1
|
Zheng H, Xu L, Xie H, Xie J, Ma Y, Hu Y, Wu L, Chen J, Wang M, Yi Y, Huang Y, Wang D. RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature. Comput Struct Biotechnol J 2024; 23:1469-1476. [PMID: 38623560 PMCID: PMC11016866 DOI: 10.1016/j.csbj.2024.03.017] [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: 01/24/2024] [Revised: 03/15/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024] Open
Abstract
RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.
Collapse
Affiliation(s)
- Hailong Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Linfu Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Hailong Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jiajing Xie
- National Institute for Data Science in Health and Medicine, Xiamen University, 361102 Xiamen, China
| | - Yapeng Ma
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yongfei Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Le Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Jia Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Meiyi Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Ying Yi
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Yan Huang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
| | - Dong Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, 510515 Guangzhou, China
- Guangdong Province Key Laboratory of Molecular Tumor Pathology, 510515, Guangzhou, China
| |
Collapse
|
2
|
Kasperova BJ, Mraz M, Svoboda P, Hlavacek D, Kratochvilova H, Modos I, Vrzackova N, Ivak P, Janovska P, Kobets T, Mahrik J, Riecan M, Steiner Mrazova L, Stranecky V, Netuka I, Cajka T, Kuda O, Melenovsky V, Stemberkova Hubackova S, Haluzik M. Sodium-glucose cotransporter 2 inhibitors induce anti-inflammatory and anti-ferroptotic shift in epicardial adipose tissue of subjects with severe heart failure. Cardiovasc Diabetol 2024; 23:223. [PMID: 38943140 PMCID: PMC11214218 DOI: 10.1186/s12933-024-02298-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/05/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Sodium-glucose cotransporter 2 inhibitors (SGLT-2i) are glucose-lowering agents used for the treatment of type 2 diabetes mellitus, which also improve heart failure and decrease the risk of cardiovascular complications. Epicardial adipose tissue (EAT) dysfunction was suggested to contribute to the development of heart failure. We aimed to elucidate a possible role of changes in EAT metabolic and inflammatory profile in the beneficial cardioprotective effects of SGLT-2i in subjects with severe heart failure. METHODS 26 subjects with severe heart failure, with reduced ejection fraction, treated with SGLT-2i versus 26 subjects without treatment, matched for age (54.0 ± 2.1 vs. 55.3 ± 2.1 years, n.s.), body mass index (27.8 ± 0.9 vs. 28.8 ± 1.0 kg/m2, n.s.) and left ventricular ejection fraction (20.7 ± 0.5 vs. 23.2 ± 1.7%, n.s.), who were scheduled for heart transplantation or mechanical support implantation, were included in the study. A complex metabolomic and gene expression analysis of EAT obtained during surgery was performed. RESULTS SGLT-2i ameliorated inflammation, as evidenced by the improved gene expression profile of pro-inflammatory genes in adipose tissue and decreased infiltration of immune cells into EAT. Enrichment of ether lipids with oleic acid noted on metabolomic analysis suggests a reduced disposition to ferroptosis, potentially further contributing to decreased oxidative stress in EAT of SGLT-2i treated subjects. CONCLUSIONS Our results show decreased inflammation in EAT of patients with severe heart failure treated by SGLT-2i, as compared to patients with heart failure without this therapy. Modulation of EAT inflammatory and metabolic status could represent a novel mechanism behind SGLT-2i-associated cardioprotective effects in patients with heart failure.
