1
|
Petti M, Farina L. Network medicine for patients' stratification: From single-layer to multi-omics. WIREs Mech Dis 2023; 15:e1623. [PMID: 37323106 DOI: 10.1002/wsbm.1623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/08/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Precision medicine research increasingly relies on the integrated analysis of multiple types of omics. In the era of big data, the large availability of different health-related information represents a great, but at the same time untapped, chance with a potentially fundamental role in the prevention, diagnosis and prognosis of diseases. Computational methods are needed to combine this data to create a comprehensive view of a given disease. Network science can model biomedical data in terms of relationships among molecular players of different nature and has been successfully proposed as a new paradigm for studying human diseases. Patient stratification is an open challenge aimed at identifying subtypes with different disease manifestations, severity, and expected survival time. Several stratification approaches based on high-throughput gene expression measurements have been successfully applied. However, few attempts have been proposed to exploit the integration of various genotypic and phenotypic data to discover novel sub-types or improve the detection of known groupings. This article is categorized under: Cancer > Biomedical Engineering Cancer > Computational Models Cancer > Genetics/Genomics/Epigenetics.
Collapse
Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| |
Collapse
|
2
|
Song E. Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks. Front Genet 2023; 14:1270185. [PMID: 37823029 PMCID: PMC10562725 DOI: 10.3389/fgene.2023.1270185] [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: 08/01/2023] [Accepted: 09/12/2023] [Indexed: 10/13/2023] Open
Abstract
Genome-wide association studies (GWAS) involving increasing sample sizes have identified hundreds of genetic variants associated with complex diseases, such as type 2 diabetes (T2D); however, it is unclear how GWAS hits form unique topological structures in protein-protein interaction (PPI) networks. Using persistent homology, this study explores the evolution and persistence of the topological features of T2D GWAS hits in the PPI network with increasing p-value thresholds. We define an n-dimensional persistent disease module as a higher-order generalization of the largest connected component (LCC). The 0-dimensional persistent T2D disease module is the LCC of the T2D GWAS hits, which is significantly detected in the PPI network (196 nodes and 235 edges, P< 0.05). In the 1-dimensional homology group analysis, all 18 1-dimensional holes (loops) of the T2D GWAS hits persist over all p-value thresholds. The 1-dimensional persistent T2D disease module comprising these 18 persistent 1-dimensional holes is significantly larger than that expected by chance (59 nodes and 83 edges, P< 0.001), indicating a significant topological structure in the PPI network. Our computational topology framework potentially possesses broad applicability to other complex phenotypes in identifying topological features that play an important role in disease pathobiology.
Collapse
Affiliation(s)
- Euijun Song
- Yonsei University College of Medicine, Seoul, Republic of Korea
- Present: Independent Researcher, Gyeonggi, Republic of Korea
| |
Collapse
|
3
|
Zhang Y, Lin X, Gao Z, Wang T, Dong K, Zhang J. An omics data analysis method based on feature linear relationship and graph convolutional network. J Biomed Inform 2023; 145:104479. [PMID: 37634557 DOI: 10.1016/j.jbi.2023.104479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/26/2023] [Accepted: 08/23/2023] [Indexed: 08/29/2023]
Abstract
Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification.
Collapse
Affiliation(s)
- Yanhui Zhang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
| | - Zhenbo Gao
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Tianxiang Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Kunjie Dong
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Jianjun Zhang
- Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Liaoning, China
| |
Collapse
|
4
|
Pandey AK, Loscalzo J. Network medicine: an approach to complex kidney disease phenotypes. Nat Rev Nephrol 2023:10.1038/s41581-023-00705-0. [PMID: 37041415 DOI: 10.1038/s41581-023-00705-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.
