51
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Nahálková J. Linking TPPII to the protein interaction and signalling networks. Comput Biol Chem 2020; 87:107291. [PMID: 32702546 DOI: 10.1016/j.compbiolchem.2020.107291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 04/21/2020] [Accepted: 05/22/2020] [Indexed: 01/18/2023]
Abstract
Tripeptidyl peptidase II (TPPII) is primarily considered a house-keeping exopeptidase, which contributes to the functions of the ubiquitin-proteasome system by the maintenance of the cellular amino acid homeostasis. Although functionally well-characterised in vitro and using the mammalian cell models, less is known about the molecular mechanisms of its involvement in the signalling and metabolic pathways, which mediate its cellular functions. The present protein-protein interaction network analysis identified these mechanisms involved in the adaptive and innate immunity, the metabolism of the glucose, cancer cell growth, apoptosis, cell cycle and DNA damage responses. The interaction network constructed based on the publicly available protein-protein interaction data was extended by the application GeneMania, which was further used for the pathway enrichment, the protein function prediction and the protein node prioritisation analysis. The analysis suggested that the molecular mechanisms linked to the adaptive and innate immunity (ID, Kit receptor, BCR, IL-2 and G-CSF signalling; the regulation of NFκB), the aerobic glycolysis (ID and IL-2 signalling), tumorigenesis (TGF-β and p53 signalling; the top priority nodes MAPKs, mTOR regulation), diabetes (Kit receptor signalling; the top priority node GSK3β) and neurodegeneration (the control of mTOR and Aβ peptide degradation) are controlling the resulting TPPII interaction network. The uncharacterized interactions with two lung cancer suppressors (DOK3, DENND2D), a protein involved in the increased risk of the lung cancer in smokers (CYP1A1) and a protein implicated in asthmatic reactions (CHIA) suggest potential roles of TPPII in the lung cancer pathology. The interactions with methyltransferase CARNMT1, which modifies di- and tripeptides and the xenobiotic processing enzyme CYP1A1, are additional candidates for the breakthrough in new functions discovery of TPPII.
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Affiliation(s)
- Jarmila Nahálková
- Biochemworld Co., Biochemistry, Molecular & Cell Biology Unit, Snickar-Anders väg 17, 74394, Skyttorp, Uppsala County, Sweden.
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52
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Bashiri H, Rahmani H, Bashiri V, Módos D, Bender A. EMDIP: An Entropy Measure to Discover Important Proteins in PPI networks. Comput Biol Med 2020; 120:103740. [PMID: 32421645 DOI: 10.1016/j.compbiomed.2020.103740] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 03/30/2020] [Accepted: 03/30/2020] [Indexed: 12/24/2022]
Abstract
Discovering important proteins in Protein-Protein Interaction (PPI) networks has attracted a lot of attention in recent years. Most of the previous work applies different network centrality measures such as Closeness, Betweenness, PageRank and many others to discover the most influential proteins in PPI networks. Although entropy is a well-known graph-based method in computer science, according to our knowledge, it is not used in the biology domain for this purpose. In this paper, first, we annotate the human PPI network with available annotation data. Second, we introduce a new concept called annotation-context that describes each protein according to annotation data of its neighbors. Third, we apply an entropy measure to discover proteins with varied annotation-context. Empirical results indicate that our proposed method succeeded in (1) differentiating essential and non-essential proteins in PPI networks with annotation data; (2) outperforming centrality measures in the task of discovering essential nodes; (3) predicting new annotated proteins based on existing annotation data.
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Affiliation(s)
- Hamid Bashiri
- School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran
| | - Hossein Rahmani
- School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
| | - Vahid Bashiri
- School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran
| | - Dezső Módos
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
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53
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Liu L, Huang X, Mamitsuka H, Zhu S. HPOLabeler: improving prediction of human protein–phenotype associations by learning to rank. Bioinformatics 2020; 36:4180-4188. [DOI: 10.1093/bioinformatics/btaa284] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 04/05/2020] [Accepted: 04/30/2020] [Indexed: 12/23/2022] Open
Abstract
Abstract
Motivation
Annotating human proteins by abnormal phenotypes has become an important topic. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities encountered in human diseases. As of November 2019, only <4000 proteins have been annotated with HPO. Thus, a computational approach for accurately predicting protein–HPO associations would be important, whereas no methods have outperformed a simple Naive approach in the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2).
Results
We present HPOLabeler, which is able to use a wide variety of evidence, such as protein–protein interaction (PPI) networks, Gene Ontology, InterPro, trigram frequency and HPO term frequency, in the framework of learning to rank (LTR). LTR has been proved to be powerful for solving large-scale, multi-label ranking problems in bioinformatics. Given an input protein, LTR outputs the ranked list of HPO terms from a series of input scores given to the candidate HPO terms by component learning models (logistic regression, nearest neighbor and a Naive method), which are trained from given multiple evidence. We empirically evaluate HPOLabeler extensively through mainly two experiments of cross validation and temporal validation, for which HPOLabeler significantly outperformed all component models and competing methods including the current state-of-the-art method. We further found that (i) PPI is most informative for prediction among diverse data sources and (ii) low prediction performance of temporal validation might be caused by incomplete annotation of new proteins.
Availability and implementation
http://issubmission.sjtu.edu.cn/hpolabeler/.
Contact
zhusf@fudan.edu.cn
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lizhi Liu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing
- Shanghai Institute of Artificial Intelligence Algorithms and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaodi Huang
- School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Shanfeng Zhu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing
- Shanghai Institute of Artificial Intelligence Algorithms and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Science, Chinese Academy of Sciences, Shanghai 200031, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
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54
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Peng J, Zhu L, Wang Y, Chen J. Mining Relationships among Multiple Entities in Biological Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:769-776. [PMID: 30872239 DOI: 10.1109/tcbb.2019.2904965] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Identifying topological relationships among multiple entities in biological networks is critical towards the understanding of the organizational principles of network functionality. Theoretically, this problem can be solved using minimum Steiner tree (MSTT) algorithms. However, due to large network size, it remains to be computationally challenging, and the predictive value of multi-entity topological relationships is still unclear. We present a novel solution called Cluster-based Steiner Tree Miner (CST-Miner) to instantly identify multi-entity topological relationships in biological networks. Given a list of user-specific entities, CST-Miner decomposes a biological network into nested cluster-based subgraphs, on which multiple minimum Steiner trees are identified. By merging all of them into a minimum cost tree, the optimal topological relationships among all the user-specific entities are revealed. Experimental results showed that CST-Miner can finish in nearly log-linear time and the tree constructed by CST-Miner is close to the global minimum.