Collapse
Affiliation(s)
- Barbora Judita Kasperova
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
- First Faculty of Medicine, Charles University in Prague, Katerinska 1660/32, 121 08, Prague, Czech Republic
| | - Milos Mraz
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital, U Nemocnice 499/2, 128 08, Prague, Czech Republic
| | - Petr Svoboda
- Centre for Experimental Medicine, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
- Department of Biochemistry and Microbiology, University of Chemistry and Technology Prague, Technicka 5, 166 28, Prague, Czech Republic
| | - Daniel Hlavacek
- Department of Cardiac Surgery, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruska 87, 100 00, Prague, Czech Republic
| | - Helena Kratochvilova
- Centre for Experimental Medicine, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
| | - Istvan Modos
- Department of Informatics, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
| | - Nikola Vrzackova
- Department of Biochemistry and Microbiology, University of Chemistry and Technology Prague, Technicka 5, 166 28, Prague, Czech Republic
| | - Peter Ivak
- Department of Cardiac Surgery, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruska 87, 100 00, Prague, Czech Republic
| | - Petra Janovska
- Department of Adipose Tissue Biology, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 00, Prague, Czech Republic
| | - Tatyana Kobets
- Department of Metabolomics, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 00, Prague, Czech Republic
| | - Jakub Mahrik
- First Faculty of Medicine, Charles University in Prague, Katerinska 1660/32, 121 08, Prague, Czech Republic
- Department of Cardiac Anesthesia, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
| | - Martin Riecan
- Department of Metabolism of Bioactive Lipids, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 00, Prague, Czech Republic
| | - Lenka Steiner Mrazova
- Department of Adipose Tissue Biology, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 00, Prague, Czech Republic
- Research Unit for Rare Diseases, Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Ke Karlovu 455/2, 128 08, Prague, Czech Republic
| | - Viktor Stranecky
- Research Unit for Rare Diseases, Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Ke Karlovu 455/2, 128 08, Prague, Czech Republic
| | - Ivan Netuka
- Department of Cardiac Surgery, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
| | - Tomas Cajka
- Department of Metabolomics, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 00, Prague, Czech Republic
| | - Ondrej Kuda
- Department of Metabolism of Bioactive Lipids, Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 00, Prague, Czech Republic
| | - Vojtech Melenovsky
- Department of Cardiology, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic
| | - Sona Stemberkova Hubackova
- Centre for Experimental Medicine, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic.
| | - Martin Haluzik
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Videnska 1958/9, 140 21, Prague, Czech Republic.
- Institute of Medical Biochemistry and Laboratory Diagnostics, First Faculty of Medicine, Charles University and General University Hospital, U Nemocnice 499/2, 128 08, Prague, Czech Republic.
| |
Collapse
|
3
|
Gualdi F, Oliva B, Piñero J. Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information. NAR Genom Bioinform 2024; 6:lqae049. [PMID: 38745993 PMCID: PMC11091931 DOI: 10.1093/nargab/lqae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/08/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024] Open
Abstract
Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.
Collapse
Affiliation(s)
- Francesco Gualdi
- Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
- Structural Bioinformatics Lab, Research Programme on Biomedical Informatics (SBI-GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Baldomero Oliva
- Structural Bioinformatics Lab, Research Programme on Biomedical Informatics (SBI-GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
| | - Janet Piñero
- Integrative Biomedical Informatics, Research Programme on Biomedical Informatics (IBI-GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, C/Dr Aiguader 88, E-08003 Barcelona, Spain
- Medbioinformatics Solutions SL, Barcelona, Spain
| |
Collapse
|
4
|
Luo D, Tong Z, Wen L, Bai M, Jin X, Liu Z, Li Y, Xue W. DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders. Comput Biol Med 2024; 175:108536. [PMID: 38701592 DOI: 10.1016/j.compbiomed.2024.108536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/15/2024] [Accepted: 04/28/2024] [Indexed: 05/05/2024]
Abstract
In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.
Collapse
Affiliation(s)
- Ding Luo
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China
| | - Zhuohao Tong
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Lu Wen
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Xiaojie Jin
- College of Pharmacy, Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Zerong Liu
- Central Nervous System Drug Key Laboratory of Sichuan Province, Sichuan Credit Pharmaceutical Co., Ltd, Sichuan, 646100, China; Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, 400030, China
| | - Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, China.
| |
Collapse
|
5
|
Parikh V, Tariq A, Patel B, Banerjee I. Comparative Analysis of Fusion Strategies for Imaging and Non-imaging Data - Use-case of Hospital Discharge Prediction. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:652-661. [PMID: 38827051 PMCID: PMC11141810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Accurate prediction of future clinical events such as discharge from hospital can not only improve hospital resource management but also provide an indicator of a patient's clinical condition. Within the scope of this work, we perform a comparative analysis of deep learning based fusion strategies against traditional single source models for prediction of discharge from hospital by fusing information encoded in two diverse but relevant data modalities, i.e., chest X-ray images and tabular electronic health records (EHR). We evaluate multiple fusion strategies including late, early and joint fusion in terms of their efficacy for target prediction compared to EHR-only and Image-only predictive models. Results indicated the importance of merging information from two modalities for prediction as fusion models tended to outperform single modality models and indicate that the joint fusion scheme was the most effective for target prediction. Joint fusion model merges the two modalities through a branched neural network that is jointly trained in an end-to-end fashion to extract target-relevant information from both modalities.