Collapse
Affiliation(s)
- Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
5
|
Pacheco Pachado M, Casas AI, Elbatreek MH, Nogales C, Guney E, Espay AJ, Schmidt HH. Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes. J Alzheimers Dis 2023; 96:47-56. [PMID: 37742653 PMCID: PMC10657714 DOI: 10.3233/jad-230694] [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] [Accepted: 08/22/2023] [Indexed: 09/26/2023]
Abstract
Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.
Collapse
Affiliation(s)
- Mayra Pacheco Pachado
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ana I. Casas
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Universitätsklinikum Essen, Klinik für Neurologie, Essen, Germany
| | - Mahmoud H. Elbatreek
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Cristian Nogales
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Emre Guney
- Discovery and Data Science (DDS) Unit, STALICLA R&D SL, Barcelona, Spain
| | - Alberto J. Espay
- James J. and Joan A. Gardner Family Center for Parkinson’s Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, USA
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
6
|
Petti M, Alfano C, Farina L. Molecular network analysis of hormonal contraceptives side effects via database integration. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
|
7
|
Loscalzo J. Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning. FASEB J 2023; 37:e22660. [PMID: 36468661 PMCID: PMC10107166 DOI: 10.1096/fj.202201683r] [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: 10/14/2022] [Revised: 10/27/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.
Collapse
Affiliation(s)
- Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
8
|
Buzzao D, Castresana-Aguirre M, Guala D, Sonnhammer ELL. TOPAS, a network-based approach to detect disease modules in a top-down fashion. NAR Genom Bioinform 2022; 4:lqac093. [PMCID: PMC9706483 DOI: 10.1093/nargab/lqac093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/14/2022] [Accepted: 11/15/2022] [Indexed: 12/02/2022] Open
Abstract
A vast scenario of potential disease mechanisms and remedies is yet to be discovered. The field of Network Medicine has grown thanks to the massive amount of high-throughput data and the emerging evidence that disease-related proteins form ‘disease modules’. Relying on prior disease knowledge, network-based disease module detection algorithms aim at connecting the list of known disease associated genes by exploiting interaction networks. Most existing methods extend disease modules by iteratively adding connector genes in a bottom-up fashion, while top-down approaches remain largely unexplored. We have created TOPAS, an iterative approach that aims at connecting the largest number of seed nodes in a top-down fashion through connectors that guarantee the highest flow of a Random Walk with Restart in a network of functional associations. We used a corpus of 382 manually selected functional gene sets to benchmark our algorithm against SCA, DIAMOnD, MaxLink and ROBUST across four interactomes. We demonstrate that TOPAS outperforms competing methods in terms of Seed Recovery Rate, Seed to Connector Ratio and consistency during module detection. We also show that TOPAS achieves competitive performance in terms of biological relevance of detected modules and scalability.
Collapse
Affiliation(s)
- Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | | | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
| | | |
Collapse
|
9
|
Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
Collapse
|
10
|
Röhl A, Baek SH, Kachroo P, Morrow JD, Tantisira K, Silverman EK, Weiss ST, Sharma A, Glass K, DeMeo DL. Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease. Respir Res 2022; 23:69. [PMID: 35331221 PMCID: PMC8944072 DOI: 10.1186/s12931-022-01963-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 02/08/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation. METHODS In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks. RESULTS We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways. CONCLUSIONS The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.
