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Nechanitzky R, Ramachandran P, Nechanitzky D, Li WY, Wakeham AC, Haight J, Saunders ME, Epelman S, Mak TW. CaSSiDI: novel single-cell "Cluster Similarity Scoring and Distinction Index" reveals critical functions for PirB and context-dependent Cebpb repression. Cell Death Differ 2024; 31:265-279. [PMID: 38383888 PMCID: PMC10923835 DOI: 10.1038/s41418-024-01268-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 01/15/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
PirB is an inhibitory cell surface receptor particularly prominent on myeloid cells. PirB curtails the phenotypes of activated macrophages during inflammation or tumorigenesis, but its functions in macrophage homeostasis are obscure. To elucidate PirB-related functions in macrophages at steady-state, we generated and compared single-cell RNA-sequencing (scRNAseq) datasets obtained from myeloid cell subsets of wild type (WT) and PirB-deficient knockout (PirB KO) mice. To facilitate this analysis, we developed a novel approach to clustering parameter optimization called "Cluster Similarity Scoring and Distinction Index" (CaSSiDI). We demonstrate that CaSSiDI is an adaptable computational framework that facilitates tandem analysis of two scRNAseq datasets by optimizing clustering parameters. We further show that CaSSiDI offers more advantages than a standard Seurat analysis because it allows direct comparison of two or more independently clustered datasets, thereby alleviating the need for batch-correction while identifying the most similar and different clusters. Using CaSSiDI, we found that PirB is a novel regulator of Cebpb expression that controls the generation of Ly6Clo patrolling monocytes and the expansion properties of peritoneal macrophages. PirB's effect on Cebpb is tissue-specific since it was not observed in splenic red pulp macrophages (RPMs). However, CaSSiDI revealed a segregation of the WT RPM population into a CD68loIrf8+ "neuronal-primed" subset and an CD68hiFtl1+ "iron-loaded" subset. Our results establish the utility of CaSSiDI for single-cell assay analyses and the determination of optimal clustering parameters. Our application of CaSSiDI in this study has revealed previously unknown roles for PirB in myeloid cell populations. In particular, we have discovered homeostatic functions for PirB that are related to Cebpb expression in distinct macrophage subsets.
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Affiliation(s)
- Robert Nechanitzky
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.
- Providence Therapeutics Holdings Inc., Calgary, AB, Canada.
| | - Parameswaran Ramachandran
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Duygu Nechanitzky
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Wanda Y Li
- Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Andrew C Wakeham
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Jillian Haight
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Mary E Saunders
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Slava Epelman
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ted Rogers Centre for Heart Research, Translational Biology and Engineering Program, Toronto, ON, Canada
- Peter Munk Cardiac Centre, UHN, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Tak W Mak
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.
- Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China.
- Department of Pathology Department of Pathology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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2
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Wang R, Jia C, Zheng N, Liu S, Qi Z, Wang R, Zhang L, Niu Y, Pan S. Effects of Photodynamic Therapy on Streptococcus mutans and Enamel Remineralization of Multifunctional TiO2-HAP Composite Nanomaterials. Photodiagnosis Photodyn Ther 2022; 42:103141. [PMID: 36202321 DOI: 10.1016/j.pdpdt.2022.103141] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/05/2022] [Accepted: 09/29/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND As photosensitizer and photocatalyst, titanium dioxide (TiO2) can produce a photodynamic reaction for antibacterial treatment. This study aims to explore a Titanium dioxide/nano-hydroxyapatite (TiO2-HAP) composite combined with the dental curing lamp (385-515 nm) in clinical which could inhibit the dental plaque biofilm formed by Streptococcus mutans (S. mutans) and promote the enamel surface remineralization simultaneously. METHODS X-ray Diffraction (XRD) and high resolution transmission electron microscope (HRTEM) were used to detect the characterization of TiO2-HAP composite nanomaterials. Photodynamic properties of TiO2-HAP were detected by Diffuse reflectance spectrum (DRS) and fluorescence spectroscopy. Bacterial growth was measured by reading the absorbance of bacterial cultures and confocal microscope was used to observe the biofilm removal ability of nanomaterials. The ability of TiO2-HAP to promote enamel remineralization was measured by Scanning electron microscope (SEM). RESULTS The OD 600 of S. mutans was 0.76 in the control group and 0.13 in group of TiO2-HAP with exposure to light-emitting diode (LED) (150 mW/cm2) for 5 min, suggesting its sustained antibacterial potency and inhibition of the metabolic activity of dental plaque microcosm biofilm. Also, the release of calcium and phosphorus ions in TiO2-HAP can promote enamel mineralization simultaneously. After 15 days of remineralization, the Ca/P ratio of demineralized enamel surface increased from 1.28 to 1.67, which was similar to that of normal enamel. CONCLUSIONS The TiO2-HAP exhibit a promising anti-bacterial activity and remineralization capacity which can prevent the occurrence of caries to the greatest extent and promote the biomimetic mineralization of dental tissues.