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55
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Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
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56
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Amanatidou AI, Nastou KC, Tsitsilonis OE, Iconomidou VA. Visualization and analysis of the interaction network of proteins associated with blood-cell targeting autoimmune diseases. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165714. [DOI: 10.1016/j.bbadis.2020.165714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/21/2020] [Accepted: 01/31/2020] [Indexed: 12/17/2022]
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57
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Liu H, Guan J, Li H, Bao Z, Wang Q, Luo X, Xue H. Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning. Front Genet 2020; 11:328. [PMID: 32373160 PMCID: PMC7186413 DOI: 10.3389/fgene.2020.00328] [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: 01/23/2020] [Accepted: 03/19/2020] [Indexed: 02/02/2023] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease for which it is difficult to find exact disease-related genes. Effectively identifying disease-related genes would contribute to improving the treatment and diagnosis of multiple sclerosis. Current methods for identifying disease-related genes mainly focus on the hypothesis of guilt-by-association and pay little attention to the global topological information of the whole protein-protein-interaction (PPI) network. Besides, network representation learning (NRL) has attracted a huge amount of attention in the area of network analysis because of its promising performance in node representation and many downstream tasks. In this paper, we try to introduce NRL into the task of disease-related gene prediction and propose a novel framework for identifying the disease-related genes multiple sclerosis. The proposed framework contains three main steps: capturing the topological structure of the PPI network using NRL-based methods, encoding learned features into low-dimensional space using a stacked autoencoder, and training a support vector machine (SVM) classifier to predict disease-related genes. Compared with three state-of-the-art algorithms, our proposed framework shows superior performance on the task of predicting disease-related genes of multiple sclerosis.
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Affiliation(s)
- Haijie Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Department of Physical Medicine and Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
- Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School Charlestown, Boston, MA, United States
| | - Jiaojiao Guan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - He Li
- Department of Automation, College of Information Science and Engineering, Tianjin Tianshi College, Tianjin, China
| | - Zhijie Bao
- School of Textile Science and Engineering, Tiangong University, Tianjin, China
| | - Qingmei Wang
- Stroke Biological Recovery Laboratory, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School Charlestown, Boston, MA, United States
| | - Xun Luo
- Kerry Rehabilitation Medicine Research Institute, Shenzhen, China
- Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, China
| | - Hansheng Xue
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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58
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Fan P, Qi X, Sweet RA, Wang L. Network Systems Pharmacology-Based Mechanism Study on the Beneficial Effects of Vitamin D against Psychosis in Alzheimer's Disease. Sci Rep 2020; 10:6136. [PMID: 32273551 PMCID: PMC7145835 DOI: 10.1038/s41598-020-63021-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 03/21/2020] [Indexed: 11/08/2022] Open
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease with significant financial costs and negative impacts on quality of life. Psychotic symptoms, i.e., the presence of delusions and/or hallucinations, is a frequent complication of AD. About 50% of AD patients will develop psychotic symptoms (AD with Psychosis, or AD + P) and these patients will experience an even more rapid cognitive decline than AD patients without psychosis (AD-P). In a previous analysis on medication records of 776 AD patients, we had shown that use of Vitamin D was associated with delayed time to psychosis in AD patients and Vitamin D was used more by AD-P than AD + P patients. To explore the potential molecular mechanism behind our findings, we applied systems pharmacology approaches to investigate the crosstalk between AD and psychosis. Specifically, we built protein-protein interaction (PPI) networks with proteins encoded by AD- and psychosis-related genes and Vitamin D-perturbed genes. Using network analysis we identified several high-impact genes, including NOTCH4, COMT, CACNA1C and DRD3 which are related to calcium homeostasis. The new findings highlight the key role of calcium-related signaling pathways in AD + P development and may provide a new direction and facilitate hypothesis generation for future drug development.
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Affiliation(s)
- Peihao Fan
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, USA
| | - Xiguang Qi
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, USA
| | - Robert A Sweet
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, USA.
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Lirong Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, Pittsburgh, USA.
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59
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Yu X, Lai S, Chen H, Chen M. Protein–protein interaction network with machine learning models and multiomics data reveal potential neurodegenerative disease-related proteins. Hum Mol Genet 2020; 29:1378-1387. [DOI: 10.1093/hmg/ddaa065] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/22/2019] [Accepted: 04/01/2020] [Indexed: 12/18/2022] Open
Abstract
AbstractResearch of protein–protein interaction in several model organisms is accumulating since the development of high-throughput experimental technologies and computational methods. The protein–protein interaction network (PPIN) is able to examine biological processes in a systematic manner and has already been used to predict potential disease-related proteins or drug targets. Based on the topological characteristics of the PPIN, we investigated the application of the random forest classification algorithm to predict proteins that may cause neurodegenerative disease, a set of pathological changes featured by protein malfunction. By integrating multiomics data, we further showed the validity of our machine learning model and narrowed down the prediction results to several hub proteins that play essential roles in the PPIN. The novel insights into neurodegeneration pathogenesis brought by this computational study can indicate promising directions for future experimental research.
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Affiliation(s)
- Xinjian Yu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Siqi Lai
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongjun Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, China
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60
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Ghantous Y, Nashef A, Abu-Elnaaj I. Epigenetic Alterations Associated with the Overall Survival and Recurrence Free Survival among Oral Squamous Cell Carcinoma Patients. J Clin Med 2020; 9:E1035. [PMID: 32272578 PMCID: PMC7231254 DOI: 10.3390/jcm9041035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 03/25/2020] [Accepted: 04/03/2020] [Indexed: 12/11/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is a fatal disease caused by complex interactions between environmental, genomic, and epigenetic alterations. In the current study, we aimed to identify clusters of genes whose promoter methylation status correlated with various tested clinical features. Molecular datasets of genetic and methylation analysis based on whole-genome sequencing of 159 OSCC patients were obtained from the The Cancer Genome Atlas (TCGA) data portal. Genes were clustered based on their methylation status and were tested for their association with demographic, pathological, and clinical features of the patients. Overall, seven clusters of genes were revealed that showed a significant association with the overall survival/recurrence free survival of patients. The top ranked genes within cluster 4, which showed the worst prognosis, primarily acted as paraneoplastic genes, while the genes within cluster 6 primarily acted as anti-tumor genes. A significant difference was found regarding the mean age in the different clusters. No significant correlation was found between the tumor staging and the different clusters. In conclusion, our result provided a proof-of-principle for the existence of phenotypic diversity among the epigenetic clusters of OSCC and demonstrated the utility of the use epigenetics alterations in devolving new prognostic and therapeutics tools for OSCC patients.
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Affiliation(s)
- Yasmen Ghantous
- Department of Oral and Maxillofacial Surgery, Baruch Padeh medical center Poriya, The lower Galilee 15208, Israel;
| | - Aysar Nashef
- Department of Oral and Maxillofacial Surgery, Baruch Padeh medical center Poriya, The lower Galilee 15208, Israel;
| | - Imad Abu-Elnaaj
- Department of Oral and Maxillofacial Surgery, Baruch Padeh medical center Poriya, The lower Galilee 15208, Israel;
- The Azrieli Faculty of Medicine, Bar Illan University, Safed 1311502, Israel;
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61
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Wang S, Wang W, Wang W, Xia P, Yu L, Lu Y, Chen X, Xu C, Liu H. Context-Specific Coordinately Regulatory Network Prioritize Breast Cancer Genetic Risk Factors. Front Genet 2020; 11:255. [PMID: 32273883 PMCID: PMC7113376 DOI: 10.3389/fgene.2020.00255] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 03/03/2020] [Indexed: 12/16/2022] Open
Abstract
Breast cancer (BC) is one of the most common tumors, leading the causes of cancer death in women. However, the pathogenesis of BC still remains unclear, and the atlas of BC-associated risk factors is far from complete. In this study, we constructed a BC-specific coordinately regulatory network (CRN) to prioritize potential BC-associated protein-coding genes (PCGs) and non-coding RNAs (ncRNAs). We integrated 813 BC sample transcriptome data from The Cancer Genome Atlas (TCGA) and eight types of regulatory relationships to construct BC-specific CRN, including 387 transcription factors (TFs), 174 microRNAs (miRNAs), 407 long non-coding RNAs (lncRNAs), and 905 PCGs. After that, the random walk with restart (RWR) method was performed on the CRN by using the known BC-associated factors as seeds, and potential BC-associated risk factors were prioritized. The leave-one-out cross-validation (LOOCV) was utilized on the BC-specific CRN and achieved an area under the curve (AUC) of 0.92. The performances of common CRN, common protein-protein interaction (PPI) network, and BC-specific PPI network were also evaluated, demonstrating that the context-specific CRN prioritizes BC risk factors. Functional analysis for the top 100-ranked risk factors in the candidate list revealed that these factors were significantly enriched in cancer-related functions and had significant semantic similarity with BC-related gene ontology (GO) terms. Differential expression analysis and survival analysis proved that the prioritized risk factors significantly associated with BC progression and prognosis. In total, we provided a computational method to predict reliable BC-associated risk factors, which would help improve the understanding of the pathology of BC and benefit disease diagnosis and prognosis.
