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Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow. PLoS Comput Biol 2022; 18:e1010181. [PMID: 35639793 PMCID: PMC9216591 DOI: 10.1371/journal.pcbi.1010181] [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: 11/23/2021] [Revised: 06/22/2022] [Accepted: 05/07/2022] [Indexed: 12/03/2022] Open
Abstract
The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.
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2
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Fraser HC, Kuan V, Johnen R, Zwierzyna M, Hingorani AD, Beyer A, Partridge L. Biological mechanisms of aging predict age-related disease co-occurrence in patients. Aging Cell 2022; 21:e13524. [PMID: 35259281 PMCID: PMC9009120 DOI: 10.1111/acel.13524] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 10/07/2021] [Accepted: 11/12/2021] [Indexed: 11/27/2022] Open
Abstract
Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity.
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Affiliation(s)
- Helen C. Fraser
- Department of Genetics, Evolution and EnvironmentInstitute of Healthy AgeingUniversity College LondonLondonUK
| | - Valerie Kuan
- Institute of Health InformaticsUniversity College LondonLondonUK
- Health Data Research UK LondonUniversity College LondonLondonUK
- University College London British Heart Foundation Research AcceleratorLondonUK
| | - Ronja Johnen
- Cologne Excellence Cluster on Cellular Stress Responses in Aging‐Associated Diseases (CECAD)Medical Faculty & Faculty of Mathematics and Natural SciencesUniversity of CologneCologneGermany
| | | | - Aroon D. Hingorani
- Health Data Research UK LondonUniversity College LondonLondonUK
- University College London British Heart Foundation Research AcceleratorLondonUK
- Institute of Cardiovascular ScienceUniversity College LondonUK
| | - Andreas Beyer
- Cologne Excellence Cluster on Cellular Stress Responses in Aging‐Associated Diseases (CECAD)Medical Faculty & Faculty of Mathematics and Natural SciencesUniversity of CologneCologneGermany
- Centre for Molecular MedicineUniversity of CologneCologneGermany
| | - Linda Partridge
- Department of Genetics, Evolution and EnvironmentInstitute of Healthy AgeingUniversity College LondonLondonUK
- Max Planck Institute for Biology of AgeingCologneGermany
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3
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Newaz K, Milenkovic T. Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:974-988. [PMID: 32897864 DOI: 10.1109/tcbb.2020.3022767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Gene expression (GE)data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.)However, GE analyses ignore physical interactions between gene products, i.e., proteins. Because proteins function by interacting with each other, and because biological networks (BNs)capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP)to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related)subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP. Our data and code are available at https://nd.edu/~cone/dynetinf.
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4
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Huang Q, Wang J, Zhang X, Guo M, Yu G. IsoDA: Isoform-Disease Association Prediction by Multiomics Data Fusion. J Comput Biol 2021; 28:804-819. [PMID: 33826865 DOI: 10.1089/cmb.2020.0626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
A gene can be spliced into different isoforms by alternative splicing, which contributes to the functional diversity of protein species. Computational prediction of gene-disease associations (GDAs) has been studied for decades. However, the process of identifying the isoform-disease associations (IDAs) at a large scale is rarely explored, which can decipher the pathology at a more granular level. The main bottleneck is the lack of IDAs in current databases and the multilevel omics data fusion. To bridge this gap, we propose a computational approach called Isoform-Disease Association prediction by multiomics data fusion (IsoDA) to predict IDAs. Based on the relationship between a gene and its spliced isoforms, IsoDA first introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to their hosting genes. At the same time, it fuses the genome, transcriptome, and proteome data by joint matrix factorization to improve the prediction of IDAs. Experimental results show that IsoDA significantly outperforms the related state-of-the-art methods at both the gene level and isoform level. A case study further shows that IsoDA credibly identifies three isoforms spliced from apolipoprotein E, which have individual associations with Alzheimer's disease, and two isoforms spliced from vascular endothelial growth factor A, which have different associations with coronary heart disease. The codes of IsoDA are available at http://mlda.swu.edu.cn/codes.php?name=IsoDA.