Collapse
|
6
|
Xu M, Li W, He J, Wang Y, Lv J, He W, Chen L, Zhi H. DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm. Int J Mol Sci 2024; 25:5267. [PMID: 38791306 PMCID: PMC11121335 DOI: 10.3390/ijms25105267] [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: 02/29/2024] [Revised: 04/25/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.
Collapse
Affiliation(s)
- Manyi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Jiaheng He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin 150000, China;
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| |
Collapse
|
7
|
Gilchrist JJ, Fang H, Danielli S, Tomkova M, Nassiri I, Ng E, Tong O, Taylor C, Muldoon D, Cohen LRZ, Al-Mossawi H, Lau E, Neville M, Schuster-Boeckler B, Knight JC, Fairfax BP. Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes. CELL GENOMICS 2024; 4:100541. [PMID: 38663408 PMCID: PMC11099345 DOI: 10.1016/j.xgen.2024.100541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/24/2023] [Accepted: 03/27/2024] [Indexed: 05/12/2024]
Abstract
To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.
Collapse
Affiliation(s)
- James J Gilchrist
- Department of Paediatrics, University of Oxford, Oxford OX3 9DU, UK; MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Hai Fang
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Sara Danielli
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Marketa Tomkova
- Ludwig Cancer Research Oxford, University of Oxford, Oxford OX3 7DQ, UK
| | - Isar Nassiri
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK; Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Esther Ng
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Orion Tong
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Chelsea Taylor
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Dylan Muldoon
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Lea R Z Cohen
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Hussein Al-Mossawi
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Evelyn Lau
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LE, UK
| | | | - Julian C Knight
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Benjamin P Fairfax
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK; Department of Oncology, University of Oxford, Oxford OX3 9DS, UK.
| |
Collapse
|
8
|
Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ. Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse. Genetics 2024; 227:iyae031. [PMID: 38531069 PMCID: PMC11075557 DOI: 10.1093/genetics/iyae031] [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: 12/02/2023] [Accepted: 02/13/2024] [Indexed: 03/28/2024] Open
Abstract
Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.
Collapse
Affiliation(s)
| | | | | | | | - Carol J Bult
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| |
Collapse
|
9
|
Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie ME, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D’Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. Database (Oxford) 2024; 2024:baae031. [PMID: 38713862 PMCID: PMC11184451 DOI: 10.1093/database/baae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/23/2024] [Accepted: 04/01/2024] [Indexed: 05/09/2024]
Abstract
Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.
Collapse
Affiliation(s)
- Marija Orlic-Milacic
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Karen Rothfels
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Lisa Matthews
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Adam Wright
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Bijay Jassal
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Veronica Shamovsky
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Quang Trinh
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Marc E Gillespie
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- College of Pharmacy and Health Sciences, St. John’s University, 8000 Utopia Parkway, Queens, NY 11439, USA
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Ralf Stephan
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Bruce May
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Robin Haw
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Joel Weiser
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
| | - Deidre Beavers
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Patrick Conley
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Lincoln D Stein
- Adaptive Oncology, Ontario Institute for Cancer Research, 661 University Avenue Suite 510, Toronto, ON M5G 0A3, Canada
- Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Room 4386, Toronto, ON M5S 1A8, Canada
| | - Peter D’Eustachio
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Guanming Wu
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97239, USA
| |
Collapse
|
10
|
Di Maria A, Bellomo L, Billeci F, Cardillo A, Alaimo S, Ferragina P, Ferro A, Pulvirenti A. NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae194. [PMID: 38597890 DOI: 10.1093/bioinformatics/btae194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/29/2024] [Accepted: 04/08/2024] [Indexed: 04/11/2024]
Abstract
MOTIVATION The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging. RESULTS We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches. AVAILABILITY AND IMPLEMENTATION https://netme.click/.
Collapse
Affiliation(s)
- Antonio Di Maria
- Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy
| | | | - Fabrizio Billeci
- Department of Computer Science, University of Catania, Catania, 95125, Italy
| | - Alfio Cardillo
- Department of Computer Science, University of Catania, Catania, 95125, Italy
| | - Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy
| | - Paolo Ferragina
- Department of Computer Science, University of Pisa, Pisa, 56126 , Italy
| | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy
| |
Collapse
|
11
|
Chen K, Han Y, Wang Y, Zhou D, Wu F, Cai W, Zheng S, Xiao Q, Zhang H, Li W. scMoresDB: A comprehensive database of single-cell multi-omics data for human respiratory system. iScience 2024; 27:109567. [PMID: 38617561 PMCID: PMC11015448 DOI: 10.1016/j.isci.2024.109567] [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: 09/07/2023] [Revised: 11/26/2023] [Accepted: 03/22/2024] [Indexed: 04/16/2024] Open
Abstract
The human respiratory system is a complex and important system that can suffer a variety of diseases. Single-cell sequencing technologies, applied in many respiratory disease studies, have enhanced our ability in characterizing molecular and phenotypic features at a single-cell resolution. The exponentially increasing data from these studies have consequently led to difficulties in data sharing and analysis. Here, we present scMoresDB, a single-cell multi-omics database platform with extensive omics types tailored for human respiratory diseases. scMoresDB re-analyzes single-cell multi-omics datasets, providing a user-friendly interface with cross-omics search capabilities, interactive visualizations, and analytical tools for comprehensive data sharing and integrative analysis. Our example applications highlight the potential significance of BSG receptor in SARS-CoV-2 infection as well as the involvement of HHIP and TGFB2 in the development and progression of chronic obstructive pulmonary disease. scMoresDB significantly increases accessibility and utility of single-cell data relevant to human respiratory system and associated diseases.
Collapse
Affiliation(s)
- Kang Chen
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Yutong Han
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Yanni Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Dingli Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Fanjie Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Wenhao Cai
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Shikang Zheng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Qinyuan Xiao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
- Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, Guangdong Province, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| |
Collapse
|
12
|
Romano JD, Truong V, Kumar R, Venkatesan M, Graham BE, Hao Y, Matsumoto N, Li X, Wang Z, Ritchie MD, Shen L, Moore JH. The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research. J Med Internet Res 2024; 26:e46777. [PMID: 38635981 PMCID: PMC11066745 DOI: 10.2196/46777] [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: 02/24/2023] [Revised: 06/23/2023] [Accepted: 11/07/2023] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.
Collapse
Affiliation(s)
- Joseph D Romano
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Van Truong
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Rachit Kumar
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mythreye Venkatesan
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Britney E Graham
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yun Hao
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nick Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Xi Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Zhiping Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Li Shen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| |
Collapse
|
13
|
Li Y, Yang Y, Tong Z, Wang Y, Mi Q, Bai M, Liang G, Li B, Shu K. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Brief Bioinform 2024; 25:bbae172. [PMID: 38647153 PMCID: PMC11033846 DOI: 10.1093/bib/bbae172] [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: 12/10/2023] [Revised: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.
Collapse
Affiliation(s)
- Yinghong Li
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yinqi Yang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Zhuohao Tong
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Yu Wang
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Qin Mi
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Mingze Bai
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, P. R. China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, P. R. China
| | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, P. R. China
| |
Collapse
|
14
|
Okumura T, Raja Xavier JP, Pasternak J, Yang Z, Hang C, Nosirov B, Singh Y, Admard J, Brucker SY, Kommoss S, Takeda S, Staebler A, Lang F, Salker MS. Rel Family Transcription Factor NFAT5 Upregulates COX2 via HIF-1α Activity in Ishikawa and HEC1a Cells. Int J Mol Sci 2024; 25:3666. [PMID: 38612478 PMCID: PMC11012216 DOI: 10.3390/ijms25073666] [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: 08/01/2023] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 04/14/2024] Open
Abstract
Nuclear factor of activated T cells 5 (NFAT5) and cyclooxygenase 2 (COX2; PTGS2) both participate in diverse pathologies including cancer progression. However, the biological role of the NFAT5-COX2 signaling pathway in human endometrial cancer has remained elusive. The present study explored whether NFAT5 is expressed in endometrial tumors and if NFAT5 participates in cancer progression. To gain insights into the underlying mechanisms, NFAT5 protein abundance in endometrial cancer tissue was visualized by immunohistochemistry and endometrial cancer cells (Ishikawa and HEC1a) were transfected with NFAT5 or with an empty plasmid. As a result, NFAT5 expression is more abundant in high-grade than in low-grade endometrial cancer tissue. RNA sequencing analysis of NFAT5 overexpression in Ishikawa cells upregulated 37 genes and downregulated 20 genes. Genes affected included cyclooxygenase 2 and hypoxia inducible factor 1α (HIF1A). NFAT5 transfection and/or treatment with HIF-1α stabilizer exerted a strong stimulating effect on HIF-1α promoter activity as well as COX2 expression level and prostaglandin E2 receptor (PGE2) levels. Our findings suggest that activation of NFAT5-HIF-1α-COX2 axis could promote endometrial cancer progression.
Collapse
Affiliation(s)
- Toshiyuki Okumura
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
- