbations.
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Affiliation(s)
- Darshna M Joshi
- Department of Instrumentation and Control, Government Polytechnic Ahmedabad, Ahmedabad 380015, Gujarat, India.,Department of Instrumentation and Control, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India
| | - Jignesh Patel
- Department of Instrumentation and Control, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India
| | - Hardik Bhatt
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad 382481, Gujarat, India
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15
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Abrahamsson DP, Wang A, Jiang T, Wang M, Siddharth A, Morello-Frosch R, Park JS, Sirota M, Woodruff TJ. A Comprehensive Non-targeted Analysis Study of the Prenatal Exposome. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:10542-10557. [PMID: 34260856 PMCID: PMC8338910 DOI: 10.1021/acs.est.1c01010] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Recent technological advances in mass spectrometry have enabled us to screen biological samples for a very broad spectrum of chemical compounds allowing us to more comprehensively characterize the human exposome in critical periods of development. The goal of this study was three-fold: (1) to analyze 590 matched maternal and cord blood samples (total 295 pairs) using non-targeted analysis (NTA); (2) to examine the differences in chemical abundance between maternal and cord blood samples; and (3) to examine the associations between exogenous chemicals and endogenous metabolites. We analyzed all samples with high-resolution mass spectrometry using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in both positive and negative electrospray ionization modes (ESI+ and ESI-) and in soft ionization (MS) and fragmentation (MS/MS) modes for prioritized features. We confirmed 19 unique compounds with analytical standards, we tentatively identified 73 compounds with MS/MS spectra matching, and we annotated 98 compounds using an annotation algorithm. We observed 103 significant associations in maternal and 128 in cord samples between compounds annotated as endogenous and compounds annotated as exogenous. An example of these relationships was an association between three poly and perfluoroalkyl substances (PFASs) and endogenous fatty acids in both the maternal and cord samples indicating potential interactions between PFASs and fatty acid regulating proteins.
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Affiliation(s)
- Dimitri Panagopoulos Abrahamsson
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, 94143, California, United States
| | - Aolin Wang
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, 94143, California, United States
| | - Ting Jiang
- California Environmental Protection Agency, Department of Toxic Substances Control, Environmental Chemistry Laboratory, Berkeley, 94710, California, United States
| | - Miaomiao Wang
- California Environmental Protection Agency, Department of Toxic Substances Control, Environmental Chemistry Laboratory, Berkeley, 94710, California, United States
| | - Adi Siddharth
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, 94143, California, United States
| | - Rachel Morello-Frosch
- Department of Environmental Science, Policy and Management and School of Public Health, University of California Berkeley, Berkeley, 94720, California, United States
| | - June-Soo Park
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, 94143, California, United States
- California Environmental Protection Agency, Department of Toxic Substances Control, Environmental Chemistry Laboratory, Berkeley, 94710, California, United States
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, 94158, California, United States
- Department of Pediatrics, University of California San Francisco, San Francisco, 94158, California, United States
| | - Tracey J. Woodruff
- Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, 94143, California, United States
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16
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Figueiredo RQ, Raschka T, Kodamullil AT, Hofmann-Apitius M, Mubeen S, Domingo-Fernández D. Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms. Nucleic Acids Res 2021; 49:7939-7953. [PMID: 34197603 PMCID: PMC8373148 DOI: 10.1093/nar/gkab556] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/17/2021] [Accepted: 06/11/2021] [Indexed: 12/17/2022] Open
Abstract
We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.
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Affiliation(s)
- Rebeca Queiroz Figueiredo
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany.,Fraunhofer Center for Machine Learning, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala, India
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany.,Fraunhofer Center for Machine Learning, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Fraunhofer Center for Machine Learning, Germany.,Enveda Biosciences, Boulder, CO 80301, USA
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17
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Abstract
Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.
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18
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Ekkers DM, Branco dos Santos F, Mallon CA, Bruggeman F, van Doorn GS. The omnistat: A flexible continuous-culture system for prolonged experimental evolution. Methods Ecol Evol 2020; 11:932-942. [PMID: 32999708 PMCID: PMC7508058 DOI: 10.1111/2041-210x.13403] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 04/20/2020] [Indexed: 11/29/2022]
Abstract
Microbial evolution experiments provide a powerful tool to unravel the molecular basis of adaptive evolution but their outcomes can be difficult to interpret, unless the selective forces that are applied during the experiment are carefully controlled. In this respect, experimental evolution in continuous cultures provides advantages over commonly used sequential batch-culture protocols because continuous cultures allow for more accurate control over the induced selective environment. However, commercial continuous-culture systems are large and expensive, while available DIY continuous-culture systems are not versatile enough to allow for multiple sensors and rigorous stirring.We present a modular continuous-culture system that adopts the commonly used GL45 glass laboratory bottle as a bioreactor vessel. Our design offers three advantages: first, it is equipped with a large head plate, fitting two sensors and seven input/output ports, enabling the customization of the system for many running modes (chemostat, auxostat, etc.). Second, the bioreactor is small (25-250 ml), which makes it feasible to run many replicates in parallel. Third, bioreactor modules can be coupled by uni- or bi-directional flows to induce spatiotemporal variation in selection. These features result in a particularly flexible culturing platform that facilitates the investigation of a broad range of evolutionary and ecological questions.To illustrate the versatility of our culturing system, we outline two evolution experiments that impose a temporally or spatially variable regime of selection. The first experiment illustrates how controlled temporal variation in resource availability can be utilized to select for anticipatory switching. The second experiment illustrates a spatially structured morbidostat setup that is designed to probe epistatic interactions between adaptive mutations. Furthermore, we demonstrate how sensor data can be used to stabilize selection pressures or track evolutionary adaptation.Evolution experiments in which populations are exposed to controlled spatiotemporal variation, are essential to gain insight into the process of adaptation and the mechanisms that constrain evolution. Continuous-culture systems, like the one presented here, offer control over key environmental parameters and establish a well-defined regime of selection. As such, they create the opportunity to expose evolutionary constraints in the form of phenotypic trade-offs, contributing to a mechanistic understanding of adaptive evolution.