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Affiliation(s)
- Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wencan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Weida Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Peng Xia
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lei Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ye Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xiaowen Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hui Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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62
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Li P, Guo M, Sun B. Integration of multi-omics data to mine cancer-related gene modules. J Bioinform Comput Biol 2020; 17:1950038. [PMID: 32019413 DOI: 10.1142/s0219720019500380] [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: 01/31/2023]
Abstract
The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.
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Affiliation(s)
- Peng Li
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China.,School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, P. R. China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, P. R. China
| | - Bo Sun
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China
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63
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The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins. J Theor Biol 2020; 487:110124. [DOI: 10.1016/j.jtbi.2019.110124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 01/12/2023]
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64
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Yang K, Wang R, Liu G, Shu Z, Wang N, Zhang R, Yu J, Chen J, Li X, Zhou X. HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction. IEEE J Biomed Health Inform 2020; 23:1805-1815. [PMID: 31283472 DOI: 10.1109/jbhi.2018.2870728] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The discovery of disease-causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making full use of disease-related (e.g., symptoms) and gene-related (e.g., gene ontology and protein-protein interactions) information to improve the performance of disease gene prediction is still an issue. Here, we develop a heterogeneous disease-gene-related network (HDGN) embedding representation framework for disease gene prediction (called HerGePred). Based on this framework, a low-dimensional vector representation (LVR) of the nodes in the HDGN can be obtained. Then, we propose two specific algorithms, namely, an LVR-based similarity prediction and a random walk with restart on a reconstructed heterogeneous disease-gene network (RW-RDGN), to predict disease genes with high performance. First, to validate the rationality of the framework, we analyze the similarity-based overlap distribution of disease pairs and design an experiment for disease-gene association recovery, the results of which revealed that the LVR of nodes performs well at preserving the local and global network structure of the HDGN. Then, we apply tenfold cross validation and external validation to compare our methods with other well-known disease gene prediction algorithms. The experimental results show that the RW-RDGN performs better than the state-of-the-art algorithm. The prediction results of disease candidate genes are essential for molecular mechanism investigation and experimental validation. The source codes of HerGePred and experimental data are available at https://github.com/yangkuoone/HerGePred.
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65
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Mao Y, Fisher DW, Yang S, Keszycki RM, Dong H. Protein-protein interactions underlying the behavioral and psychological symptoms of dementia (BPSD) and Alzheimer's disease. PLoS One 2020; 15:e0226021. [PMID: 31951614 PMCID: PMC6968845 DOI: 10.1371/journal.pone.0226021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022] Open
Abstract
Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder currently affecting 45 million people worldwide, ranking as the 6th highest cause of death. Throughout the development and progression of AD, over 90% of patients display behavioral and psychological symptoms of dementia (BPSD), with some of these symptoms occurring before memory deficits and therefore serving as potential early predictors of AD-related cognitive decline. However, the biochemical links between AD and BPSD are not known. In this study, we explored the molecular interactions between AD and BPSD using protein-protein interaction (PPI) networks built from OMIM (Online Mendelian Inheritance in Man) genes that were related to AD and two distinct BPSD domains, the Affective Domain and the Hyperactivity, Impulsivity, Disinhibition, and Aggression (HIDA) Domain. Our results yielded 8 unique proteins for the Affective Domain (RHOA, GRB2, PIK3R1, HSPA4, HSP90AA1, GSK3beta, PRKCZ, and FYN), 5 unique proteins for the HIDA Domain (LRP1, EGFR, YWHAB, SUMO1, and EGR1), and 6 shared proteins between both BPSD domains (APP, UBC, ELAV1, YWHAZ, YWHAE, and SRC) and AD. These proteins might suggest specific targets and pathways that are involved in the pathogenesis of these BPSD domains in AD.
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Affiliation(s)
- Yimin Mao
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
- Applied Science Institute, Jiangxi University of Science and Technology, Jiangxi, China
| | - Daniel W. Fisher
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Shuxing Yang
- School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
| | - Rachel M. Keszycki
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Hongxin Dong
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
- * E-mail:
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66
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Das D, Krishnan SR, Roy A, Bulusu G. A network-based approach reveals novel invasion and Maurer's clefts-related proteins in Plasmodium falciparum. Mol Omics 2019; 15:431-441. [PMID: 31631203 DOI: 10.1039/c9mo00124g] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Malaria continues to be a major concern in developing countries despite continuous efforts to find a cure for the disease. Understanding the pathogenesis mechanism is necessary to identify more effective drug targets against malaria. Many years of experimental research have generated a large amount of data for the malarial parasite, Plasmodium falciparum. These data are useful to understand the importance of certain parasite proteins, but it often remains unclear how these proteins come together, interact with other proteins and carry out their function. Identification of all proteins involved in pathogenesis is an important step towards understanding the molecular mechanism of pathogenesis. In this study, dynamic stage-specific protein-protein interaction networks were created based on gene expression data during the parasite's intra-erythrocytic stages and static protein-protein interaction data. Using previously known proteins of a biological event as seed proteins, the random walk with restart (RWR) method was used on the dynamic protein-protein interaction networks to identify novel proteins related to that event. Two screening procedures namely, permutation test and GO enrichment test were performed to increase the reliability of the RWR predictions. The proposed method was first validated on Plasmodium falciparum proteins related to invasion, where it could reproduce the existing knowledge from a small set of seed proteins. It was then used to identify novel Maurer's clefts resident proteins, where it could identify 152 parasite proteins. We show that the current approach can annotate conserved proteins with unknown function. The predicted proteins can help build a mechanistic model for disease pathogenesis, which will be useful in identifying new drug targets.
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Affiliation(s)
- Dibyajyoti Das
- TCS Innovation Labs - Hyderabad (Life Sciences Division), Tata Consultancy Services Limited, Hyderabad, India.
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Afiqah-Aleng N, Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA. Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome. Reprod Biomed Online 2019; 40:319-330. [PMID: 32001161 DOI: 10.1016/j.rbmo.2019.11.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/07/2019] [Accepted: 11/25/2019] [Indexed: 12/18/2022]
Abstract
RESEARCH QUESTION Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach? METHODS Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways. RESULTS The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity. CONCLUSIONS These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.