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Affiliation(s)
- Qiuyue Huang
- College of Computer and Information Science, Southwest University, Chongqing, China.,School of Software, Shandong University, Jinan, China
| | - Jun Wang
- School of Software, Shandong University, Jinan, China
| | - Xiangliang Zhang
- Department of Computer Science, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Maozu Guo
- Department of Computer Science, College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Guoxian Yu
- College of Computer and Information Science, Southwest University, Chongqing, China.,School of Software, Shandong University, Jinan, China.,Department of Computer Science, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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5
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Barel G, Herwig R. NetCore: a network propagation approach using node coreness. Nucleic Acids Res 2020; 48:e98. [PMID: 32735660 PMCID: PMC7515737 DOI: 10.1093/nar/gkaa639] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/22/2020] [Accepted: 07/21/2020] [Indexed: 02/07/2023] Open
Abstract
We present NetCore, a novel network propagation approach based on node coreness, for phenotype–genotype associations and module identification. NetCore addresses the node degree bias in PPI networks by using node coreness in the random walk with restart procedure, and achieves improved re-ranking of genes after propagation. Furthermore, NetCore implements a semi-supervised approach to identify phenotype-associated network modules, which anchors the identification of novel candidate genes at known genes associated with the phenotype. We evaluated NetCore on gene sets from 11 different GWAS traits and showed improved performance compared to the standard degree-based network propagation using cross-validation. Furthermore, we applied NetCore to identify disease genes and modules for Schizophrenia GWAS data and pan-cancer mutation data. We compared the novel approach to existing network propagation approaches and showed the benefits of using NetCore in comparison to those. We provide an easy-to-use implementation, together with a high confidence PPI network extracted from ConsensusPathDB, which can be applied to various types of genomics data in order to obtain a re-ranking of genes and functionally relevant network modules.
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Affiliation(s)
- Gal Barel
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Ralf Herwig
- Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
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6
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NPF:network propagation for protein function prediction. BMC Bioinformatics 2020; 21:355. [PMID: 32787776 PMCID: PMC7430911 DOI: 10.1186/s12859-020-03663-7] [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: 02/29/2020] [Accepted: 07/14/2020] [Indexed: 11/29/2022] Open
Abstract
Background The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.
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7
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Sumathipala M, Weiss ST. Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data. Sci Rep 2020; 10:8705. [PMID: 32457435 PMCID: PMC7251138 DOI: 10.1038/s41598-020-65633-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 05/07/2020] [Indexed: 12/18/2022] Open
Abstract
With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease associations. The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.
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Affiliation(s)
- Marissa Sumathipala
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Harvard College, Cambridge, MA, USA.
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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8
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Bersanelli M, Mosca E, Milanesi L, Bazzani A, Castellani G. Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach. Sci Rep 2020; 10:2643. [PMID: 32060296 PMCID: PMC7021762 DOI: 10.1038/s41598-020-59036-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 11/22/2019] [Indexed: 11/13/2022] Open
Abstract
In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.
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Affiliation(s)
- Matteo Bersanelli
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy. .,National Institute for Nuclear Physics (INFN), Bologna, 40127, Italy.
| | - Ettore Mosca
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, 20090, Italy
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, 20090, Italy
| | - Armando Bazzani
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
| | - Gastone Castellani
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
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9
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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.
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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
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10
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Sumathipala M, Maiorino E, Weiss ST, Sharma A. Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION. Front Physiol 2019; 10:888. [PMID: 31379598 PMCID: PMC6646690 DOI: 10.3389/fphys.2019.00888] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Accepted: 06/26/2019] [Indexed: 11/13/2022] Open
Abstract
Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the LncRNA rankIng by NetwOrk DiffusioN (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein–protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION’s accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.
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Affiliation(s)
- Marissa Sumathipala
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Harvard College, Cambridge, MA, United States
| | - Enrico Maiorino
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Medicine, Harvard Medical School, Boston, MA, United States.,Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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11
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Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 2018; 6:484-495.e5. [PMID: 29605183 DOI: 10.1016/j.cels.2018.03.001] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/19/2017] [Accepted: 02/28/2018] [Indexed: 12/27/2022]
Abstract
Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.
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Abstract
Network propagation is a powerful tool for genetic analysis which is widely used to identify genes and genetic modules that underlie a process of interest. Here we provide a graphical, web-based platform (http://anat.cs.tau.ac.il/WebPropagate/) in which researchers can easily apply variants of this method to data sets of interest using up-to-date networks of protein-protein interactions in several organisms.
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Affiliation(s)
- Hadas Biran
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tovi Almozlino
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Martin Kupiec
- Department of Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv 69978, Israel
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
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13
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Carlin DE, Demchak B, Pratt D, Sage E, Ideker T. Network propagation in the cytoscape cyberinfrastructure. PLoS Comput Biol 2017; 13:e1005598. [PMID: 29023449 PMCID: PMC5638226 DOI: 10.1371/journal.pcbi.1005598] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 05/29/2017] [Indexed: 01/15/2023] Open
Abstract
Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.
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Affiliation(s)
- Daniel E. Carlin
- Department of Medicine, University of California-San Diego, San Diego, California, United States of America
- * E-mail:
| | - Barry Demchak
- Department of Medicine, University of California-San Diego, San Diego, California, United States of America
| | - Dexter Pratt
- Department of Medicine, University of California-San Diego, San Diego, California, United States of America
| | - Eric Sage
- Department of Medicine, University of California-San Diego, San Diego, California, United States of America
| | - Trey Ideker
- Department of Medicine, University of California-San Diego, San Diego, California, United States of America
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Le Morvan M, Zinovyev A, Vert JP. NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis. PLoS Comput Biol 2017; 13:e1005573. [PMID: 28650955 PMCID: PMC5507468 DOI: 10.1371/journal.pcbi.1005573] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 07/11/2017] [Accepted: 05/15/2017] [Indexed: 01/01/2023] Open
Abstract
Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.