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Affiliation(s)
- David M. Ekkers
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
| | - Filipe Branco dos Santos
- Molecular Microbial Physiology GroupFaculty of ScienceSwammerdam Institute for Life SciencesUniversity of AmsterdamAmsterdamThe Netherlands
- Systems Bioinformatics/Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)/Netherlands Institute for Systems BiologyVU UniversityAmsterdamThe Netherlands
| | - Cyrus A. Mallon
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
| | - Frank Bruggeman
- Systems Bioinformatics/Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)/Netherlands Institute for Systems BiologyVU UniversityAmsterdamThe Netherlands
| | - G. Sander van Doorn
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
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19
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Small-world networks of prognostic genes associated with lung adenocarcinoma development. Genomics 2020; 112:4078-4088. [PMID: 32659327 DOI: 10.1016/j.ygeno.2020.07.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/26/2020] [Accepted: 07/07/2020] [Indexed: 11/23/2022]
Abstract
The present study investigates the role of network topology in lung adenocarcinoma (LUAD) development. Analysis of sex- and stage-specific whole-genome expression data revealed that co-expressed and highly connected prognostic genes common to all cancer stages form a small-world network in each stage of LUAD. These small-world networks are present within stage-specific scale-free networks, conserved across the cancer stages, and linked to cancer-specific events. The presence of small-world networks across the cancer stages presents a synchronized system in the disordered environment of cancer, resulting in the evolution of malignancy. Our study reported that these small-world networks are resilient to random and systematic attacks, indicating the least opportunity to introduce perturbations by drugs as a therapeutic intervention. We concluded that highly clustered small-world networks could be controlled through transcriptional modulation for the improved treatment of LUAD.
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20
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Conforte AJ, Alves L, Coelho FC, Carels N, da Silva FAB. Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks. Front Genet 2020; 11:314. [PMID: 32318098 PMCID: PMC7154169 DOI: 10.3389/fgene.2020.00314] [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: 12/19/2019] [Accepted: 03/16/2020] [Indexed: 12/26/2022] Open
Abstract
Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.
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Affiliation(s)
- Alessandra Jordano Conforte
- Laboratory of Biological Systems Modeling, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.,Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Leon Alves
- Applied Math School, Getúlio Vargas Foundation, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Nicolas Carels
- Laboratory of Biological Systems Modeling, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fabrício Alves Barbosa da Silva
- Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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21
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Mastej E, Gillenwater L, Zhuang Y, Pratte KA, Bowler RP, Kechris K. Identifying Protein-metabolite Networks Associated with COPD Phenotypes. Metabolites 2020; 10:metabo10040124. [PMID: 32218378 PMCID: PMC7241079 DOI: 10.3390/metabo10040124] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/06/2020] [Accepted: 03/23/2020] [Indexed: 02/02/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a disease in which airflow obstruction in the lung makes it difficult for patients to breathe. Although COPD occurs predominantly in smokers, there are still deficits in our understanding of the additional risk factors in smokers. To gain a deeper understanding of the COPD molecular signatures, we used Sparse Multiple Canonical Correlation Network (SmCCNet), a recently developed tool that uses sparse multiple canonical correlation analysis, to integrate proteomic and metabolomic data from the blood of 1008 participants of the COPDGene study to identify novel protein-metabolite networks associated with lung function and emphysema. Our aim was to integrate -omic data through SmCCNet to build interpretable networks that could assist in the discovery of novel biomarkers that may have been overlooked in alternative biomarker discovery methods. We found a protein-metabolite network consisting of 13 proteins and 7 metabolites which had a -0.34 correlation (p-value = 2.5 × 10-28) to lung function. We also found a network of 13 proteins and 10 metabolites that had a -0.27 correlation (p-value = 2.6 × 10-17) to percent emphysema. Protein-metabolite networks can provide additional information on the progression of COPD that complements single biomarker or single -omic analyses.
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Affiliation(s)
- Emily Mastej
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Correspondence:
| | | | - Yonghua Zhuang
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Russell P. Bowler
- National Jewish Health, Denver, CO 80206, USA (K.A.P.)
- School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Katerina Kechris
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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22
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Verma Y, Yadav A, Katara P. Mining of cancer core-genes and their protein interactome using expression profiling based PPI network approach. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2019.100583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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23
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Franzese N, Groce A, Murali TM, Ritz A. Hypergraph-based connectivity measures for signaling pathway topologies. PLoS Comput Biol 2019; 15:e1007384. [PMID: 31652258 PMCID: PMC6834280 DOI: 10.1371/journal.pcbi.1007384] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 11/06/2019] [Accepted: 09/09/2019] [Indexed: 12/12/2022] Open
Abstract
Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules “connected” in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway’s downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals pairs of pathways with statistically significant influence. We present two such case studies, and we describe the specific reactions that contribute to the large influence score. Finally, we investigate the ability for connectivity measures to capture functional relationships among proteins, and use the evidence channels in the STRING database as a benchmark dataset. STRING interactions whose proteins are B-connected in Reactome have statistically significantly higher scores than interactions connected in the bipartite graph representation. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis. Signaling pathways describe how cells respond to external signals through molecular interactions. As we gain a deeper understanding of these signaling reactions, it is important to understand how molecules may influence downstream responses and how pathways may affect each other. As the amount of information in signaling pathway databases continues to grow, we have the opportunity to analyze properties about pathway structure. We pose an intuitive question about signaling pathways: when are two molecules “connected” in a pathway? This answer varies dramatically based on the assumptions we make about how reactions link molecules. Here, examine four approaches for modeling the structural topology of signaling pathways, and present methods to quantify whether two molecules are “connected” in a pathway database. We find that existing approaches are either too permissive (molecules are connected to many others) or restrictive (molecules are connected to a handful of others), and we present a new measure that offers a continuum between these two extremes. We then expand our question to ask when an entire signaling pathway is “downstream” of another pathway, and show two case studies from the Reactome pathway database that uncovers pathway influence. Finally, we show that the strict notion of connectivity can capture functional relationships among proteins using an independent benchmark dataset. Our approach to quantify connectivity in pathways considers a biologically-motivated definition of connectivity, laying the foundation for more sophisticated analyses that leverage the detailed information in pathway databases.
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Affiliation(s)
- Nicholas Franzese
- Biology Department, Reed College, Portland, Oregon, United States of America
- Computer Science Department, Reed College, Portland, Oregon, United States of America
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Adam Groce
- Computer Science Department, Reed College, Portland, Oregon, United States of America
| | - T. M. Murali
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
- ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Anna Ritz
- Biology Department, Reed College, Portland, Oregon, United States of America
- * E-mail:
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24
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Conforte AJ, Tuszynski JA, da Silva FAB, Carels N. Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine. Front Genet 2019; 10:930. [PMID: 31695721 PMCID: PMC6816034 DOI: 10.3389/fgene.2019.00930] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 09/05/2019] [Indexed: 12/28/2022] Open
Abstract
Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.