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Affiliation(s)
- Nor Afiqah-Aleng
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; Institute of Marine Biotechnology, Universiti Malaysia Terengganu (UMT), 21030 Kuala Nerus, Terengganu, Malaysia
| | - M Altaf-Ul-Amin
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara 630-0192, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara 630-0192, Japan
| | - Zeti-Azura Mohamed-Hussein
- Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia; Centre for Frontier Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
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68
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Zhang W, Lei Ieee Member X, Bian C. Identifying Cancer genes by combining two-rounds RWR based on multiple biological data. BMC Bioinformatics 2019; 20:518. [PMID: 31760937 PMCID: PMC6876101 DOI: 10.1186/s12859-019-3123-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background It’s a very urgent task to identify cancer genes that enables us to understand the mechanisms of biochemical processes at a biomolecular level and facilitates the development of bioinformatics. Although a large number of methods have been proposed to identify cancer genes at recent times, the biological data utilized by most of these methods is still quite less, which reflects an insufficient consideration of the relationship between genes and diseases from a variety of factors. Results In this paper, we propose a two-rounds random walk algorithm to identify cancer genes based on multiple biological data (TRWR-MB), including protein-protein interaction (PPI) network, pathway network, microRNA similarity network, lncRNA similarity network, cancer similarity network and protein complexes. In the first-round random walk, all cancer nodes, cancer-related genes, cancer-related microRNAs and cancer-related lncRNAs, being associated with all the cancer, are used as seed nodes, and then a random walker walks on a quadruple layer heterogeneous network constructed by multiple biological data. The first-round random walk aims to select the top score k of potential cancer genes. Then in the second-round random walk, genes, microRNAs and lncRNAs, being associated with a certain special cancer in corresponding cancer class, are regarded as seed nodes, and then the walker walks on a new quadruple layer heterogeneous network constructed by lncRNAs, microRNAs, cancer and selected potential cancer genes. After the above walks finish, we combine the results of two-rounds RWR as ranking score for experimental analysis. As a result, a higher value of area under the receiver operating characteristic curve (AUC) is obtained. Besides, cases studies for identifying new cancer genes are performed in corresponding section. Conclusion In summary, TRWR-MB integrates multiple biological data to identify cancer genes by analyzing the relationship between genes and cancer from a variety of biological molecular perspective.
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Affiliation(s)
- Wenxiang Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | | | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
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Nam Y, Lee DG, Bang S, Kim JH, Kim JH, Shin H. The translational network for metabolic disease - from protein interaction to disease co-occurrence. BMC Bioinformatics 2019; 20:576. [PMID: 31722666 PMCID: PMC6854734 DOI: 10.1186/s12859-019-3106-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 09/20/2019] [Indexed: 02/08/2023] Open
Abstract
Background The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing ‘n-of-1 utility’ (n potential diseases of one patient) to human disease network—the translational disease network. Results We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. Conclusions The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.
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Affiliation(s)
- Yonghyun Nam
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea
| | - Dong-Gi Lee
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea
| | - Sunjoo Bang
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea
| | - Ju Han Kim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jae-Hoon Kim
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.
| | - Hyunjung Shin
- Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.
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Su L, Liu G, Wang J, Xu D. A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions. Methods 2019; 166:22-30. [PMID: 31121299 PMCID: PMC6708461 DOI: 10.1016/j.ymeth.2019.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/14/2019] [Accepted: 05/13/2019] [Indexed: 12/12/2022] Open
Abstract
Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.
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Affiliation(s)
- Lingtao Su
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Guixia Liu
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Juexin Wang
- Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Electrical Engineering & Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
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Conte F, Fiscon G, Licursi V, Bizzarri D, D'Antò T, Farina L, Paci P. A paradigm shift in medicine: A comprehensive review of network-based approaches. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194416. [PMID: 31382052 DOI: 10.1016/j.bbagrm.2019.194416] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 07/19/2019] [Accepted: 07/28/2019] [Indexed: 02/01/2023]
Abstract
Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
| | - Valerio Licursi
- Biology and Biotechnology Department "Charles Darwin" (BBCD), Sapienza University of Rome, Rome, Italy
| | - Daniele Bizzarri
- Department of Internal Medicine and Medical Specialties, Sapienza University of Rome, Rome, Italy
| | - Tommaso D'Antò
- 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
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
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Ramsahai E, Tripathi V, John M. Cancer driver genes: a guilty by resemblance doctrine. PeerJ 2019; 7:e6979. [PMID: 31275738 PMCID: PMC6598669 DOI: 10.7717/peerj.6979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 04/16/2019] [Indexed: 11/30/2022] Open
Abstract
A major benefit of expansive cancer genome projects is the discovery of new targets for drug treatment and development. To date, cancer driver genes have been primarily identified by methods based on gene mutation frequency. This approach fails to identify culpable genes that are not mutated, rarely mutated, or contribute to the development of rare forms of cancer. Due to the complexity of the disease and the sheer volume of data, computational methods may encounter a NP-complete problem. We have developed a novel pathway and reach (PAR) method that employs a guilty by resemblance approach to identify cancer driver genes that avoids the above problems. Essentially PAR sifts through a list of genes of biological pathways to find those that are common to the same pathways and possess a similar 2-reach topology metric as a reference set of recognized driver genes. This approach leads to faster processing times and eliminates any dependency on gene mutation frequency. Out of the three pathways, signal transduction, immune system, and gene expression, a set of 50 candidate driver genes were identified, 30 of which were new. The top five were HGF, E2F1, C6, MIF, and CDK2.
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Affiliation(s)
- Emilie Ramsahai
- Department of Mathematics and Statistics, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Vrijesh Tripathi
- Department of Mathematics and Statistics, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Melford John
- Department of Preclinical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
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Cáceres JJ, Paccanaro A. Disease gene prediction for molecularly uncharacterized diseases. PLoS Comput Biol 2019; 15:e1007078. [PMID: 31276496 PMCID: PMC6636748 DOI: 10.1371/journal.pcbi.1007078] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 07/17/2019] [Accepted: 05/09/2019] [Indexed: 02/06/2023] Open
Abstract
Network medicine approaches have been largely successful at increasing our knowledge of molecularly characterized diseases. Given a set of disease genes associated with a disease, neighbourhood-based methods and random walkers exploit the interactome allowing the prediction of further genes for that disease. In general, however, diseases with no known molecular basis constitute a challenge. Here we present a novel network approach to prioritize gene-disease associations that is able to also predict genes for diseases with no known molecular basis. Our method, which we have called Cardigan (ChARting DIsease Gene AssociatioNs), uses semi-supervised learning and exploits a measure of similarity between disease phenotypes. We evaluated its performance at predicting genes for both molecularly characterized and uncharacterized diseases in OMIM, using both weighted and binary interactomes, and compared it with state-of-the-art methods. Our tests, which use datasets collected at different points in time to replicate the dynamics of the disease gene discovery process, prove that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases. Additionally, standard leave-one-out cross validation tests show how our approach outperforms state-of-the-art methods at predicting genes for molecularly characterized diseases by 14%-65%. Cardigan can also be used for disease module prediction, where it outperforms state-of-the-art methods by 87%-299%.
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Affiliation(s)
- Juan J. Cáceres
- Centre for Systems and Synthetic Biology & Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Alberto Paccanaro
- Centre for Systems and Synthetic Biology & Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
- * E-mail:
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Yan J, Risacher SL, Shen L, Saykin AJ. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 2019; 19:1370-1381. [PMID: 28679163 DOI: 10.1093/bib/bbx066] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Indexed: 11/14/2022] Open
Abstract
In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.
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Affiliation(s)
- Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, USA
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Yang CW, Cao HH, Guo Y, Feng YM, Zhang N. Identification of Novel Breast Cancer Genes based on Gene Expression Profiles and PPI Data. CURR PROTEOMICS 2019. [DOI: 10.2174/1570164616666190126111354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Breast cancer is one of the most common malignancies, and a threat to female health all over the world. However, the molecular mechanism of breast cancer has not been fully discovered yet.Objective:It is crucial to identify breast cancer-related genes, which could provide new biomarker for breast cancer diagnosis as well as potential treatment targets.Methods:Here we used the minimum redundancy-maximum relevance (mRMR) method to select significant genes, then mapped the transcripts of the genes on the Protein-Protein Interaction (PPI) network and traced the shortest path between each pair of two proteins.Results:As a result, we identified 24 breast cancer-related genes whose betweenness were over 700. The GO enrichment analysis indicated that the transcription and oxygen level are very important in breast cancer. And the pathway analysis indicated that most of these 24 genes are enriched in prostate cancer, endocrine resistance, and pathways in cancer.Conclusion:We hope these 24 genes might be useful for diagnosis, prognosis and treatment for breast cancer.
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Affiliation(s)
- Cheng-Wen Yang
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Huan-Huan Cao
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Yu Guo
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Yuan-Ming Feng
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Ning Zhang
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
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Xiang B, Liu K, Yu M, Liang X, Huang C, Zhang J, He W, Lei W, Chen J, Gu X, Gong K. Systematic genetic analyses of GWAS data reveal an association between the immune system and insomnia. Mol Genet Genomic Med 2019; 7:e00742. [PMID: 31094102 PMCID: PMC6625127 DOI: 10.1002/mgg3.742] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 04/18/2019] [Accepted: 04/22/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Previous studies have inferred a strong genetic component for insomnia. However, the etiology of insomnia is still unclear. The aim of the current study was to explore potential biological pathways, gene networks, and brain regions associated with insomnia. METHODS Using pathways (gene sets) from Reactome, we carried out a two-stage gene set enrichment analysis strategy. From a large genome-wide association studies (GWASs) of insomnia symptoms (32,155 cases/26,973 controls), significant gene sets were tested for replication in other large GWASs of insomnia complaints (32,384 cases/80,622 controls). After the network analysis of unique genes within the replicated pathways, a gene set analysis for genes in each cluster/module of the enhancing neuroimaging genetics through meta-analysis GWAS data was performed for the volumes of the intracranial and seven subcortical regions. RESULTS A total of 31 of 1,816 Reactome pathways were identified and showed associations with insomnia risk. In addition, seven functionally and topologically interconnected clusters (clusters 0-6) and six gene modules (named Yellow, Blue, Brown, Green, Red, and Turquoise) were associated with insomnia. Moreover, significant associations were detected between common variants of the genes in Cluster 2 with hippocampal volume (p = 0.035; family wise error [FWE] correction) and the red module with intracranial volume (p = 0.047; FWE correction). Functional enrichment for genes in the Cluster 2 and the Red module revealed the involvement of immune responses, nervous system development, NIK/NF-kappaB signaling, and I-kappaB kinase/NF-kappaB signaling. Core genes (UBC, UBB, and UBA52) in the interconnected functional network were found to be involved in regulating brain development. CONCLUSIONS The current study demonstrates that the immune system and the hippocampus may play central roles in neurodevelopment and insomnia risk.
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Affiliation(s)
- Bo Xiang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Kezhi Liu
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Minglan Yu
- Medical Laboratory CenterAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Xuemei Liang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Chaohua Huang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Jin Zhang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Wenying He
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Wei Lei
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Jing Chen
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
| | - Xiaochu Gu
- Clinical LaboratorySuzhou Guangji HospitalSuzhouJiangsu ProvinceChina
| | - Ke Gong
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan ProvinceAffiliated Hospital of Southwest Medical UniversityLuzhouSichuan ProvinceChina
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GPS: Identification of disease genes by rank aggregation of multi-genomic scoring schemes. Genomics 2019; 111:612-618. [DOI: 10.1016/j.ygeno.2018.03.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 03/16/2018] [Accepted: 03/21/2018] [Indexed: 12/19/2022]
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Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019; 10:294. [PMID: 31031797 PMCID: PMC6470635 DOI: 10.3389/fgene.2019.00294] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
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Affiliation(s)
- Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital, 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, Boston, MA, United States
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79
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Peng J, Guan J, Shang X. Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder. Front Genet 2019; 10:226. [PMID: 31001311 PMCID: PMC6454041 DOI: 10.3389/fgene.2019.00226] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 02/28/2019] [Indexed: 12/26/2022] Open
Abstract
Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study.
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Affiliation(s)
| | | | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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80
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Almasi SM, Hu T. Measuring the importance of vertices in the weighted human disease network. PLoS One 2019; 14:e0205936. [PMID: 30901770 PMCID: PMC6430629 DOI: 10.1371/journal.pone.0205936] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network (WHDN) in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in WHDN, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases.
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Affiliation(s)
| | - Ting Hu
- Department of Computer Science, Memorial University, St. John’s, NL, Canada
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81
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Chen SJ, Liao DL, Chen CH, Wang TY, Chen KC. Construction and Analysis of Protein-Protein Interaction Network of Heroin Use Disorder. Sci Rep 2019; 9:4980. [PMID: 30899073 PMCID: PMC6428805 DOI: 10.1038/s41598-019-41552-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/11/2019] [Indexed: 12/17/2022] Open
Abstract
Heroin use disorder (HUD) is a complex disease resulting from interactions among genetic and other factors (e.g., environmental factors). The mechanism of HUD development remains unknown. Newly developed network medicine tools provide a platform for exploring complex diseases at the system level. This study proposes that protein–protein interactions (PPIs), particularly those among proteins encoded by casual or susceptibility genes, are extremely crucial for HUD development. The giant component of our constructed PPI network comprised 111 nodes with 553 edges, including 16 proteins with large degree (k) or high betweenness centrality (BC), which were further identified as the backbone of the network. JUN with the largest degree was suggested to be central to the PPI network associated with HUD. Moreover, PCK1 with the highest BC and MAPK14 with the secondary largest degree and 9th highest BC might be involved in the development HUD and other substance diseases.
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Affiliation(s)
- Shaw-Ji Chen
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.,Department of Psychiatry, Mackay Memorial Hospital, Taitung Branch, Taiwan
| | - Ding-Lieh Liao
- Bali Psychiatric Center, Department of Health, Executive Yuan, New Taipei, Taiwan
| | - Chia-Hsiang Chen
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou and Chang Gung University School of Medicine, Taoyuan, Taiwan
| | - Tse-Yi Wang
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan
| | - Kuang-Chi Chen
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan.