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Affiliation(s)
- Ranxu Wang
- The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Conghui Jia
- The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Nannan Zheng
- School of Life Science and Technology, Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education, Micro/Nano Technology Research Center, Harbin Institute of Technology, Harbin 150080, China
| | - Shujuan Liu
- School of Materials Science and Engineering, Harbin Institute of Technology, Harbin 150001, PR China
| | - Zhilin Qi
- School of Life Science and Technology, Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education, Micro/Nano Technology Research Center, Harbin Institute of Technology, Harbin 150080, China
| | - Ruiwen Wang
- Material Science and Engineering college, Northeast Forestry University, Harbin, Heilongjiang 150080, China
| | - Lu Zhang
- School of Life Science and Technology, Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education, Micro/Nano Technology Research Center, Harbin Institute of Technology, Harbin 150080, China
| | - Yumei Niu
- The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China.
| | - Shuang Pan
- The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China.
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3
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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4
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Lüscher-Dias T, Siqueira Dalmolin RJ, de Paiva Amaral P, Alves TL, Schuch V, Franco GR, Nakaya HI. The evolution of knowledge on genes associated with human diseases. iScience 2022; 25:103610. [PMID: 35005554 PMCID: PMC8719018 DOI: 10.1016/j.isci.2021.103610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022] Open
Abstract
Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.
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Affiliation(s)
- Thomaz Lüscher-Dias
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Rodrigo Juliani Siqueira Dalmolin
- Bioinformatics Multidisciplinary Environment—BioME, IMD, Federal University of Rio Grande do Norte, Natal, RN, Brazil
- Department of Biochemistry, CB, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | | | - Tiago Lubiana Alves
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Viviane Schuch
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Glória Regina Franco
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Helder I. Nakaya
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
- Scientific Platform Pasteur-University of São Paulo, São Paulo, Brazil
- Hospital Israelita Albert Einstein, São Paulo, Brazil
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5
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Prieto Santamaría L, García Del Valle EP, Zanin M, Hernández Chan GS, Pérez Gallardo Y, Rodríguez-González A. Classifying diseases by using biological features to identify potential nosological models. Sci Rep 2021; 11:21096. [PMID: 34702888 PMCID: PMC8548311 DOI: 10.1038/s41598-021-00554-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 10/14/2021] [Indexed: 11/25/2022] Open
Abstract
Established nosological models have provided physicians an adequate enough classification of diseases so far. Such systems are important to correctly identify diseases and treat them successfully. However, these taxonomies tend to be based on phenotypical observations, lacking a molecular or biological foundation. Therefore, there is an urgent need to modernize them in order to include the heterogeneous information that is produced in the present, as could be genomic, proteomic, transcriptomic and metabolic data, leading this way to more comprehensive and robust structures. For that purpose, we have developed an extensive methodology to analyse the possibilities when it comes to generate new nosological models from biological features. Different datasets of diseases have been considered, and distinct features related to diseases, namely genes, proteins, metabolic pathways and genetical variants, have been represented as binary and numerical vectors. From those vectors, diseases distances have been computed on the basis of several metrics. Clustering algorithms have been implemented to group diseases, generating different models, each of them corresponding to the distinct combinations of the previous parameters. They have been evaluated by means of intrinsic metrics, proving that some of them are highly suitable to cover new nosologies. One of the clustering configurations has been deeply analysed, demonstrating its quality and validity in the research context, and further biological interpretations have been made. Such model was particularly generated by OPTICS clustering algorithm, by studying the distance between diseases based on gene sharedness and following cosine index metric. 729 clusters were formed in this model, which obtained a Silhouette coefficient of 0.43.