Collapse
Affiliation(s)
- Annika Röhl
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Seung Han Baek
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jarrett D Morrow
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kelan Tantisira
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pediatric Respiratory Medicine, University of California San Diego, San Diego, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Center for Complex Network Research, Northeastern University, Boston, MA, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| |
Collapse
|
11
|
Bernett J, Krupke D, Sadegh S, Baumbach J, Fekete SP, Kacprowski T, List M, Blumenthal DB. Robust disease module mining via enumeration of diverse prize-collecting Steiner trees. Bioinformatics 2022; 38:1600-1606. [PMID: 34984440 DOI: 10.1093/bioinformatics/btab876] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/29/2021] [Accepted: 12/31/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Disease module mining methods (DMMMs) extract subgraphs that constitute candidate disease mechanisms from molecular interaction networks such as protein-protein interaction (PPI) networks. Irrespective of the employed models, DMMMs typically include non-robust steps in their workflows, i.e. the computed subnetworks vary when running the DMMMs multiple times on equivalent input. This lack of robustness has a negative effect on the trustworthiness of the obtained subnetworks and is hence detrimental for the widespread adoption of DMMMs in the biomedical sciences. RESULTS To overcome this problem, we present a new DMMM called ROBUST (robust disease module mining via enumeration of diverse prize-collecting Steiner trees). In a large-scale empirical evaluation, we show that ROBUST outperforms competing methods in terms of robustness, scalability and, in most settings, functional relevance of the produced modules, measured via KEGG (Kyoto Encyclopedia of Genes and Genomes) gene set enrichment scores and overlap with DisGeNET disease genes. AVAILABILITY AND IMPLEMENTATION A Python 3 implementation and scripts to reproduce the results reported in this article are available on GitHub: https://github.com/bionetslab/robust, https://github.com/bionetslab/robust-eval. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Judith Bernett
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Dominik Krupke
- Department of Computer Science, TU Braunschweig, 38106 Braunschweig, Germany
| | - Sepideh Sadegh
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany.,Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, 22607 Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark
| | - Sándor P Fekete
- Department of Computer Science, TU Braunschweig, 38106 Braunschweig, Germany.,Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany
| | - Tim Kacprowski
- Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Braunschweig, Germany.,Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technical University of Braunschweig and Hannover Medical School, 38106 Braunschweig, Germany
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - David B Blumenthal
- Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander University Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| |
Collapse
|
12
|
Balogh OM, Benczik B, Horváth A, Pétervári M, Csermely P, Ferdinandy P, Ágg B. Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model. BMC Bioinformatics 2022; 23:78. [PMID: 35183129 PMCID: PMC8858570 DOI: 10.1186/s12859-022-04598-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/02/2022] [Indexed: 12/03/2022] Open
Abstract
Background The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein–protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model. Results We created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein–protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species. Conclusion We developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04598-x.
Collapse
|
13
|
Santos SDS, Torres M, Galeano D, Sánchez MDM, Cernuzzi L, Paccanaro A. Machine learning and network medicine approaches for drug repositioning for COVID-19. PATTERNS (NEW YORK, N.Y.) 2022; 3:100396. [PMID: 34778851 PMCID: PMC8576113 DOI: 10.1016/j.patter.2021.100396] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/21/2021] [Accepted: 11/01/2021] [Indexed: 12/13/2022]
Abstract
We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.
Collapse
Affiliation(s)
- Suzana de Siqueira Santos
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- COVID-19 International Research Team
| | - Mateo Torres
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- COVID-19 International Research Team
| | - Diego Galeano
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- Facultad de Ingenieria, Universidad Nacional de Asunción, Luque 110948, Paraguay
- COVID-19 International Research Team
| | | | - Luca Cernuzzi
- Universidad Católica “Nuestra Señora de la Asunción”, Asunción C.C. 1683, Paraguay
| | - Alberto Paccanaro
- Escola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro 22250-900, Brazil
- Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham TW20 0EX, UK
- COVID-19 International Research Team
| |
Collapse
|
14
|
Badkas A, De Landtsheer S, Sauter T. Construction and contextualization approaches for protein-protein interaction networks. Comput Struct Biotechnol J 2022; 20:3280-3290. [PMID: 35832626 PMCID: PMC9251778 DOI: 10.1016/j.csbj.2022.06.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 11/17/2022] Open
Abstract
Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.
Collapse
|
15
|
Demirel HC, Arici MK, Tuncbag N. Computational approaches leveraging integrated connections of multi-omic data toward clinical applications. Mol Omics 2021; 18:7-18. [PMID: 34734935 DOI: 10.1039/d1mo00158b] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.