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Affiliation(s)
- Alessandra J Conforte
- Laboratory of Biological Systems Modeling, Center of Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.,Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Jack Adam Tuszynski
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.,Department of Physics, University of Alberta, Edmonton, AB, Canada.,DIMEAS, Politecnico di Torino, Turin, Italy
| | - Fabricio Alves Barbosa da Silva
- Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Nicolas Carels
- Laboratory of Biological Systems Modeling, Center of Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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25
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Kim EY, Ashlock D, Yoon SH. Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks. BMC Bioinformatics 2019; 20:328. [PMID: 31195955 PMCID: PMC6567475 DOI: 10.1186/s12859-019-2897-z] [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: 04/30/2019] [Accepted: 05/13/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Detection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes' importance in a network. In topological analyses on metabolic networks, various centrality metrics have been mostly applied to metabolite-centric graphs. However, centrality metrics including those not depending on high connections are largely unexplored for directed reaction-centric graphs. RESULTS We applied directed versions of centrality metrics to directed reaction-centric graphs of microbial metabolic networks. To investigate the local role of a node, we developed a novel metric, cascade number, considering how many nodes are closed off from information flow when a particular node is removed. High modularity and scale-freeness were found in the directed reaction-centric graphs and betweenness centrality tended to belong to densely connected modules. Cascade number and bridging centrality identified cascade subnetworks controlling local information flow and irreplaceable bridging nodes between functional modules, respectively. Reactions highly ranked with bridging centrality and cascade number tended to be essential, compared to reactions that other central metrics detected. CONCLUSIONS We demonstrate that cascade number and bridging centrality are useful to identify key reactions controlling local information flow in directed reaction-centric graphs of microbial metabolic networks. Knowledge about the local flow connectivity and connections between local modules will contribute to understand how metabolic pathways are assembled.
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Affiliation(s)
- Eun-Youn Kim
- School of Basic Sciences, Hanbat National University, Daejeon, 34158, Republic of Korea
| | - Daniel Ashlock
- Department of Mathematics and Statistics, the University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Sung Ho Yoon
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, 05029, Republic of Korea.
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26
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Das S, Bhuyan R, Bagchi A, Saha T. Network analysis of hyphae forming proteins in Candida albicans identifies important proteins responsible for pathovirulence in the organism. Heliyon 2019; 5:e01916. [PMID: 31338453 PMCID: PMC6580234 DOI: 10.1016/j.heliyon.2019.e01916] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 05/20/2019] [Accepted: 06/03/2019] [Indexed: 12/21/2022] Open
Abstract
Candida albicans causes two types of major infections in humans: superficial infections, such as skin and mucosal infection, and life-threatening systemic infections, like airway and catheter-related blood stream infections. It is a polymorphic fungus with two distinct forms (yeast and hyphal) and the morphological plasticity is strongly associated with many disease causing proteins. In this study, 137 hyphae associated proteins from Candida albicans (C. albicans) were collected from different sources to create a Protein-Protein Interaction (PPI) network. Out of these, we identified 18 hub proteins (Hog1, Hsp90, Cyr1, Cdc28, Pkc1, Cla4, Cdc42, Tpk1, Act1, Pbs2, Bem1, Tpk2, Ras1, Cdc24, Rim101, Cdc11, Cdc10 and Cln3) that were the most important ones in hyphae development. Ontology and functional enrichment analysis of these proteins could categorize these hyphae associated proteins into groups like signal transduction, kinase activity, biofilm formation, filamentous growth, MAPK signaling etc. Functional annotation analysis of these proteins showed that the protein kinase activity to be essential for hyphae formation in Candida. Additionally, most of the proteins from the network were predicted to be localized on cell surface or periphery, suggesting them as the main protagonists in inducing infections within the host. The complex hyphae formation phenomenon of C. albicans is an attractive target for exploitation to develop new antifungals and anti-virulence strategies to combat C. albicans infections. We further tried to characterize few of the most crucial proteins, especially the kinases by their sequence and structural prospects. Therefore, through this article an attempt to understand the hyphae forming protein network analysis has been made to unravel and elucidate the complex pathogenesis processes with the principal aim of systems biological research involving novel Bioinformatics strategies to combat fungal infections.
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Affiliation(s)
- Sanjib Das
- Department of Molecular Biology & Biotechnology, University of Kalyani, West Bengal, 741235, India
| | - Rajabrata Bhuyan
- Department of Biochemistry & Biophysics, University of Kalyani, West Bengal, 741235, India
| | - Angshuman Bagchi
- Department of Biochemistry & Biophysics, University of Kalyani, West Bengal, 741235, India
| | - Tanima Saha
- Department of Molecular Biology & Biotechnology, University of Kalyani, West Bengal, 741235, India
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27
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Chamberlin SR, Blucher A, Wu G, Shinto L, Choonoo G, Kulesz-Martin M, McWeeney S. Natural Product Target Network Reveals Potential for Cancer Combination Therapies. Front Pharmacol 2019; 10:557. [PMID: 31214023 PMCID: PMC6555193 DOI: 10.3389/fphar.2019.00557] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 05/03/2019] [Indexed: 12/20/2022] Open
Abstract
A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.