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Affiliation(s)
- Lucía Prieto Santamaría
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain. .,Ezeris Networks Global Services S.L., 28028, Madrid, Spain.
| | | | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos, CSIC-UIB, 07122, Palma de Mallorca, Spain
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6
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Sharma M, Barai RS, Kundu I, Bhaye S, Pokar K, Idicula-Thomas S. PCOSKB R2: a database of genes, diseases, pathways, and networks associated with polycystic ovary syndrome. Sci Rep 2020; 10:14738. [PMID: 32895427 PMCID: PMC7477240 DOI: 10.1038/s41598-020-71418-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/17/2020] [Indexed: 01/08/2023] Open
Abstract
PolyCystic Ovary Syndrome KnowledgeBase (PCOSKBR2) is a manually curated database with information on 533 genes, 145 SNPs, 29 miRNAs, 1,150 pathways, and 1,237 diseases associated with PCOS. This data has been retrieved based on evidence gleaned by critically reviewing literature and related records available for PCOS in databases such as KEGG, DisGeNET, OMIM, GO, Reactome, STRING, and dbSNP. Since PCOS is associated with multiple genes and comorbidities, data mining algorithms for comorbidity prediction and identification of enriched pathways and hub genes are integrated in PCOSKBR2, making it an ideal research platform for PCOS. PCOSKBR2 is freely accessible at http://www.pcoskb.bicnirrh.res.in/ .
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Affiliation(s)
- Mridula Sharma
- Biomedical Informatics Center, Indian Council of Medical Research-National Institute for Research in Reproductive Health, Mumbai, 400012, India
| | - Ram Shankar Barai
- Biomedical Informatics Center, Indian Council of Medical Research-National Institute for Research in Reproductive Health, Mumbai, 400012, India
| | - Indra Kundu
- Biomedical Informatics Center, Indian Council of Medical Research-National Institute for Research in Reproductive Health, Mumbai, 400012, India
| | - Sameeksha Bhaye
- Biomedical Informatics Center, Indian Council of Medical Research-National Institute for Research in Reproductive Health, Mumbai, 400012, India
| | - Khushal Pokar
- Biomedical Informatics Center, Indian Council of Medical Research-National Institute for Research in Reproductive Health, Mumbai, 400012, India
| | - Susan Idicula-Thomas
- Biomedical Informatics Center, Indian Council of Medical Research-National Institute for Research in Reproductive Health, Mumbai, 400012, India.
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7
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Wang W, Langlois R, Langlois M, Genchev GZ, Wang X, Lu H. Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins. Front Genet 2019; 10:729. [PMID: 31543893 PMCID: PMC6729729 DOI: 10.3389/fgene.2019.00729] [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: 12/21/2018] [Accepted: 07/11/2019] [Indexed: 12/27/2022] Open
Abstract
Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.
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Affiliation(s)
- Wenchuan Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas
| | - Robert Langlois
- Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Marina Langlois
- Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Georgi Z Genchev
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States.,Bulgarian Institute for Genomics and Precision Medicine, Sofia, Bulgaria
| | - Xiaolei Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Hui Lu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States.,Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China
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8
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Wei D(H, Kang T, Pincus HA, Weng C. Construction of Disease Similarity Networks Using Concept Embedding and Ontology. Stud Health Technol Inform 2019; 264:442-446. [PMID: 31437962 PMCID: PMC6874911 DOI: 10.3233/shti190260] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Discovering disease similarities are beneficial for the diagnosis and treatment of mental diseases. In this research, we proposed a data driven method, that is, integrating a variety of publicly available data resources including Unified Medical Language System (UMLS) Metathesaurus, Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) and cui2vec concept embedding to construct a mental disease similarity network. The resulting mental disease similarity network offered a new view for navigating and investigating disease relations; it also revealed popular mental disease in the literature in terms of the number of connections and similarities with other diseases. It shows that depressive disorder is directly connected with nine other popular diseases and connects 52 other diseases in the network. The top three popular mental diseases are depressive disorder, dysthymia (now known as persistent depressive disorder), and neurosis. Future research will focus on studying the clusters generated from the similarity network.