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82
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Abstract
This chapter is based on exploiting the network-based representations of proteins, metagraphs, in protein-protein interaction network to identify candidate disease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To enhance PPI networks, we utilize biological properties of individual proteins as well. More specifically, we integrate keywords from UniProt database describing protein properties into the PPI network and construct a novel heterogeneous PPI-Keyword (PPIK) network consisting of both proteins and keywords. As proteins with similar functional duties or involving in the same metabolic pathway tend to have similar topological characteristics, we propose to represent them with metagraphs. Compared to the traditional network motif or subgraph, a metagraph can capture the topological arrangements through not only the protein-protein interactions but also protein-keyword associations. We feed those novel metagraph representations into classifiers for disease protein prediction and conduct our experiments on three different PPI databases. They show that the proposed method consistently increases disease protein prediction performance across various classifiers, by 15.3% in AUC on average. It outperforms the diffusion-based (e.g., RWR) and the module-based baselines by 13.8-32.9% in overall disease protein prediction. Breast cancer protein prediction outperforms RWR, PRINCE, and the module-based baselines by 6.6-14.2%. Finally, our predictions also exhibit better correlations with literature findings from PubMed database.
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83
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A network biology approach to unraveling inherited axonopathies. Sci Rep 2019; 9:1692. [PMID: 30737464 PMCID: PMC6368620 DOI: 10.1038/s41598-018-37119-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 11/23/2018] [Indexed: 12/14/2022] Open
Abstract
Inherited axonopathies represent a spectrum of disorders unified by the common pathological mechanism of length-dependent axonal degeneration. Progressive axonal degeneration can lead to both Charcot-Marie-Tooth type 2 (CMT2) and Hereditary Spastic Paraplegia (HSP) depending on the affected neurons: peripheral motor and sensory nerves or central nervous system axons of the corticospinal tract and dorsal columns, respectively. Inherited axonopathies display an extreme degree of genetic heterogeneity of Mendelian high-penetrance genes. High locus heterogeneity is potentially advantageous to deciphering disease etiology by providing avenues to explore biological pathways in an unbiased fashion. Here, we investigate ‘gene modules’ in inherited axonopathies through a network-based analysis of the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database. We demonstrate that CMT2 and HSP disease proteins are significantly more connected than randomly expected. We define these connected disease proteins as ‘proto-modules’ and show the topological relationship of these proto-modules by evaluating their overlap through a shortest-path based measurement. In particular, we observe that the CMT2 and HSP proto-modules significantly overlapped, demonstrating a shared genetic etiology. Comparison of both modules with other diseases revealed an overlapping relationship between HSP and hereditary ataxia and between CMT2 + HSP and hereditary ataxia. We then use the DIseAse Module Detection (DIAMOnD) algorithm to expand the proto-modules into comprehensive disease modules. Analysis of disease modules thus obtained reveals an enrichment of ribosomal proteins and pathways likely central to inherited axonopathy pathogenesis, including protein processing in the endoplasmic reticulum, spliceosome, and mRNA processing. Furthermore, we determine pathways specific to each axonopathy by analyzing the difference of the axonopathy modules. CMT2-specific pathways include glycolysis and gluconeogenesis-related processes, while HSP-specific pathways include processes involved in viral infection response. Unbiased characterization of inherited axonopathy disease modules will provide novel candidate disease genes, improve interpretation of candidate genes identified through patient data, and guide therapy development.
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84
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Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles. BIOMED RESEARCH INTERNATIONAL 2019; 2019:3907195. [PMID: 30723737 PMCID: PMC6339728 DOI: 10.1155/2019/3907195] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 12/06/2018] [Accepted: 12/12/2018] [Indexed: 12/16/2022]
Abstract
Proteomics, the large-scale analysis of proteins, is contributing greatly to understanding gene function in the postgenomic era. However, disease protein ranking using shotgun proteomics data has not been fully evaluated. In this study, we prioritized disease-related proteins by integrating the protein-protein interaction (PPI) network and protein differential expression profiles from colon and rectal cancer (CRC) or breast cancer (BC) proteomics. We applied Local Ranking (LR) and Global Ranking (GR) methods in network with three kinds of protein sets as a priori knowledge, which were known disease proteins (KDPs) that were collected from the Online Mendelian Inheritance in Man (OMIM) database, differentially expressed proteins (DEPs), and the collection of KDPs and their direct neighborhood with differential expression (eKDPs). The cross-validations showed that GR method outperformed LR method while using eKDPs as the initial training showed significantly higher accuracy compared to using the other two a priori sets. And then we validated the top ranked proteins using RNAi-based loss-of-function screens in the DepMap database. The results showed that 75% of top 20 proteins in CRC are necessary for tumor survival. In summary, the network-based Global Ranking with protein differential expression can efficiently prioritize cancer-related proteins and discover new candidate cancer genes or proteins.
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85
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Gomez-Varela D, Barry AM, Schmidt M. Proteome-based systems biology in chronic pain. J Proteomics 2019; 190:1-11. [DOI: 10.1016/j.jprot.2018.04.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 03/15/2018] [Accepted: 04/05/2018] [Indexed: 02/07/2023]
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86
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Wang ZT, Tan CC, Tan L, Yu JT. Systems biology and gene networks in Alzheimer’s disease. Neurosci Biobehav Rev 2019; 96:31-44. [DOI: 10.1016/j.neubiorev.2018.11.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 11/18/2018] [Accepted: 11/18/2018] [Indexed: 12/25/2022]
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87
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Zhu X, Shen X, Jiang X, Wei K, He T, Ma Y, Liu J, Hu X. Nonlinear expression and visualization of nonmetric relationships in genetic diseases and microbiome data. BMC Bioinformatics 2018; 19:505. [PMID: 30577738 PMCID: PMC6302369 DOI: 10.1186/s12859-018-2537-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background The traditional methods of visualizing high-dimensional data objects in low-dimensional metric spaces are subject to the basic limitations of metric space. These limitations result in multidimensional scaling that fails to faithfully represent non-metric similarity data. Results Multiple maps t-SNE (mm-tSNE) has drawn much attention due to the construction of multiple mappings in low-dimensional space to visualize the non-metric pairwise similarity to eliminate the limitations of a single metric map. mm-tSNE regularization combines the intrinsic geometry between data points in a high-dimensional space. The weight of data points on each map is used as the regularization parameter of the manifold, so the weights of similar data points on the same map are also as close as possible. However, these methods use standard momentum methods to calculate parameters of gradient at each iteration, which may lead to erroneous gradient search directions so that the target loss function fails to achieve a better local minimum. In this article, we use a Nesterov momentum method to learn the target loss function and correct each gradient update by looking back at the previous gradient in the candidate search direction. By using indirect second-order information, the algorithm obtains faster convergence than the original algorithm. To further evaluate our approach from a comparative perspective, we conducted experiments on several datasets including social network data, phenotype similarity data, and microbiomic data. Conclusions The experimental results show that the proposed method achieves better results than several versions of mm-tSNE based on three evaluation indicators including the neighborhood preservation ratio (NPR), error rate and time complexity.
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Affiliation(s)
- Xianchao Zhu
- School of Computer, Central China Normal University, Wuhan, China
| | - Xianjun Shen
- School of Computer, Central China Normal University, Wuhan, China.
| | - Xingpeng Jiang
- School of Computer, Central China Normal University, Wuhan, China
| | - Kaiping Wei
- School of Computer, Central China Normal University, Wuhan, China
| | - Tingting He
- School of Computer, Central China Normal University, Wuhan, China
| | - Yuanyuan Ma
- School of Computer, Central China Normal University, Wuhan, China
| | - Jiaqi Liu
- School of Computer, Central China Normal University, Wuhan, China
| | - Xiaohua Hu
- School of Computer, Central China Normal University, Wuhan, China.,College of Computing and Informatics, Drexel University, Philadelphia, PA, 19104, USA
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88
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Capriotti E, Ozturk K, Carter H. Integrating molecular networks with genetic variant interpretation for precision medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2018; 11:e1443. [PMID: 30548534 PMCID: PMC6450710 DOI: 10.1002/wsbm.1443] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/23/2018] [Accepted: 10/30/2018] [Indexed: 02/01/2023]
Abstract
More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under:
Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods
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Affiliation(s)
- Emidio Capriotti
- Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
| | - Kivilcim Ozturk
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, California
| | - Hannah Carter
- Department of Medicine and Institute for Genomic Medicine, University of California, San Diego, La Jolla, California
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89
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Mortezaei Z, Cazier JB, Mehrabi AA, Cheng C, Masoudi-Nejad A. Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis. J Cell Biochem 2018; 120:5459-5471. [PMID: 30302804 DOI: 10.1002/jcb.27825] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 09/12/2018] [Indexed: 12/26/2022]
Abstract
Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.
