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Jia ZC, Yang X, Wu YK, Li M, Das D, Chen MX, Wu J. The Art of Finding the Right Drug Target: Emerging Methods and Strategies. Pharmacol Rev 2024; 76:896-914. [PMID: 38866560 PMCID: PMC11334170 DOI: 10.1124/pharmrev.123.001028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.
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Affiliation(s)
- Zi-Chang Jia
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Xue Yang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Yi-Kun Wu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Min Li
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Debatosh Das
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| | - Mo-Xian Chen
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| | - Jian Wu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
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Ruprecht NA, Kennedy JD, Bansal B, Singhal S, Sens D, Maggio A, Doe V, Hawkins D, Campbel R, O’Connell K, Gill JS, Schaefer K, Singhal SK. Transcriptomics and epigenetic data integration learning module on Google Cloud. Brief Bioinform 2024; 25:bbae352. [PMID: 39101486 PMCID: PMC11299028 DOI: 10.1093/bib/bbae352] [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: 11/30/2023] [Revised: 05/12/2024] [Accepted: 07/06/2024] [Indexed: 08/06/2024] Open
Abstract
Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. HIGHLIGHTS
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Affiliation(s)
- Nathan A Ruprecht
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Joshua D Kennedy
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Chemistry and Physics, Drury University, 900 N. Benton Avenue, Springfield, MO 65802, United States
| | - Benu Bansal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sonalika Singhal
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Donald Sens
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Angela Maggio
- Deloitte, Health Data and AI, Deloitte Consulting LLP, 1919 N. Lynn Street, Suite 1500, Arlington, VA 22209, United States
| | - Valena Doe
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Dale Hawkins
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Ross Campbel
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Kyle O’Connell
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Jappreet Singh Gill
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Kalli Schaefer
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sandeep K Singhal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
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Cao S, Zhao X, Li Z, Yu R, Li Y, Zhou X, Yan W, Chen D, He C. Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification. PLANT DIVERSITY 2024; 46:372-385. [PMID: 38798726 PMCID: PMC11119547 DOI: 10.1016/j.pld.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 05/29/2024]
Abstract
Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors (TFs) in intricate regulatory networks in a cell-type specific manner. Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings. This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets, addressing batch effects and conserving biological variance. This integration spans a broad spectrum of tissues, including both below- and above-ground parts. Utilizing a rigorous approach for cell type annotation, we identified 47 distinct cell types or states, largely expanding our current view of plant cell compositions. We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression. Taken together, our study not only offers extensive plant cell atlas exploration that serves as a valuable resource, but also provides molecular insights into gene-regulatory programs that varies from different cell types.
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Affiliation(s)
- Shanni Cao
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Xue Zhao
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Zhuojin Li
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Ranran Yu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Yuqi Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinkai Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Chao He
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
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Tsare EPG, Klapa MI, Moschonas NK. Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis. Hum Genomics 2024; 18:15. [PMID: 38326862 DOI: 10.1186/s40246-023-00565-6] [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: 08/08/2023] [Accepted: 11/12/2023] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS The implemented workflow could be used for other multifactorial diseases.
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Affiliation(s)
- Evridiki-Pandora G Tsare
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece
| | - Maria I Klapa
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
| | - Nicholas K Moschonas
- Department of General Biology, School of Medicine, University of Patras, Patras, Greece.
- Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research and Technology-Hellas (FORTH/ICE-HT), Patras, Greece.
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Tang J, Xu Q, Tang K, Ye X, Cao Z, Zou M, Zeng J, Guan X, Han J, Wang Y, Yang L, Lin Y, Jiang K, Chen X, Zhao Y, Tian D, Li C, Shen W, Du X. Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome. Front Immunol 2023; 13:1048774. [PMID: 36713410 PMCID: PMC9878565 DOI: 10.3389/fimmu.2022.1048774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
Introduction Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza. Methods Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized. Results We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79. Discussion The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.
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Affiliation(s)
- Jing Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Qiumei Xu
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,Guangzhou Eighth People’s Hospital, Guangzhou Medical University, Guangzhou, China
| | - Kang Tang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xiaoyan Ye
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,School of Public Health, Shantou University, Shantou, China
| | - Min Zou
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Zeng
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Xinyan Guan
- Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Jinglin Han
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Yihan Wang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Lan Yang
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Yishan Lin
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Kaiao Jiang
- Palos Verdes Peninsula High School, Rancho Palos Verdes, CA, United States
| | - Xiaoliang Chen
- Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, China
| | - Yang Zhao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Dechao Tian
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China
| | - Chunwei Li
- Department of Otolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wei Shen
- Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China,*Correspondence: Xiangjun Du, ; Wei Shen,
| | - Xiangjun Du
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China,School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, China,Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China,*Correspondence: Xiangjun Du, ; Wei Shen,
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Li MM, Huang K, Zitnik M. Graph representation learning in biomedicine and healthcare. Nat Biomed Eng 2022; 6:1353-1369. [PMID: 36316368 PMCID: PMC10699434 DOI: 10.1038/s41551-022-00942-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 08/09/2022] [Indexed: 11/11/2022]
Abstract
Networks-or graphs-are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines.
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Affiliation(s)
- Michelle M Li
- Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kexin Huang
- Health Data Science Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
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Loers JU, Vermeirssen V. SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks. BMC Bioinformatics 2022; 23:363. [PMID: 36064320 PMCID: PMC9442970 DOI: 10.1186/s12859-022-04908-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. RESULTS SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. CONCLUSIONS We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC .
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Affiliation(s)
- Jens Uwe Loers
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium.,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Vanessa Vermeirssen
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium. .,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. .,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
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8
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Zhao C, Dong J, Deng L, Tan Y, Jiang W, Cai Z. Molecular network strategy in multi-omics and mass spectrometry imaging. Curr Opin Chem Biol 2022; 70:102199. [PMID: 36027696 DOI: 10.1016/j.cbpa.2022.102199] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/01/2022] [Accepted: 07/10/2022] [Indexed: 11/30/2022]
Abstract
Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.
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Affiliation(s)
- Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Yawen Tan
- Department of Breast and Thyroid Surgery, Shenzhen Second People's Hospital, Shenzhen, China
| | - Wei Jiang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China.
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Kondratyeva L, Alekseenko I, Chernov I, Sverdlov E. Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism. BIOLOGY 2022; 11:1208. [PMID: 36009835 PMCID: PMC9404739 DOI: 10.3390/biology11081208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 08/03/2022] [Accepted: 08/10/2022] [Indexed: 11/23/2022]
Abstract
In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.
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Affiliation(s)
- Liya Kondratyeva
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
| | - Irina Alekseenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
- Institute of Molecular Genetics of National Research Centre “Kurchatov Institute”, Moscow 123182, Russia
| | - Igor Chernov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
| | - Eugene Sverdlov
- Institute of Molecular Genetics of National Research Centre “Kurchatov Institute”, Moscow 123182, Russia
- Kurchatov Center for Genome Research, National Research Center “Kurchatov Institute”, Moscow 123182, Russia
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Ma X, Xie S, Wang R, Wang Z, Jing M, Li H, Wei S, Liu H, Li J, He Q, Zhao Y. Metabolomics Profiles Associated with the Treatment of Zuojin Pill on Patients with Chronic Nonatrophic Gastritis. Front Pharmacol 2022; 13:898680. [PMID: 35899115 PMCID: PMC9310101 DOI: 10.3389/fphar.2022.898680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: Chronic nonatrophic gastritis (CNG) is the most common digestive disease. In China, Zuojin pill (ZJP) is considered an effective medicine formula for CNG. However, its efficacy and mechanism have never been explored. In order to understand how and why ZJP demonstrates therapeutic effect on CNG, a clinical trial was conducted. Metabolomics was used to explore its deep mechanism. Methods: A total of 14 patients with CNG were recruited from October 2020 to March 2021 (ChiCTR2000040549). The endoscopy and histopathological changes were evaluated as efficacy. Serum samples were prepared and detected by performing widely targeted metabolome using UPLC. Multivariate statistical analysis was conducted to identify potential differential metabolites and signaling pathways. Last, the signal-related inflammatory factors containing COX-2, IL-4, and IL-17 were confirmed via immunohistochemical staining and enzyme-linked immunosorbent assay. Results: ZJP was able to alleviate several indexes of mucosal injury under endoscopy and histology. Erosion and bile reflux, but not red plaques and hemorrhage, were downregulated by ZJP. In addition, it could remarkably alleviate active chronic inflammation. A total of 14 potential metabolites, namely, hypoxanthine, adipic acid, D-ribono-1,4-lactone, L-sepiapterin, imidazoleacetic acid, sebacate, ADP-ribose, 4-hydroxybenzyl alcohol, 11,12-EET, 15-OxoETE, 12-OxoETE, (±)8-HETE, glycyrrhizinate, and DL-aminopimelic acid, were discriminated by metabolomics. Moreover, certain amino acid metabolism got significance during the disease progress and treatment. The related inflammatory factors including COX-2, IL-4, and IL-17 were inhibited by ZJP in both mucosa and serum. Conclusion: All these results indicated that ZJP partially acts as an inflammatory suppressor to regulate comprehensive metabolism disorders. This might be an important mechanism of ZJP in the treatment of CNG.
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Affiliation(s)
- Xiao Ma
- Department of Pharmacy, Chinese PLA General Hospital, Beijing, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shuying Xie
- Department of Pharmacy, Chinese PLA General Hospital, Beijing, China
| | - Ruilin Wang
- Division of Integrative Medicine, The Fifth Medical Center, General Hospital of PLA, Beijing, China
| | - Zhongxia Wang
- Division of Integrative Medicine, The Fifth Medical Center, General Hospital of PLA, Beijing, China
| | - Manyi Jing
- Department of Pharmacy, Chinese PLA General Hospital, Beijing, China
| | - Haotian Li
- Department of Pharmacy, Chinese PLA General Hospital, Beijing, China
| | - Shizhang Wei
- Department of Pharmacy, Chinese PLA General Hospital, Beijing, China
- Department of Anatomy, Histology and Embryology, School of Basic Medical Sciences, Health Science Center, Peking University, Beijing, China
| | - Honghong Liu
- Division of Integrative Medicine, The Fifth Medical Center, General Hospital of PLA, Beijing, China
| | - Jianyu Li
- Division of Integrative Medicine, The Fifth Medical Center, General Hospital of PLA, Beijing, China
| | - Qingyong He
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Qingyong He, ; Yanling Zhao,
| | - Yanling Zhao
- Department of Pharmacy, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Qingyong He, ; Yanling Zhao,
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