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Affiliation(s)
- Duo (Helen) Wei
- Department of Computer Science, School of Business, Stockton University, Galloway, New Jersey, USA
| | - Tian Kang
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Harold Alan Pincus
- Department of Psychiatry, Columbia University, New York, New York, USA
- Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, USA
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9
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Jia J, An Z, Ming Y, Guo Y, Li W, Liang Y, Guo D, Li X, Tai J, Chen G, Jin Y, Liu Z, Ni X, Shi T. eRAM: encyclopedia of rare disease annotations for precision medicine. Nucleic Acids Res 2019; 46:D937-D943. [PMID: 29106618 PMCID: PMC5753383 DOI: 10.1093/nar/gkx1062] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/24/2017] [Indexed: 01/12/2023] Open
Abstract
Rare diseases affect over a hundred million people worldwide, most of these patients are not accurately diagnosed and effectively treated. The limited knowledge of rare diseases forms the biggest obstacle for improving their treatment. Detailed clinical phenotyping is considered as a keystone of deciphering genes and realizing the precision medicine for rare diseases. Here, we preset a standardized system for various types of rare diseases, called encyclopedia of Rare disease Annotations for Precision Medicine (eRAM). eRAM was built by text-mining nearly 10 million scientific publications and electronic medical records, and integrating various data in existing recognized databases (such as Unified Medical Language System (UMLS), Human Phenotype Ontology, Orphanet, OMIM, GWAS). eRAM systematically incorporates currently available data on clinical manifestations and molecular mechanisms of rare diseases and uncovers many novel associations among diseases. eRAM provides enriched annotations for 15 942 rare diseases, yielding 6147 human disease related phenotype terms, 31 661 mammalians phenotype terms, 10,202 symptoms from UMLS, 18 815 genes and 92 580 genotypes. eRAM can not only provide information about rare disease mechanism but also facilitate clinicians to make accurate diagnostic and therapeutic decisions towards rare diseases. eRAM can be freely accessed at http://www.unimd.org/eram/.
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Affiliation(s)
- Jinmeng Jia
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Zhongxin An
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yue Ming
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yongli Guo
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Wei Li
- Beijing Key Laboratory for Genetics of Birth Defects, The Ministry of Education Key Laboratory of Major Diseases in Children, Center for Medical Genetics, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Yunxiang Liang
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Dongming Guo
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xin Li
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jun Tai
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Geng Chen
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yaqiong Jin
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Zhimei Liu
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Xin Ni
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
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10
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Jiang J, Gu J, Zhao T, Lu H. VCF-Server: A web-based visualization tool for high-throughput variant data mining and management. Mol Genet Genomic Med 2019; 7:e00641. [PMID: 31127704 PMCID: PMC6625089 DOI: 10.1002/mgg3.641] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 01/25/2019] [Accepted: 02/20/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Next-generation sequencing (NGS) has been widely used in both clinics and research. It has become the most powerful tool for diagnosing genetic disorders and investigating disease etiology through the discovery of genetic variants. Variants identified by NGS are stored in variant call format (VCF) files. However, querying and filtering VCF files are extremely difficult for researchers without programming skills. Furthermore, as the mutation data are increasing exponentially, there is an urgent need to develop tools to manage these variant data in a centralized way. METHODS The VCF-Server was developed as a web-based visualization tool to support the interactive analysis of genetic variant data. It allows researchers and medical geneticists to manage, annotate, filter, query, and export variants in a fast and effective way. RESULTS In this study, we developed the VCF-Server, a powerful and easily accessible tool for researchers and medical geneticists to perform variant analysis. Users can query VCFs, annotate, and filter variants without knowing programming code. Once the VCF file is uploaded, VCF-Server allows users to annotate the VCF with commonly used databases or user-defined variant annotations (including variant blacklist and whitelist). Variant information in the VCF is shown visually via the interactive graphical interface. Users can filter the variants with flexible filtering rules, and the prioritized variants can be exported locally for further analysis. As VCF-Server adopts a web file system, files in the VCF-Server can be stored and managed in a centralized way. Moreover, VCF-Server allows direct web-based analysis (accessible through either desktop computers or mobile devices) as well as local deployment. CONCLUSIONS With an easy-to-use graphical interface, VCF-Server allows researchers with little bioinformatics background to explore and mine mutation data, which may broaden the application of NGS technology in clinics and research. The tool is freely available for use at https://www.diseasegps.org/VCF-Server?lan = eng.
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Affiliation(s)
- Jianping Jiang
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jianlei Gu
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China
| | - Tingting Zhao
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Lu
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China
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Qin W, Wang X, Zhao H, Lu H. A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis. Front Genet 2019; 10:293. [PMID: 31031796 PMCID: PMC6473067 DOI: 10.3389/fgene.2019.00293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/19/2019] [Indexed: 12/25/2022] Open
Abstract
Motivation: Gene set enrichment analysis is a widely accepted expression analysis tool which aims at detecting coordinated expression change within a pre-defined gene sets rather than individual genes. The benefit of gene set analysis over individual differentially expressed (DE) gene analysis includes more reproducible and interpretable results and detecting small but consistent change among gene set which could not be detected by DE gene analysis. There have been many successful gene set analysis applications in human diseases. However, when the sample size of a disease study is small and no other public data sets of the same disease are available, it will lead to lack of power to detect pathways of importance to the disease. Results: We have developed a novel joint gene set analysis statistical framework which aims at improving the power of identifying enriched gene sets through integrating multiple similar disease data sets. Through comprehensive simulation studies, we demonstrated that our proposed frameworks obtained much better AUC scores than single data set analysis and another meta-analysis method in identification of enriched pathways. When applied to two real data sets, the proposed framework could retain the enriched gene sets identified by single data set analysis and exclusively obtained up to 200% more disease-related gene sets demonstrating the improved identification power through information shared between similar diseases. We expect that the proposed framework would enable researchers to better explore public data sets when the sample size of their study is limited.