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Affiliation(s)
- Steven R Chamberlin
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States
| | - Aurora Blucher
- OHSU Knight Cancer Institute, Portland, OR, United States
| | - Guanming Wu
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States.,Oregon Clinical and Translational Research Institute, Portland, OR, United States
| | - Lynne Shinto
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Gabrielle Choonoo
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States
| | - Molly Kulesz-Martin
- OHSU Knight Cancer Institute, Portland, OR, United States.,Departments of Dermatology and Cell, Developmental and Cancer Biology, Oregon Health and Sciences University, Portland, OR, United States
| | - Shannon McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Portland, OR, United States.,OHSU Knight Cancer Institute, Portland, OR, United States.,Oregon Clinical and Translational Research Institute, Portland, OR, United States
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28
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Lammers M, Kraaijeveld K, Mariën J, Ellers J. Gene expression changes associated with the evolutionary loss of a metabolic trait: lack of lipogenesis in parasitoids. BMC Genomics 2019; 20:309. [PMID: 31014246 PMCID: PMC6480896 DOI: 10.1186/s12864-019-5673-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 04/08/2019] [Indexed: 12/24/2022] Open
Abstract
Background Trait loss is a pervasive phenomenon in evolution, yet the underlying molecular causes have been identified in only a handful of cases. Most of these cases involve loss-of-function mutations in one or more trait-specific genes. However, in parasitoid insects the evolutionary loss of a metabolic trait is not associated with gene decay. Parasitoids have lost the ability to convert dietary sugars into fatty acids. Earlier research suggests that lack of lipogenesis in the parasitoid wasp Nasonia vitripennis is caused by changes in gene regulation. Results We compared transcriptomic responses to sugar-feeding in the non-lipogenic parasitoid species Nasonia vitripennis and the lipogenic Drosophila melanogaster. Both species adjusted their metabolism within 4 hours after sugar-feeding, but there were sharp differences between the expression profiles of the two species, especially in the carbohydrate and lipid metabolic pathways. Several genes coding for key enzymes in acetyl-CoA metabolism, such as malonyl-CoA decarboxylase (mcd) and HMG-CoA synthase (hmgs) differed in expression between the two species. Their combined action likely blocks lipogenesis in the parasitoid species. Network-based analysis showed connectivity of genes to be negatively correlated to the fold change of gene expression. Furthermore, genes involved in the fatty acid metabolic pathway were more connected than the set of genes of all metabolic pathways combined. Conclusion High connectivity of lipogenesis genes is indicative of pleiotropic effects and could explain the absence of gene degradation. We conclude that modification of expression levels of only a few little-connected genes, such as mcd, is sufficient to enable complete loss of lipogenesis in N. vitripennis. Electronic supplementary material The online version of this article (10.1186/s12864-019-5673-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mark Lammers
- Department of Ecological Sciences, Section Animal Ecology, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.
| | - Ken Kraaijeveld
- Department of Ecological Sciences, Section Animal Ecology, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - Janine Mariën
- Department of Ecological Sciences, Section Animal Ecology, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
| | - Jacintha Ellers
- Department of Ecological Sciences, Section Animal Ecology, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands
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29
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Liang Y, Kelemen A. Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications. Brief Bioinform 2019; 19:1051-1068. [PMID: 28430854 DOI: 10.1093/bib/bbx036] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Indexed: 12/23/2022] Open
Abstract
Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.
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Affiliation(s)
- Yulan Liang
- Department of Family and Community Health, University of Maryland, Baltimore, MD, USA
| | - Arpad Kelemen
- Department of Family and Community Health, University of Maryland, Baltimore, MD, USA
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30
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Ansariola M, Megraw M, Koslicki D. IndeCut evaluates performance of network motif discovery algorithms. Bioinformatics 2019; 34:1514-1521. [PMID: 29236975 DOI: 10.1093/bioinformatics/btx798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 12/08/2017] [Indexed: 12/24/2022] Open
Abstract
Motivation Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs. Results In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options. Availability and implementation The open source software package is available at https://github.com/megrawlab/IndeCut. Contact megrawm@science.oregonstate.edu or david.koslicki@math.oregonstate.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mitra Ansariola
- Center for Genome Research and Biocomputing.,Department of Botany and Plant Pathology
| | - Molly Megraw
- Center for Genome Research and Biocomputing.,Department of Botany and Plant Pathology.,Department of Computer Science
| | - David Koslicki
- Center for Genome Research and Biocomputing.,Department of Mathematics, Oregon State University, Corvallis, OR 97331, USA
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31
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Dikicioglu D, Nightingale DJH, Wood V, Lilley KS, Oliver SG. Transcriptional regulation of the genes involved in protein metabolism and processing in Saccharomyces cerevisiae. FEMS Yeast Res 2019; 19:5315759. [PMID: 30753445 DOI: 10.1093/femsyr/foz014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 02/08/2019] [Indexed: 12/17/2022] Open
Abstract
Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nuclear-enriched fraction of non-stressed yeast cells, suggesting that the transcriptional regulation of protein metabolism and processing in yeast is primarily concerned with maintaining cellular robustness in the face of threat by either internal or external stressors.
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Affiliation(s)
- Duygu Dikicioglu
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.,Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
| | - Daniel J H Nightingale
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Cambridge Centre for Proteomics, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA UK
| | - Valerie Wood
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA UK
| | - Kathryn S Lilley
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Cambridge Centre for Proteomics, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA UK
| | - Stephen G Oliver
- Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.,Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1GA UK
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32
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Zhang Z, Chng KR, Lingadahalli S, Chen Z, Liu MH, Do HH, Cai S, Rinaldi N, Poh HM, Li G, Sung YY, Heng CL, Core LJ, Tan SK, Ruan X, Lis JT, Kellis M, Ruan Y, Sung WK, Cheung E. An AR-ERG transcriptional signature defined by long-range chromatin interactomes in prostate cancer cells. Genome Res 2019; 29:223-235. [PMID: 30606742 PMCID: PMC6360806 DOI: 10.1101/gr.230243.117] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Accepted: 12/13/2018] [Indexed: 01/10/2023]
Abstract
The aberrant activities of transcription factors such as the androgen receptor (AR) underpin prostate cancer development. While the AR cis-regulation has been extensively studied in prostate cancer, information pertaining to the spatial architecture of the AR transcriptional circuitry remains limited. In this paper, we propose a novel framework to profile long-range chromatin interactions associated with AR and its collaborative transcription factor, erythroblast transformation-specific related gene (ERG), using chromatin interaction analysis by paired-end tag (ChIA-PET). We identified ERG-associated long-range chromatin interactions as a cooperative component in the AR-associated chromatin interactome, acting in concert to achieve coordinated regulation of a subset of AR target genes. Through multifaceted functional data analysis, we found that AR-ERG interaction hub regions are characterized by distinct functional signatures, including bidirectional transcription and cotranscription factor binding. In addition, cancer-associated long noncoding RNAs were found to be connected near protein-coding genes through AR-ERG looping. Finally, we found strong enrichment of prostate cancer genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) at AR-ERG co-binding sites participating in chromatin interactions and gene regulation, suggesting GWAS target genes identified from chromatin looping data provide more biologically relevant findings than using the nearest gene approach. Taken together, our results revealed an AR-ERG-centric higher-order chromatin structure that drives coordinated gene expression in prostate cancer progression and the identification of potential target genes for therapeutic intervention.