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Affiliation(s)
- Zahra Mortezaei
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Jean-Baptiste Cazier
- Centre for Computational Biology, Haworth Building, University of Birmingham, Birmingham, UK
| | - Ali Ashraf Mehrabi
- Department of Biometry and Plant Genetics, University of Ilam, Ilam, Iran
| | - Chao Cheng
- Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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90
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Boutchueng-Djidjou M, Belleau P, Bilodeau N, Fortier S, Bourassa S, Droit A, Elowe S, Faure RL. A type 2 diabetes disease module with a high collective influence for Cdk2 and PTPLAD1 is localized in endosomes. PLoS One 2018; 13:e0205180. [PMID: 30300385 PMCID: PMC6177195 DOI: 10.1371/journal.pone.0205180] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 09/20/2018] [Indexed: 01/19/2023] Open
Abstract
Despite the identification of many susceptibility genes our knowledge of the underlying mechanisms responsible for complex disease remains limited. Here, we identified a type 2 diabetes disease module in endosomes, and validate it for functional relevance on selected nodes. Using hepatic Golgi/endosomes fractions, we established a proteome of insulin receptor-containing endosomes that allowed the study of physical protein interaction networks on a type 2 diabetes background. The resulting collated network is formed by 313 nodes and 1147 edges with a topology organized around a few major hubs with Cdk2 displaying the highest collective influence. Overall, 88% of the nodes are associated with the type 2 diabetes genetic risk, including 101 new candidates. The Type 2 diabetes module is enriched with cytoskeleton and luminal acidification–dependent processes that are shared with secretion-related mechanisms. We identified new signaling pathways driven by Cdk2 and PTPLAD1 whose expression affects the association of the insulin receptor with TUBA, TUBB, the actin component ACTB and the endosomal sorting markers Rab5c and Rab11a. Therefore, the interactome of internalized insulin receptors reveals the presence of a type 2 diabetes disease module enriched in new layers of feedback loops required for insulin signaling, clearance and islet biology.
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Affiliation(s)
- Martial Boutchueng-Djidjou
- Départment of Pediatrics, Faculty of Medicine, Université Laval, Centre de Recherche du CHU de Québec, Québec city, Canada
| | - Pascal Belleau
- Plateforme Protéomique de l’Est du Québec, Université Laval. Université Laval, Québec, QC, Canada
| | - Nicolas Bilodeau
- Départment of Pediatrics, Faculty of Medicine, Université Laval, Centre de Recherche du CHU de Québec, Québec city, Canada
| | - Suzanne Fortier
- Départment of Pediatrics, Faculty of Medicine, Université Laval, Centre de Recherche du CHU de Québec, Québec city, Canada
| | - Sylvie Bourassa
- Plateforme Protéomique de l’Est du Québec, Université Laval. Université Laval, Québec, QC, Canada
| | - Arnaud Droit
- Plateforme Protéomique de l’Est du Québec, Université Laval. Université Laval, Québec, QC, Canada
| | - Sabine Elowe
- Départment of Pediatrics, Faculty of Medicine, Université Laval, Centre de Recherche du CHU de Québec, Québec city, Canada
| | - Robert L. Faure
- Départment of Pediatrics, Faculty of Medicine, Université Laval, Centre de Recherche du CHU de Québec, Québec city, Canada
- * E-mail:
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91
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Sharma A, Kitsak M, Cho MH, Ameli A, Zhou X, Jiang Z, Crapo JD, Beaty TH, Menche J, Bakke PS, Santolini M, Silverman EK. Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module. Sci Rep 2018; 8:14439. [PMID: 30262855 PMCID: PMC6160419 DOI: 10.1038/s41598-018-32173-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 08/20/2018] [Indexed: 12/21/2022] Open
Abstract
The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (CAB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method (local radiality), the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.
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Affiliation(s)
- Amitabh Sharma
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. .,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. .,Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA. .,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.
| | - Maksim Kitsak
- Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Asher Ameli
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Department of Physics, Northeastern University, Boston, MA, 02115, United States
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Zhiqiang Jiang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - James D Crapo
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jörg Menche
- Department of Bioinformatics, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090, Vienna, Austria
| | - Per S Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marc Santolini
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA.,Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA.,Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA. .,Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, USA. .,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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92
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Kashyap S, Kumar S, Agarwal V, Misra DP, Phadke SR, Kapoor A. Protein protein interaction network analysis of differentially expressed genes to understand involved biological processes in coronary artery disease and its different severity. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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93
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Fiscon G, Conte F, Farina L, Paci P. Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine. Genes (Basel) 2018; 9:genes9090437. [PMID: 30200360 PMCID: PMC6162385 DOI: 10.3390/genes9090437] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 08/25/2018] [Accepted: 08/30/2018] [Indexed: 12/14/2022] Open
Abstract
Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.
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Affiliation(s)
- Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, via dei Taurini 19, 00185 Rome, Italy.
- SysBio Centre of Systems Biology, Piazza della Scienza, 3, 20126 Milan, Italy.
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, via dei Taurini 19, 00185 Rome, Italy.
- SysBio Centre of Systems Biology, Piazza della Scienza, 3, 20126 Milan, Italy.
| | - Lorenzo Farina
- Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Viale Ariosto 25, 00185 Rome, Italy.
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, via dei Taurini 19, 00185 Rome, Italy.
- SysBio Centre of Systems Biology, Piazza della Scienza, 3, 20126 Milan, Italy.
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94
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Barry AM, Sondermann JR, Sondermann JH, Gomez-Varela D, Schmidt M. Region-Resolved Quantitative Proteome Profiling Reveals Molecular Dynamics Associated With Chronic Pain in the PNS and Spinal Cord. Front Mol Neurosci 2018; 11:259. [PMID: 30154697 PMCID: PMC6103001 DOI: 10.3389/fnmol.2018.00259] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 07/10/2018] [Indexed: 12/27/2022] Open
Abstract
To obtain a thorough understanding of chronic pain, large-scale molecular mapping of the pain axis at the protein level is necessary, but has not yet been achieved. We applied quantitative proteome profiling to build a comprehensive protein compendium of three regions of the pain neuraxis in mice: the sciatic nerve (SN), the dorsal root ganglia (DRG), and the spinal cord (SC). Furthermore, extensive bioinformatics analysis enabled us to reveal unique protein subsets which are specifically enriched in the peripheral nervous system (PNS) and SC. The immense value of these datasets for the scientific community is highlighted by validation experiments, where we monitored protein network dynamics during neuropathic pain. Here, we resolved profound region-specific differences and distinct changes of PNS-enriched proteins under pathological conditions. Overall, we provide a unique and validated systems biology proteome resource (summarized in our online database painproteome.em.mpg.de), which facilitates mechanistic insights into somatosensory biology and chronic pain—a prerequisite for the identification of novel therapeutic targets.