Collapse
Affiliation(s)
- Habibe Cansu Demirel
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey
| | - Muslum Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, 06800, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, 06044, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, 34450, Turkey.,School of Medicine, Koc University, Istanbul, 34450, Turkey.,Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Turkey.
| |
Collapse
|
16
|
Petti M, Farina L, Francone F, Lucidi S, Macali A, Palagi L, De Santis M. MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction. Genes (Basel) 2021; 12:1713. [PMID: 34828319 PMCID: PMC8624742 DOI: 10.3390/genes12111713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/16/2021] [Accepted: 10/25/2021] [Indexed: 11/17/2022] Open
Abstract
Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.
Collapse
Affiliation(s)
- Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy; (L.F.); (F.F.); (S.L.); (A.M.); (L.P.); (M.D.S.)
| | | | | | | | | | | | | |
Collapse
|
17
|
Liu J, Zhu H, Qiu J. Locally Adjust Networks Based on Connectivity and Semantic Similarities for Disease Module Detection. Front Genet 2021; 12:726596. [PMID: 34759955 PMCID: PMC8575408 DOI: 10.3389/fgene.2021.726596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/22/2021] [Indexed: 11/13/2022] Open
Abstract
For studying the pathogenesis of complex diseases, it is important to identify the disease modules in the system level. Since the protein-protein interaction (PPI) networks contain a number of incomplete and incorrect interactome, most existing methods often lead to many disease proteins isolating from disease modules. In this paper, we propose an effective disease module identification method IDMCSS, where the used human PPI networks are obtained by adding some potential missing interactions from existing PPI networks, as well as removing some potential incorrect interactions. In IDMCSS, a network adjustment strategy is developed to add or remove links around disease proteins based on both topological and semantic information. Next, neighboring proteins of disease proteins are prioritized according to a suggested similarity between each of them and disease proteins, and the protein with the largest similarity with disease proteins is added into a candidate disease protein set one by one. The stopping criterion is set to the boundary of the disease proteins. Finally, the connected subnetwork having the largest number of disease proteins is selected as a disease module. Experimental results on asthma demonstrate the effectiveness of the method in comparison to existing algorithms for disease module identification. It is also shown that the proposed IDMCSS can obtain the disease modules having crucial biological processes of asthma and 12 targets for drug intervention can be predicted.
Collapse
Affiliation(s)
- Jia Liu
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
| | - Huole Zhu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, China
| | - Jianfeng Qiu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, Hefei, China
| |
Collapse
|
18
|
Rivero-García I, Castresana-Aguirre M, Guglielmo L, Guala D, Sonnhammer ELL. Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19. Sci Rep 2021; 11:20687. [PMID: 34667255 PMCID: PMC8526804 DOI: 10.1038/s41598-021-99721-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/30/2021] [Indexed: 12/14/2022] Open
Abstract
This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.
Collapse
Affiliation(s)
- Inés Rivero-García
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Miguel Castresana-Aguirre
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Luca Guglielmo
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Dimitri Guala
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Erik L. L. Sonnhammer
- grid.10548.380000 0004 1936 9377Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| |
Collapse
|
19
|
Tian Y, Su X, Su Y, Zhang X. EMODMI: A Multi-Objective Optimization Based Method to Identify Disease Modules. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1109/tetci.2020.3014923] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
20
|
Sarno F, Benincasa G, List M, Barabasi AL, Baumbach J, Ciardiello F, Filetti S, Glass K, Loscalzo J, Marchese C, Maron BA, Paci P, Parini P, Petrillo E, Silverman EK, Verrienti A, Altucci L, Napoli C. Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside. Clin Epigenetics 2021; 13:66. [PMID: 33785068 PMCID: PMC8010949 DOI: 10.1186/s13148-021-01047-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/01/2021] [Indexed: 02/07/2023] Open
Abstract
Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.