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Affiliation(s)
- Zhizhuo Zhang
- School of Computing, National University of Singapore, Singapore 117417.,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | | | - Shreyas Lingadahalli
- Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China.,Centre of Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Zikai Chen
- Genome Institute of Singapore, Singapore 138672.,Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China.,Centre of Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Mei Hui Liu
- Genome Institute of Singapore, Singapore 138672
| | | | | | - Nicola Rinaldi
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | | | - Guoliang Li
- Genome Institute of Singapore, Singapore 138672.,National Key Laboratory of Crop Genetic Improvement, Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | | | | | - Leighton J Core
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Si Kee Tan
- Genome Institute of Singapore, Singapore 138672
| | - Xiaoan Ruan
- Genome Institute of Singapore, Singapore 138672
| | - John T Lis
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Yijun Ruan
- Genome Institute of Singapore, Singapore 138672
| | - Wing-Kin Sung
- School of Computing, National University of Singapore, Singapore 117417.,Genome Institute of Singapore, Singapore 138672
| | - Edwin Cheung
- Genome Institute of Singapore, Singapore 138672.,Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China.,Centre of Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
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33
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Kara S, Hanna A, Pirela-Morillo GA, Gilliam CT, Wilson GD. Molecular Interaction Network Approach (MINA) identifies association of novel candidate disease genes. MethodsX 2019; 6:1286-1291. [PMID: 31198690 PMCID: PMC6555892 DOI: 10.1016/j.mex.2019.05.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 05/29/2019] [Indexed: 12/03/2022] Open
Abstract
Molecular Interaction Network Approach (MINA) was used to elucidate candidate disease genes. The approach was implemented to identify novel gene association with commonly known autoimmune diseases [1]. In MINA, we evaluated the hypothesis that “network proximity” within a whole genome molecular interaction network can be used to inform the search for multigene inheritance. There are now numerous examples of gene discoveries based upon network proximity between novel and previously identified disease genes (Yin et al., 2017 [2], Wang et al., 2011 [3], and Barrenas et al., 2009 [4]). This study extends the application of interaction networks to the interrogation of Genome Wide Association studies: first, by showing that a group of nine autoimmune diseases (AuD) genes “seed genes”, are connected in a highly non-random manner within a whole genome network; and second, by showing that the minimal number of connecting genes required to connect a maximal number of AuD candidate genes are highly enriched as candidate genes for AuD predisposing mutations. The findings imply that a threshold number of candidate genes for any heritable disorder can be used to “seed” a molecular interaction network that Serves to validate the disease status of closely associated seed genes Identifies genes that are highly enriched as novel candidate disease genes Provides a strategy for elucidation of epistatic gene x gene interactions
The method could provide a critical toll for understanding the genetic architecture of common traits and disorders.
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34
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Scheiner SM. The genetics of phenotypic plasticity. XVI. Interactions among traits and the flow of information. Evolution 2018; 72:2292-2307. [PMID: 30225897 DOI: 10.1111/evo.13601] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 08/30/2018] [Indexed: 12/17/2022]
Abstract
Although the environment varies, adaptive trait plasticity is uncommon, which can be due to either costs or limitations. Currently there is little evidence for costs of plasticity; limitations are a more promising explanation, including information reliability. A possible cause for a decrease in information reliability is the channeling of environmental information through one trait that then affects the phenotype of a second trait, the information path. Using an individual-based simulation model, I explored the ways in which configurations of trait interactions and patterns of environmental variation in space and time affect the evolution of phenotypic plasticity. I found that genotypes and phenotypes evolved to shorten and simplify the information path from the environment to fitness. A shortened path was characterized by a decrease in the amount of plasticity for traits that had a less direct connection between the environment of development and fitness. A simplified path was characterized by a decrease in the amount of plasticity for traits that had multiple paths between the environment and their phenotype. These results suggest that an eighth proposition be added to the theory of the evolution of phenotypic plasticity: Trait plasticity will evolve to minimize the information path between the environment and fitness.
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Affiliation(s)
- Samuel M Scheiner
- Division of Environmental Biology, National Science Foundation, Alexandria, Virginia, 22314
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35
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da Silveira WA, Renaud L, Simpson J, Glen WB, Hazard ES, Chung D, Hardiman G. miRmapper: A Tool for Interpretation of miRNA⁻mRNA Interaction Networks. Genes (Basel) 2018; 9:genes9090458. [PMID: 30223528 PMCID: PMC6162471 DOI: 10.3390/genes9090458] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 09/07/2018] [Accepted: 09/07/2018] [Indexed: 12/11/2022] Open
Abstract
It is estimated that 30% of all genes in the mammalian cells are regulated by microRNA (miRNAs). The most relevant miRNAs in a cellular context are not necessarily those with the greatest change in expression levels between healthy and diseased tissue. Differentially expressed (DE) miRNAs that modulate a large number of messenger RNA (mRNA) transcripts ultimately have a greater influence in determining phenotypic outcomes and are more important in a global biological context than miRNAs that modulate just a few mRNA transcripts. Here, we describe the development of a tool, “miRmapper”, which identifies the most dominant miRNAs in a miRNA–mRNA network and recognizes similarities between miRNAs based on commonly regulated mRNAs. Using a list of miRNA–target gene interactions and a list of DE transcripts, miRmapper provides several outputs: (1) an adjacency matrix that is used to calculate miRNA similarity utilizing the Jaccard distance; (2) a dendrogram and (3) an identity heatmap displaying miRNA clusters based on their effect on mRNA expression; (4) a miRNA impact table and (5) a barplot that provides a visual illustration of this impact. We tested this tool using nonmetastatic and metastatic bladder cancer cell lines and demonstrated that the most relevant miRNAs in a cellular context are not necessarily those with the greatest fold change. Additionally, by exploiting the Jaccard distance, we unraveled novel cooperative interactions between miRNAs from independent families in regulating common target mRNAs; i.e., five of the top 10 miRNAs act in synergy.
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Affiliation(s)
- Willian A da Silveira
- Center for Genomic Medicine, Bioinformatics, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
| | - Ludivine Renaud
- Division of Nephrology, Department of Medicine, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
- Laboratory for Marine Systems Biology, Hollings Marine Laboratory, Charleston, SC 29412, USA.
| | - Jonathan Simpson
- Center for Genomic Medicine, Bioinformatics, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
| | - William B Glen
- Center for Genomic Medicine, Bioinformatics, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
| | - Edward S Hazard
- Center for Genomic Medicine, Bioinformatics, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
- Academic Affairs Faculty, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
| | - Dongjun Chung
- Department of Public Health Sciences, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
| | - Gary Hardiman
- Center for Genomic Medicine, Bioinformatics, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
- Division of Nephrology, Department of Medicine, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
- Laboratory for Marine Systems Biology, Hollings Marine Laboratory, Charleston, SC 29412, USA.