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Affiliation(s)
- Allison M Barry
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - Julia R Sondermann
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - Jan-Hendrik Sondermann
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - David Gomez-Varela
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
| | - Manuela Schmidt
- Max-Planck Institute of Experimental Medicine, Somatosensory Signaling and Systems Biology Group, Goettingen, Germany
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95
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Garcia-Vaquero ML, Gama-Carvalho M, Rivas JDL, Pinto FR. Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis. Sci Rep 2018; 8:11555. [PMID: 30068933 PMCID: PMC6070533 DOI: 10.1038/s41598-018-29990-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B .
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Affiliation(s)
- Marina L Garcia-Vaquero
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
| | - Margarida Gama-Carvalho
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), Salamanca, Spain
| | - Francisco R Pinto
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal.
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96
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Valdeolivas A, Tichit L, Navarro C, Perrin S, Odelin G, Levy N, Cau P, Remy E, Baudot A. Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics 2018; 35:497-505. [DOI: 10.1093/bioinformatics/bty637] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 07/16/2018] [Indexed: 01/04/2023] Open
Affiliation(s)
- Alberto Valdeolivas
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
- ProGeLife, Marseille
| | - Laurent Tichit
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
| | - Claire Navarro
- ProGeLife, Marseille
- Aix Marseille Univ, INSERM, MMG, Marseille, France
| | - Sophie Perrin
- ProGeLife, Marseille
- Aix Marseille Univ, INSERM, MMG, Marseille, France
| | - Gaëlle Odelin
- ProGeLife, Marseille
- Aix Marseille Univ, INSERM, MMG, Marseille, France
| | - Nicolas Levy
- Aix Marseille Univ, INSERM, MMG, Marseille, France
| | - Pierre Cau
- ProGeLife, Marseille
- Aix Marseille Univ, INSERM, MMG, Marseille, France
| | - Elisabeth Remy
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
| | - Anaïs Baudot
- Aix Marseille Univ, CNRS, Centrale Marseille, I2M, Marseille, France
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97
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Sharma A, Halu A, Decano JL, Padi M, Liu YY, Prasad RB, Fadista J, Santolini M, Menche J, Weiss ST, Vidal M, Silverman EK, Aikawa M, Barabási AL, Groop L, Loscalzo J. Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes. NPJ Syst Biol Appl 2018; 4:25. [PMID: 29977601 PMCID: PMC6028434 DOI: 10.1038/s41540-018-0057-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 04/09/2018] [Accepted: 05/04/2018] [Indexed: 01/14/2023] Open
Abstract
Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways' relationship to T2D.
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Affiliation(s)
- Amitabh Sharma
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA.,2Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115 USA.,3Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215 USA.,4Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215 USA
| | - Arda Halu
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA.,4Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215 USA
| | - Julius L Decano
- 4Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215 USA
| | - Megha Padi
- 5Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721 USA
| | - Yang-Yu Liu
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Rashmi B Prasad
- 6Lund University Diabetes Center, Department of Clinical Sciences, Diabetes & Endocrinology, Skåne University Hospital Malmö, Lund University, Malmö, 20502 Sweden
| | - Joao Fadista
- 6Lund University Diabetes Center, Department of Clinical Sciences, Diabetes & Endocrinology, Skåne University Hospital Malmö, Lund University, Malmö, 20502 Sweden
| | - Marc Santolini
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA.,2Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115 USA
| | - Jörg Menche
- 2Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115 USA.,7 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, 1090 Austria
| | - Scott T Weiss
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Marc Vidal
- 3Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215 USA.,8Department of Genetics, Harvard Medical School, Boston, MA 02115 USA
| | - Edwin K Silverman
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Masanori Aikawa
- 4Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215 USA
| | - Albert-László Barabási
- 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA.,2Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115 USA.,3Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215 USA.,9Center for Network Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
| | - Leif Groop
- 6Lund University Diabetes Center, Department of Clinical Sciences, Diabetes & Endocrinology, Skåne University Hospital Malmö, Lund University, Malmö, 20502 Sweden.,10Department of Clinical Sciences, Islet cell physiology, Skåne University Hospital Malmö, Lund University, Malmö, 20502 Sweden
| | - Joseph Loscalzo
- 11Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115 USA
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98
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Predicting perturbation patterns from the topology of biological networks. Proc Natl Acad Sci U S A 2018; 115:E6375-E6383. [PMID: 29925605 DOI: 10.1073/pnas.1720589115] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.
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99
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Attea BA, Abdullah QZ. Improving the performance of evolutionary-based complex detection models in protein–protein interaction networks. Soft comput 2018. [DOI: 10.1007/s00500-017-2593-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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100
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Xiang B, Liu K, Yu M, Liang X, Zhang J, Lei W, Huang C, Chen J, Gu X, Li N, Wu G, Wang Y, He W, Tan J, Zhang T. Systematic genetic analyses of genome-wide association study data reveal an association between the key nucleosome remodeling and deacetylase complex and bipolar disorder development. Bipolar Disord 2018; 20:370-380. [PMID: 29280245 DOI: 10.1111/bdi.12580] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Accepted: 10/21/2017] [Indexed: 11/29/2022]
Abstract
BACKGROUND Genome-wide association studies (GWASs) are used to identify genetic variants for association with bipolar disorder (BD) risk; however, each GWAS can only reveal a small fraction of this association. This study systematically analyzed multiple GWAS data sets to provide further insights into potential causal BD processes by integrating the results of Psychiatric Genomics Consortium Phase I (PGC-I) for BD with core human pathways and functional networks. METHODS The i-Gsea4GwasV2 program was used to analyze data from the PGC-I GWAS for BD (the pathways came from Reactome), as well as the nominally significant pathways. We established a gene network of the significant pathways and performed a gene set analysis for each gene cluster of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) GWAS data for the volumes of the intracranial region and seven subcortical regions. RESULTS A total of 30 of 1816 Reactome pathways were identified and showed associations with BD risk. We further revealed 22 interconnected functional and topologically interacting clusters (Clusters 0-21) that were associated with BD risk. Moreover, we obtained brain transcriptome data from BrainSpan and found significant associations between common variants of the genes in Cluster 1 with the hippocampus (HIP; P = .026; family-wise error [FWE] correction) and amygdala (AMY; P = .016; FEW correction) in Cluster 8 with HIP (P = .022; FWE correction). The genes in Cluster 1 were enriched for the transcriptional co-expression profile in the prenatal AMY, and core genes (CDH4, MTA2, RBBP4, and HDAC2) were identified to be involved in regulating early brain development. CONCLUSION This study demonstrated that the HIP and AMY play a central role in neurodevelopment and BD risk.
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Affiliation(s)
- Bo Xiang
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Kezhi Liu
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Minglan Yu
- Medical Laboratory Center, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Xuemei Liang
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Jin Zhang
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Wei Lei
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Chaohua Huang
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Jing Chen
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Xiaochu Gu
- Clinical Laboratory, Su zhou Guang ji Hospital, Suzhou, Jiangsu Province, China
| | - Nian Li
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Guoying Wu
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Yan Wang
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Wenying He
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Jinhua Tan
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Tao Zhang
- Department of Psychiatry, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
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