Collapse
Affiliation(s)
- Federica Sarno
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Albert-Lazlo Barabasi
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg, Germany
| | - Fortunato Ciardiello
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | | | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Bradley A Maron
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paola Paci
- Department of Computer, Control, and Management Engineering, Sapienza University, Rome, Italy
| | - Paolo Parini
- Department of Laboratory Medicine and Department of Medicine, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
| | - Enrico Petrillo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Antonella Verrienti
- Department of Translational and Precision Medicine, Sapienza University, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy.
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
- Clinical Department of Internal Medicine and Specialistic Units, AOU, University of Campania "Luigi Vanvitelli", Naples, Italy
| |
Collapse
|
21
|
Lazareva O, Baumbach J, List M, Blumenthal DB. On the limits of active module identification. Brief Bioinform 2021; 22:6189770. [PMID: 33782690 DOI: 10.1093/bib/bbab066] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 01/29/2021] [Indexed: 12/12/2022] Open
Abstract
In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein-protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.
Collapse
Affiliation(s)
- Olga Lazareva
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany.,Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.,Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Markus List
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| | - David B Blumenthal
- Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany
| |
Collapse
|
22
|
Paci P, Fiscon G, Conte F, Wang RS, Farina L, Loscalzo J. Gene co-expression in the interactome: moving from correlation toward causation via an integrated approach to disease module discovery. NPJ Syst Biol Appl 2021; 7:3. [PMID: 33479222 PMCID: PMC7819998 DOI: 10.1038/s41540-020-00168-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 10/19/2020] [Indexed: 01/29/2023] Open
Abstract
In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein-protein interaction network (PPI, or interactome) to predict novel disease-disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.
Collapse
Affiliation(s)
- Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Fondazione per la Medicina Personalizzata, Via Goffredo Mameli, 3/1 Genova, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Rui-Sheng Wang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| |
Collapse
|
23
|
Lee LY, Pandey AK, Maron BA, Loscalzo J. Network medicine in Cardiovascular Research. Cardiovasc Res 2020; 117:2186-2202. [PMID: 33165538 DOI: 10.1093/cvr/cvaa321] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/08/2020] [Accepted: 10/30/2020] [Indexed: 12/21/2022] Open
Abstract
The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.
Collapse
Affiliation(s)
- Laurel Y Lee
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.,Department of Cardiology, Boston VA Healthcare System, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| |
Collapse
|
24
|
Gao Y, Mu J, Xu T, Linghu T, Zhao H, Tian J, Qin X. Metabolomic analysis of the hippocampus in a rat model of chronic mild unpredictable stress-induced depression based on a pathway crosstalk and network module approach. J Pharm Biomed Anal 2020; 193:113755. [PMID: 33190083 DOI: 10.1016/j.jpba.2020.113755] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/16/2020] [Accepted: 11/04/2020] [Indexed: 02/04/2023]
Abstract
BACKGROUND The molecular alterations underlying the pathogenesis of depression have not been systematically defined. Increasing evidence suggests that hippocampus metabolism is strongly involved in the pathogenesis of chronic mild unpredictable stress (CUMS)-induced depression. The principal objective of this study was to reveal important information concerning the pathogenesis of depression through a comprehensive analysis of metabolites in the hippocampus in a CUMS rat model. METHODS Metabolites related to metabolic changes in the hippocampus in the CUMS model were collected from a depression-specific database and published literature. Potential metabolite pathways were identified by the Omicsolution tool. Then, crosstalk analysis was carried out to investigate the relationship between different important pathways. In addition, MetaboAnalyst was used to analyze potential metabolites for drug-related metabolite enrichment analysis, which