- Department of Public Health Sciences, Medical University of South Carolina (MUSC), Charleston, SC 29425, USA.
- Institute for Global Food Security, Queens University Belfast, Stranmillis Road, Belfast BT9 5AG, UK.
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36
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Beguerisse-Díaz M, Bosque G, Oyarzún D, Picó J, Barahona M. Flux-dependent graphs for metabolic networks. NPJ Syst Biol Appl 2018; 4:32. [PMID: 30131869 PMCID: PMC6092364 DOI: 10.1038/s41540-018-0067-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 06/28/2018] [Accepted: 07/03/2018] [Indexed: 12/28/2022] Open
Abstract
Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic flows via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic flows and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions. Cellular metabolism is the result of a highly enmeshed set of biochemical reactions that is naturally amenable to graph-based analyses. Yet there are multiple ways to construct a graph representation from any given metabolic model. Here an international research team of UK and Spain scientists presents a principled approach to study metabolic models through the lens of network science. They propose a framework to construct graphs for genome-scale metabolic models that resolve various challenges, such as the incorporation of pool metabolites, the preservation of the directionality of metabolic flows, and the capability to incorporate specific flux information. The method can be integrated into pipelines based on flux balance analysis and provides a systematic framework to explore changes in network connectivity as a result of environmental shifts or genetic perturbations. The framework thus allows to interrogate context-specific metabolic responses beyond standard pathway descriptions. The authors illustrate the approach through the analysis of Escherichia coli's core metabolism in different growth conditions, as well as a rare metabolic disease affecting human hepatocytes.
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Affiliation(s)
- Mariano Beguerisse-Díaz
- 1Department of Mathematics, Imperial College London, London, SW7 2AZ UK.,2Mathematical Institute, University of Oxford, Oxford, OX2 6GG UK
| | - Gabriel Bosque
- 3Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Diego Oyarzún
- 1Department of Mathematics, Imperial College London, London, SW7 2AZ UK
| | - Jesús Picó
- 3Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
| | - Mauricio Barahona
- 1Department of Mathematics, Imperial College London, London, SW7 2AZ UK
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37
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Johnson LM, Chandler LM, Davies SK, Baer CF. Network Architecture and Mutational Sensitivity of the C. elegans Metabolome. Front Mol Biosci 2018; 5:69. [PMID: 30109234 PMCID: PMC6079199 DOI: 10.3389/fmolb.2018.00069] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 07/06/2018] [Indexed: 12/30/2022] Open
Abstract
A fundamental issue in evolutionary systems biology is understanding the relationship between the topological architecture of a biological network, such as a metabolic network, and the evolution of the network. The rate at which an element in a metabolic network accumulates genetic variation via new mutations depends on both the size of the mutational target it presents and its robustness to mutational perturbation. Quantifying the relationship between topological properties of network elements and the mutability of those elements will facilitate understanding the variation in and evolution of networks at the level of populations and higher taxa. We report an investigation into the relationship between two topological properties of 29 metabolites in the C. elegans metabolic network and the sensitivity of those metabolites to the cumulative effects of spontaneous mutation. The correlations between measures of network centrality and mutability are not statistically significant, but several trends point toward a weak positive association between network centrality and mutational sensitivity. There is a small but significant negative association between the mutational correlation of a pair of metabolites (rM) and the shortest path length between those metabolites. Positive association between the centrality of a metabolite and its mutational heritability is consistent with centrally-positioned metabolites presenting a larger mutational target than peripheral ones, and is inconsistent with centrality conferring mutational robustness, at least in toto. The weakness of the correlation between rM and the shortest path length between pairs of metabolites suggests that network locality is an important but not overwhelming factor governing mutational pleiotropy. These findings provide necessary background against which the effects of other evolutionary forces, most importantly natural selection, can be interpreted.
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Affiliation(s)
- Lindsay M Johnson
- Department of Biology, University of Florida, Gainesville, FL, United States
| | - Luke M Chandler
- University of Florida Genetics Institute, Gainesville, FL, United States
| | - Sarah K Davies
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, United Kingdom
| | - Charles F Baer
- Department of Biology, University of Florida, Gainesville, FL, United States.,University of Florida Genetics Institute, Gainesville, FL, United States
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38
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Structure Optimization for Large Gene Networks Based on Greedy Strategy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:9674108. [PMID: 30013615 PMCID: PMC6022335 DOI: 10.1155/2018/9674108] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 04/23/2018] [Accepted: 05/11/2018] [Indexed: 01/04/2023]
Abstract
In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.
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39
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Botero D, Alvarado C, Bernal A, Danies G, Restrepo S. Network Analyses in Plant Pathogens. Front Microbiol 2018; 9:35. [PMID: 29441045 PMCID: PMC5797656 DOI: 10.3389/fmicb.2018.00035] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 01/09/2018] [Indexed: 11/14/2022] Open
Abstract
Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.
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Affiliation(s)
- David Botero
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Diseño de Productos y Procesos, Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Biología Computacional y Ecología Microbiana, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Camilo Alvarado
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Adriana Bernal
- Laboratory of Molecular Interactions of Agricultural Microbes, LIMMA, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Giovanna Danies
- Department of Design, Universidad de Los Andes, Bogotá, Colombia
| | - Silvia Restrepo
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
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40
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Kirk IK, Weinhold N, Brunak S, Belling K. The impact of the protein interactome on the syntenic structure of mammalian genomes. PLoS One 2017; 12:e0179112. [PMID: 28910296 PMCID: PMC5598925 DOI: 10.1371/journal.pone.0179112] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 05/10/2017] [Indexed: 02/06/2023] Open
Abstract
Conserved synteny denotes evolutionary preserved gene order across species. It is not well understood to which degree functional relationships between genes are preserved in syntenic blocks. Here we investigate whether protein-coding genes conserved in mammalian syntenic blocks encode gene products that serve the common functional purpose of interacting at protein level, i.e. connectivity. High connectivity among protein-protein interactions (PPIs) was only moderately associated with conserved synteny on a genome-wide scale. However, we observed a smaller subset of 3.6% of all syntenic blocks with high-confidence PPIs that had significantly higher connectivity than expected by random. Additionally, syntenic blocks with high-confidence PPIs contained significantly more chromatin loops than the remaining blocks, indicating functional preservation among these syntenic blocks. Conserved synteny is typically defined by sequence similarity. In this study, we also examined whether a functional relationship, here PPI connectivity, can identify syntenic blocks independently of orthology. While orthology-based syntenic blocks with high-confident PPIs and the connectivity-based syntenic blocks largely overlapped, the connectivity-based approach identified additional syntenic blocks that were not found by conventional sequence-based methods alone. Additionally, the connectivity-based approach enabled identification of potential orthologous genes between species. Our analyses demonstrate that subsets of syntenic blocks are associated with highly connected proteins, and that PPI connectivity can be used to detect conserved synteny even if sequence conservation drifts beyond what orthology algorithms normally can identify.