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Affiliation(s)
- Marine Le Morvan
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 75006 Paris, France
- Institut Curie, 75248 Paris Cedex 5, France
- INSERM, U900, 75248 Paris Cedex 5, France
| | - Andrei Zinovyev
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 75006 Paris, France
- Institut Curie, 75248 Paris Cedex 5, France
- INSERM, U900, 75248 Paris Cedex 5, France
| | - Jean-Philippe Vert
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 75006 Paris, France
- Institut Curie, 75248 Paris Cedex 5, France
- INSERM, U900, 75248 Paris Cedex 5, France
- Department of Mathematics and Applications, Ecole normale supérieure, CNRS, PSL Research University, 75005 Paris, France
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15
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Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules. Sci Rep 2016; 6:34841. [PMID: 27731320 PMCID: PMC5059623 DOI: 10.1038/srep34841] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 08/19/2016] [Indexed: 11/08/2022] Open
Abstract
A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD.
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Zhou H, Skolnick J. A knowledge-based approach for predicting gene-disease associations. Bioinformatics 2016; 32:2831-8. [PMID: 27283949 DOI: 10.1093/bioinformatics/btw358] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 05/31/2016] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat deadly diseases and provide possible targets to cure these diseases. Genome-wide association studies (GWAS) have identified many individual genes associated with common diseases. To exploit the large amount of data obtained from GWAS studies and leverage our understanding of common as well as rare diseases, we have developed a knowledge-based approach to predict gene-disease associations. We first derive gene-gene mutual information by utilizing the cooccurrence of genes in known gene-disease association data. Subsequently, the mutual information is combined with known protein-protein interaction networks by a boosted tree regression method. RESULTS The method called Know-GENE is compared with the method of random walking on the heterogeneous network using the same input data. For a set of 960 diseases, using the same training data in testing in 3-fold cross-validation, the average recall rate within the top ranked 100 genes by Know-GENE is 65.0% compared with 37.9% by the state of the art random walking on heterogeneous network. This significant improvement is mostly due to the inclusion of knowledge-based mutual information. AVAILABILITY AND IMPLEMENTATION Predictions for genes associated with the 960 diseases are available at http://cssb2.biology.gatech.edu/knowgene CONTACT : skolnick@gatech.edu.
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Affiliation(s)
- Hongyi Zhou
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jeffrey Skolnick
- School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Castellani GC, Menichetti G, Garagnani P, Giulia Bacalini M, Pirazzini C, Franceschi C, Collino S, Sala C, Remondini D, Giampieri E, Mosca E, Bersanelli M, Vitali S, Valle IFD, Liò P, Milanesi L. Systems medicine of inflammaging. Brief Bioinform 2016; 17:527-40. [PMID: 26307062 PMCID: PMC4870395 DOI: 10.1093/bib/bbv062] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Revised: 06/29/2015] [Indexed: 12/30/2022] Open
Abstract
Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging.
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Mosca E, Alfieri R, Milanesi L. Diffusion of information throughout the host interactome reveals gene expression variations in network proximity to target proteins of hepatitis C virus. PLoS One 2014; 9:e113660. [PMID: 25461596 PMCID: PMC4251971 DOI: 10.1371/journal.pone.0113660] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 10/27/2014] [Indexed: 12/22/2022] Open
Abstract
Hepatitis C virus infection is one of the most common and chronic in the world, and hepatitis associated with HCV infection is a major risk factor for the development of cirrhosis and hepatocellular carcinoma (HCC). The rapidly growing number of viral-host and host protein-protein interactions is enabling more and more reliable network-based analyses of viral infection supported by omics data. The study of molecular interaction networks helps to elucidate the mechanistic pathways linking HCV molecular activities and the host response that modulates the stepwise hepatocarcinogenic process from preneoplastic lesions (cirrhosis and dysplasia) to HCC. Simulating the impact of HCV-host molecular interactions throughout the host protein-protein interaction (PPI) network, we ranked the host proteins in relation to their network proximity to viral targets. We observed that the set of proteins in the neighborhood of HCV targets in the host interactome is enriched in key players of the host response to HCV infection. In opposition to HCV targets, subnetworks of proteins in network proximity to HCV targets are significantly enriched in proteins reported as differentially expressed in preneoplastic and neoplastic liver samples by two independent studies. Using multi-objective optimization, we extracted subnetworks that are simultaneously “guilt-by-association” with HCV proteins and enriched in proteins differentially expressed. These subnetworks contain established, recently proposed and novel candidate proteins for the regulation of the mechanisms of liver cells response to chronic HCV infection.
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Affiliation(s)
- Ettore Mosca
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy
- * E-mail:
| | - Roberta Alfieri
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, Italy
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