was used to study hippocampus metabolite-related drug pathways in a CUMS model. Then, a metabolite-protein interaction (MPI) network was constructed and analyzed to identify important metabolites and proteins. The functional modules were extracted using the CNM network decomposition algorithm. Finally, neurotransmitters in the hippocampus of rats with CUMS depression were detected to verify the important pathways. RESULTS In the current study, 53 significantly enriched pathways related to the 107 identified metabolites were selected, and the top ranked enriched pathways included arginine and proline metabolism, neuroactive ligand-receptor interaction, phenylalanine metabolism, bile secretion, and glutathione metabolism. Pathway crosstalk analysis showed that the significantly enriched pathways were divided into two interrelated modules, which were mainly involved in metabolism, signal transduction, neurotransmitters, and the endocrine system. Enrichment analysis of drug-related metabolic KEGG pathways identified the antibiotic pathways as the most important pathways. In the MPI network, the hub metabolites were phosphate, arachidonic acid, oxoglutaric acid, l-glutamic acid, and glutathione, and the hub proteins were Got1, Got2, Tat, Ccbl1, Ccbl2, Il4i1. A total of 16 functional modules were extracted from the MPI network by using the CNM algorithm. Finally, metabolites related to serotonergic synapses, dopaminergic synapses, and glutamatergic synapses were found to be involved in the pathology of depression. CONCLUSION We found that neurotransmitter pathways (serotonergic synapses, dopaminergic synapses and glutamatergic synapses) in the hippocampus play a crucial role in the underlying molecular mechanism of depression, which provides useful clues for identifying the detailed depression-associated metabolic profiles.
Collapse
Affiliation(s)
- Yao Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan, 030006, Shanxi, China
| | - Junfang Mu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China
| | - Teng Xu
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan, 030006, Shanxi, China
| | - Ting Linghu
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan, 030006, Shanxi, China
| | - Huiliang Zhao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan, 030006, Shanxi, China
| | - Junsheng Tian
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan, 030006, Shanxi, China.
| | - Xuemei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Taiyuan, 030006, Shanxi, China.
| |
Collapse
|
25
|
Benincasa G, de Candia P, Costa D, Faenza M, Mansueto G, Ambrosio G, Napoli C. Network Medicine Approach in Prevention and Personalized Treatment of Dyslipidemias. Lipids 2020; 56:259-268. [PMID: 33118184 DOI: 10.1002/lipd.12290] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 10/01/2020] [Indexed: 12/18/2022]
Abstract
Dyslipidemias can affect molecular networks underlying the metabolic homeostasis and vascular function leading to atherogenesis at early stages of development. Since disease-related proteins often interact with each other in functional modules, many advanced network-oriented algorithms were applied to patient-derived big data to identify the complex gene-environment interactions underlying the early pathophysiology of dyslipidemias and atherosclerosis. Both the proprotein convertase subtilisin/kexin type 7 (PCSK7) and collagen type 1 alpha 1 chain (COL1A1) genes arose from the application of TFfit and WGCNA algorithms, respectively, as potential useful therapeutic targets in prevention of dyslipidemias. Moreover, the Seed Connector algorithm (SCA) algorithm suggested a putative role of the neuropilin-1 (NRP1) protein as drug target, whereas a regression network analysis reported that niacin may provide benefits in mixed dyslipidemias. Dyslipidemias are highly heterogeneous at the clinical level; thus, it would be helpful to overcome traditional evidence-based paradigm toward a personalized risk assessment and therapy. Network Medicine uses omics data, artificial intelligence (AI), imaging tools, and clinical information to design personalized therapy of dyslipidemias and atherosclerosis. Recently, a novel non-invasive AI-derived biomarker, named Fat Attenuation Index (FAI™) has been established to early detect clinical signs of atherosclerosis. Moreover, an integrated AI-radiomics approach can detect fibrosis and microvascular remodeling improving the customized risk assessment. Here, we offer a network-based roadmap ranging from novel molecular pathways to digital therapeutics which can improve personalized therapy of dyslipidemias.