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Affiliation(s)
- Wenyi Qin
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Genetics, School of Medicine, Yale University, New Haven, CT, United States
| | - Xujun Wang
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
| | - Hongyu Zhao
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
| | - Hui Lu
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
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12
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Maternal Cognitive Impairment Associated with Gestational Diabetes Mellitus-A Review of Potential Contributing Mechanisms. Int J Mol Sci 2018; 19:ijms19123894. [PMID: 30563117 PMCID: PMC6321050 DOI: 10.3390/ijms19123894] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 11/14/2018] [Accepted: 11/29/2018] [Indexed: 12/16/2022] Open
Abstract
Gestational diabetes mellitus (GDM) carries many risks, where high blood pressure, preeclampsia and future type II diabetes are widely acknowledged, but less focus has been placed on its effect on cognitive function. Although the multifactorial pathogenesis of maternal cognitive impairment is not completely understood, it shares several features with type 2 diabetes mellitus (T2DM). In this review, we discuss some key pathophysiologies of GDM that may lead to cognitive impairment, specifically hyperglycemia, insulin resistance, oxidative stress, and neuroinflammation. We explain how these incidents: (i) impair the insulin-signaling pathway and/or (ii) lead to cognitive impairment through hyperphosphorylation of τ protein, overexpression of amyloid-β and/or activation of microglia. The aforementioned pathologies impair the insulin-signaling pathway primarily through serine phosphorylation of insulin receptor substances (IRS). This then leads to the inactivation of the phosphatidylinositol 3-kinase/Protein kinase B (PI3K/AKT) signaling cascade, which is responsible for maintaining brain homeostasis and normal cognitive functioning. PI3K/AKT is crucial in maintaining normal cognitive function through the inactivation of glycogen synthase kinase 3β (GSκ3β), which hyperphosphorylates τ protein and releases pro-inflammatory cytokines that are neurotoxic. Several biomarkers were also highlighted as potential biomarkers of GDM-related cognitive impairment such as AGEs, serine-phosphorylated IRS-1 and inflammatory markers such as tumor necrosis factor α (TNF-α), high-sensitivity C-reactive protein (hs-CRP), leptin, interleukin 1β (IL-1β), and IL-6. Although GDM is a transient disease, its complications may be long-term, and hence increased mechanistic knowledge of the molecular changes contributing to cognitive impairment may provide important clues for interventional strategies.
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Jia J, An Z, Ming Y, Guo Y, Li W, Li X, Liang Y, Guo D, Tai J, Chen G, Jin Y, Liu Z, Ni X, Shi T. PedAM: a database for Pediatric Disease Annotation and Medicine. Nucleic Acids Res 2018; 46:D977-D983. [PMID: 29126123 PMCID: PMC5753298 DOI: 10.1093/nar/gkx1049] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 10/04/2017] [Accepted: 10/24/2017] [Indexed: 12/14/2022] Open
Abstract
There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/.
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Affiliation(s)
- Jinmeng Jia
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Zhongxin An
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yue Ming
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yongli Guo
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
| | - Wei Li
- Beijing Key Laboratory for Genetics of Birth Defects, The Ministry of Education Key Laboratory of Major Diseases in Children, Center for Medical Genetics, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
| | - Xin Li
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yunxiang Liang
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Dongming Guo
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jun Tai
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
| | - Geng Chen
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yaqiong Jin
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
| | - Zhimei Liu
- Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
| | - Xin Ni
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
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Lee KH, Kim JH. Evolution of Translational Bioinformatics: lessons learned from TBC 2016. BMC Med Genomics 2017; 10:32. [PMID: 28589861 PMCID: PMC5461521 DOI: 10.1186/s12920-017-0262-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Affiliation(s)
- Kye Hwa Lee
- Biomedical Informatics, Seoul National University Hospital, 28 Yongon-dong, Chongno-gu, Seoul, 110799, Korea
| | - Ju Han Kim
- Div. of Biomedical Informatics, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul, 110799, Korea.
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