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Affiliation(s)
- Isa Kristina Kirk
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nils Weinhold
- Memorial Sloan Kettering Cancer Center, Computational Biology Program, New York, NY, United States of America
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kirstine Belling
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
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41
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Moya-García A, Adeyelu T, Kruger FA, Dawson NL, Lees JG, Overington JP, Orengo C, Ranea JAG. Structural and Functional View of Polypharmacology. Sci Rep 2017; 7:10102. [PMID: 28860623 PMCID: PMC5579063 DOI: 10.1038/s41598-017-10012-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Accepted: 08/02/2017] [Indexed: 02/06/2023] Open
Abstract
Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.
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Affiliation(s)
- Aurelio Moya-García
- University College London, Institute of Structural and Molecular Biology, London, UK.
- Department of Molecular Biology and Biochemistry, Universidad de Malaga, 29071, Málaga Spain, CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain.
| | - Tolulope Adeyelu
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Felix A Kruger
- European Molecular Laboratory - European Bioinformatics Institute, Hinxton, UK
- BenevolentAI, Churchway 40, NW1 1LW, London, UK
| | - Natalie L Dawson
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Jon G Lees
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - John P Overington
- European Molecular Laboratory - European Bioinformatics Institute, Hinxton, UK
- Medicines Discovery Catapult, Mereside, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK
| | - Christine Orengo
- University College London, Institute of Structural and Molecular Biology, London, UK
| | - Juan A G Ranea
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, 29071, Málaga, Spain
- CIBER de Enfermedades Raras (CIBERER), 29071, Málaga, Spain
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42
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Khoomrung S, Wanichthanarak K, Nookaew I, Thamsermsang O, Seubnooch P, Laohapand T, Akarasereenont P. Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine. Front Pharmacol 2017; 8:474. [PMID: 28769804 PMCID: PMC5513896 DOI: 10.3389/fphar.2017.00474] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Accepted: 07/03/2017] [Indexed: 12/28/2022] Open
Abstract
In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.
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Affiliation(s)
- Sakda Khoomrung
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden
| | - Kwanjeera Wanichthanarak
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Intawat Nookaew
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of TechnologyGothenburg, Sweden.,Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical SciencesLittle Rock, AR, United States
| | - Onusa Thamsermsang
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Patcharamon Seubnooch
- Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Tawee Laohapand
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
| | - Pravit Akarasereenont
- Center of Applied Thai Traditional Medicine, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand.,Department of Pharmacology, Faculty of Medicine Siriraj Hospital, Mahidol UniversityBangkok, Thailand
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43
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Liang Y, Kelemen A. Computational dynamic approaches for temporal omics data with applications to systems medicine. BioData Min 2017. [PMID: 28638442 PMCID: PMC5473988 DOI: 10.1186/s13040-017-0140-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.
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Affiliation(s)
- Yulan Liang
- Department of Family and Community Health, University of Maryland, Baltimore, MD 21201 USA
| | - Arpad Kelemen
- Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD 21201 USA
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44
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Malhotra AG, Jha M, Singh S, Pandey KM. Construction of a Comprehensive Protein-Protein Interaction Map for Vitiligo Disease to Identify Key Regulatory Elements: A Systemic Approach. Interdiscip Sci 2017; 10:500-514. [PMID: 28290051 DOI: 10.1007/s12539-017-0213-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 12/09/2016] [Accepted: 12/29/2016] [Indexed: 12/14/2022]
Abstract
Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo.
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Affiliation(s)
- Anvita Gupta Malhotra
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Mohit Jha
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Sudha Singh
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Khushhali M Pandey
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India.
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45
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Considerations when choosing a genetic model organism for metabolomics studies. Curr Opin Chem Biol 2016; 36:7-14. [PMID: 28025166 DOI: 10.1016/j.cbpa.2016.12.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 12/01/2016] [Accepted: 12/05/2016] [Indexed: 01/16/2023]
Abstract
Model organisms are important in many areas of chemical biology. In metabolomics, model organisms can provide excellent samples for methods development as well as the foundation of comparative phylometabolomics, which will become possible as metabolomics applications expand. Comparative studies of conserved and unique metabolic pathways will help in the annotation of metabolites as well as provide important new targets of investigation in biology and biomedicine. However, most chemical biologists are not familiar with genetics, which needs to be considered when choosing a model organism. In this review we summarize the strengths and weaknesses of several genetic systems, including natural isolates, recombinant inbred lines, and genetic mutations. We also discuss methods to detect targets of selection on the metabolome.
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46
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Duclert-Savatier N, Bouvier G, Nilges M, Malliavin TE. Building Graphs To Describe Dynamics, Kinetics, and Energetics in the d-ALa:d-Lac Ligase VanA. J Chem Inf Model 2016; 56:1762-75. [PMID: 27579990 PMCID: PMC5039762 DOI: 10.1021/acs.jcim.6b00211] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
![]()
The d-Ala:d-Lac ligase, VanA, plays a critical
role in the resistance of vancomycin. Indeed, it is involved in the
synthesis of a peptidoglycan precursor, to which vancomycin cannot
bind. The reaction catalyzed by VanA requires the opening of the so-called
“ω-loop”, so that the substrates can enter the
active site. Here, the conformational landscape of VanA is explored
by an enhanced sampling approach: the temperature-accelerated molecular
dynamics (TAMD). Analysis of the molecular dynamics (MD) and TAMD
trajectories recorded on VanA permits a graphical description of the
structural and kinetics aspects of the conformational space of VanA,
where the internal mobility and various opening modes of the ω-loop
play a major role. The other important feature is the correlation
of the ω-loop motion with the movements of the opposite domain,
defined as containing the residues A149–Q208. Conformational
and kinetic clusters have been determined and a path describing the
ω-loop opening was extracted from these clusters. The determination
of this opening path, as well as the relative importance of hydrogen
bonds along the path, permit one to propose some key residue interactions
for the kinetics of the ω-loop opening.
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Affiliation(s)
- Nathalie Duclert-Savatier
- Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528 , 25, rue du Dr Roux, 75015 Paris, France
| | - Guillaume Bouvier
- Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528 , 25, rue du Dr Roux, 75015 Paris, France
| | - Michael Nilges
- Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528 , 25, rue du Dr Roux, 75015 Paris, France
| | - Thérèse E Malliavin
- Département de Biologie Structurale et Chimie, Institut Pasteur, Unité de Bioinformatique Structurale, CNRS UMR 3528 , 25, rue du Dr Roux, 75015 Paris, France
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47
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Platig J, Castaldi PJ, DeMeo D, Quackenbush J. Bipartite Community Structure of eQTLs. PLoS Comput Biol 2016; 12:e1005033. [PMID: 27618581 PMCID: PMC5019382 DOI: 10.1371/journal.pcbi.1005033] [Citation(s) in RCA: 37] [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: 01/08/2016] [Accepted: 06/23/2016] [Indexed: 11/18/2022] Open
Abstract
Genome Wide Association Studies (GWAS) and expression quantitative trait locus (eQTL) analyses have identified genetic associations with a wide range of human phenotypes. However, many of these variants have weak effects and understanding their combined effect remains a challenge. One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes, including disease states. Here we present CONDOR, a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context. In applying CONDOR to eQTLs in chronic obstructive pulmonary disease (COPD), we found the global network “hub” SNPs were devoid of disease associations through GWAS. However, the network was organized into 52 communities of SNPs and genes, many of which were enriched for genes in specific functional classes. We identified local hubs within each community (“core SNPs”) and these were enriched for GWAS SNPs for COPD and many other diseases. These results speak to our intuition: rather than single SNPs influencing single genes, we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions. These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits. Large-scale studies have identified thousands of genetic variants associated with different phenotypes without explaining their function. Expression quantitative trait locus analysis associates the compendium of genetic variants with expression levels of individual genes, providing the opportunity to link those variants to functions. But the complexity of those associations has caused most analyses to focus solely on genetic variants immediately adjacent to the genes they may influence. We describe a method that embraces the complexity, representing all variant-gene associations as a bipartite graph. The graph contains highly modular, functional communities in which disease-associated variants emerge as those likely to perturb the structure of the network and the function of the genes in these communities.
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Affiliation(s)
- John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard Chan School of Public Health, Boston, Massachusetts, United States of America
- * E-mail:
| | - Peter J. Castaldi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Division of General Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dawn DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard Chan School of Public Health, Boston, Massachusetts, United States of America
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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Ashford P, Hernandez A, Greco TM, Buch A, Sodeik B, Cristea IM, Grünewald K, Shepherd A, Topf M. HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1. Mol Cell Proteomics 2016; 15:2939-53. [PMID: 27384951 PMCID: PMC5013309 DOI: 10.1074/mcp.m116.058552] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Indexed: 11/12/2022] Open
Abstract
Human herpesviruses are widespread human pathogens with a remarkable impact on worldwide public health. Despite intense decades of research, the molecular details in many aspects of their function remain to be fully characterized. To unravel the details of how these viruses operate, a thorough understanding of the relationships between the involved components is key. Here, we present HVint, a novel protein-protein intraviral interaction resource for herpes simplex virus type 1 (HSV-1) integrating data from five external sources. To assess each interaction, we used a scoring scheme that takes into consideration aspects such as the type of detection method and the number of lines of evidence. The coverage of the initial interactome was further increased using evolutionary information, by importing interactions reported for other human herpesviruses. These latter interactions constitute, therefore, computational predictions for potential novel interactions in HSV-1. An independent experimental analysis was performed to confirm a subset of our predicted interactions. This subset covers proteins that contribute to nuclear egress and primary envelopment events, including VP26, pUL31, pUL40, and the recently characterized pUL32 and pUL21. Our findings support a coordinated crosstalk between VP26 and proteins such as pUL31, pUS9, and the CSVC complex, contributing to the development of a model describing the nuclear egress and primary envelopment pathways of newly synthesized HSV-1 capsids. The results are also consistent with recent findings on the involvement of pUL32 in capsid maturation and early tegumentation events. Further, they open the door to new hypotheses on virus-specific regulators of pUS9-dependent transport. To make this repository of interactions readily accessible for the scientific community, we also developed a user-friendly and interactive web interface. Our approach demonstrates the power of computational predictions to assist in the design of targeted experiments for the discovery of novel protein-protein interactions.
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Affiliation(s)
- Paul Ashford
- From the: ‡Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK
| | - Anna Hernandez
- From the: ‡Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK; §Oxford Particle Imaging Centre, Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Todd Michael Greco
- ¶Department of Molecular Biology, Princeton University, Lewis Thomas Laboratory, Washington Road, Princeton, New Jersey 08544
| | - Anna Buch
- ‖Institute of Virology, Hannover Medical School, OE 4310, Carl-Neuberg-Str. 1, D-30623, Hannover, Germany
| | - Beate Sodeik
- ‖Institute of Virology, Hannover Medical School, OE 4310, Carl-Neuberg-Str. 1, D-30623, Hannover, Germany
| | - Ileana Mihaela Cristea
- ¶Department of Molecular Biology, Princeton University, Lewis Thomas Laboratory, Washington Road, Princeton, New Jersey 08544;
| | - Kay Grünewald
- §Oxford Particle Imaging Centre, Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Adrian Shepherd
- From the: ‡Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK
| | - Maya Topf
- From the: ‡Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK;
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Gómez-Vela F, Barranco CD, Díaz-Díaz N. Incorporating biological knowledge for construction of fuzzy networks of gene associations. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.01.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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50
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Stoney RA, Ames RM, Nenadic G, Robertson DL, Schwartz JM. Disentangling the multigenic and pleiotropic nature of molecular function. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 6:S3. [PMID: 26678917 PMCID: PMC4674882 DOI: 10.1186/1752-0509-9-s6-s3] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. RESULTS We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. CONCLUSIONS Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes.
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