Collapse
Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, Naples, 80138, Italy
| | | | - Dario Costa
- UOC Division of Immunohematology, Transfusion Medicine and Transplant Immunology, Department of Internal Medicine and Specialistics, University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, Naples, 80138, Italy
| | - Mario Faenza
- Multidisciplinary Department of Medical, Surgical and Dental Sciences, Plastic Surgery Unit, University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, Naples, 80138, Italy
| | - Gelsomina Mansueto
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, Naples, 80138, Italy
| | - Giuseppe Ambrosio
- Division of Cardiology, University of Perugia School of Medicine, Via S. Andrea delle Fratte, Perugia, 06156, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, Naples, 80138, Italy.,Clinical Department of Internal Medicine and Specialistics, Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Pz. Miraglia, 2, Naples, 80138, Italy
| |
Collapse
|
26
|
Su Y, Su X, Wang Q, Zhang L. A multi-objective optimization method for identification of module biomarkers for disease diagnosis. Methods 2020; 192:35-45. [PMID: 32949693 DOI: 10.1016/j.ymeth.2020.09.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 07/03/2020] [Accepted: 09/07/2020] [Indexed: 01/14/2023] Open
Abstract
Biomarker identification aims at finding a set of biological indicators that best discriminate biological samples of different phenotypes. In this paper, we take the module containing the significant disease-related genes and their interactions from biological networks as a module biomarker, and propose an evolutionary multi-objective optimization method to identify module biomarkers for disease diagnosis. To be specific, we take the classification accuracy on control and disease samples, the association with disease and the intra-link density in the module as the optimization objectives. To achieve the best performance, a novel population initiation strategy is tailored to generate dense-connected initial solutions, and a specific population update strategy is employed to direct the evolution towards the global optimums with abundant diversity. Experimental results show that our method outperforms the previous state-of-the-art disease diagnosis methods. Meantime, the detected biomarker module can reflect the basic and significant biological functions and has a great correlation with a disease phenotype.
Collapse
Affiliation(s)
- Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | - Xiaochun Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | - Qijun Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China.
| | - Lejun Zhang
- Yangzhou Univeristy, Yangzhou 225009, China.
| |
Collapse
|
27
|
Kumar T, Blondel L, Extavour CG. Topology-driven protein-protein interaction network analysis detects genetic sub-networks regulating reproductive capacity. eLife 2020; 9:54082. [PMID: 32901612 PMCID: PMC7550192 DOI: 10.7554/elife.54082] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 09/01/2020] [Indexed: 12/23/2022] Open
Abstract
Understanding the genetic regulation of organ structure is a fundamental problem in developmental biology. Here, we use egg-producing structures of insect ovaries, called ovarioles, to deduce systems-level gene regulatory relationships from quantitative functional genetic analysis. We previously showed that Hippo signalling, a conserved regulator of animal organ size, regulates ovariole number in Drosophila melanogaster. To comprehensively determine how Hippo signalling interacts with other pathways in this regulation, we screened all known signalling pathway genes, and identified Hpo-dependent and Hpo-independent signalling requirements. Network analysis of known protein-protein interactions among screen results identified independent gene regulatory sub-networks regulating one or both of ovariole number and egg laying. These sub-networks predict involvement of previously uncharacterised genes with higher accuracy than the original candidate screen. This shows that network analysis combining functional genetic and large-scale interaction data can predict function of novel genes regulating development.
Collapse
Affiliation(s)
- Tarun Kumar
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
| | - Leo Blondel
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States
| | - Cassandra G Extavour
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.,Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States
| |
Collapse
|
28
|
Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
Collapse
Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| |
Collapse
|
29
|
Infante T, Del Viscovo L, De Rimini ML, Padula S, Caso P, Napoli C. Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease. J Atheroscler Thromb 2020; 27:279-302. [PMID: 31723086 PMCID: PMC7192819 DOI: 10.5551/jat.52407] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/24/2019] [Indexed: 12/13/2022] Open
Abstract
Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.
Collapse
Affiliation(s)
- Teresa Infante
- Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Luca Del Viscovo
- Department of Precision Medicine, Section of Diagnostic Imaging, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | | | - Sergio Padula
- Department of Cardiology, A.O.R.N. Dei Colli, Monaldi Hospital, Naples, Italy
| | - Pio Caso
- Department of Cardiology, A.O.R.N. Dei Colli, Monaldi Hospital, Naples, Italy
| | - Claudio Napoli
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
- IRCCS SDN, Naples, Italy
| |
Collapse
|
30
|
Cui H, Srinivasan S, Korkin D. Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases. Genes (Basel) 2019; 10:E933. [PMID: 31731769 PMCID: PMC6895925 DOI: 10.3390/genes10110933] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 11/04/2019] [Accepted: 11/11/2019] [Indexed: 11/16/2022] Open
Abstract
Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.
Collapse
Affiliation(s)
- Hongzhu Cui
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Suhas Srinivasan
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
| | - Dmitry Korkin
- Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA
- Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA;
- Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| |
Collapse
|
31
|
Cheng F, Lu W, Liu C, Fang J, Hou Y, Handy DE, Wang R, Zhao Y, Yang Y, Huang J, Hill DE, Vidal M, Eng C, Loscalzo J. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat Commun 2019; 10:3476. [PMID: 31375661 PMCID: PMC6677722 DOI: 10.1038/s41467-019-10744-6] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 05/26/2019] [Indexed: 01/28/2023] Open
Abstract
Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the 'precise' targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient's DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.
Collapse
Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, 311121, Hangzhou, China
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Diane E Handy
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ruisheng Wang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yuzheng Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, 200237, Shanghai, China
| | - Yi Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, 200237, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| |
Collapse
|
32
|
Lee LYH, Loscalzo J. Network Medicine in Pathobiology. THE AMERICAN JOURNAL OF PATHOLOGY 2019; 189:1311-1326. [PMID: 31014954 DOI: 10.1016/j.ajpath.2019.03.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/05/2019] [Indexed: 12/11/2022]
Abstract
The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.
Collapse
Affiliation(s)
| | - Joseph Loscalzo
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
| |
Collapse
|
33
|
Tu C, Niu M, Li C, Liu Z, He Q, Li R, Zhang Y, Xiao X, Wang J. Network pharmacology oriented study reveals inflammatory state-dependent dietary supplement hepatotoxicity responses in normal and diseased rats. Food Funct 2019; 10:3477-3490. [DOI: 10.1039/c8fo01974f] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Rhubarb, a well-used herbal and dietary supplement, has been widely used as a laxative in many countries.
Collapse
Affiliation(s)
- Can Tu
- School of Pharmacy
- Chengdu University of Traditional Chinese Medicine
- Chengdu 610000
- China
- China Military Institute of Chinese Medicine
| | - Ming Niu
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
| | - Chunyu Li
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital
- Chinese Academy of Medical Sciences and Peking Union Medical College
| | - Zhenjie Liu
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
| | - Qin He
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
| | - Ruisheng Li
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
| | - Yaming Zhang
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
| | - Xiaohe Xiao
- Integrative Medical Center for Liver Diseases
- Beijing 100039
- China
| | - Jiabo Wang
- China Military Institute of Chinese Medicine
- Beijing 100039
- China
| |
Collapse
|
34
|
Balligand JL. Phosphatase regulatory subunits in beta-adrenergic signalling: a delicate balancing act. Cardiovasc Res 2018; 115:477-478. [DOI: 10.1093/cvr/cvy275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain and Cliniques Universitaires Saint-Luc, Brussels, Belgium
| |
Collapse
|
35
|
Demchak B, Kreisberg JF, Fuxman Bass JI. Theory and Application of Network Biology Toward Precision Medicine. J Mol Biol 2018; 430:2873-2874. [DOI: 10.1016/j.jmb.2018.07.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|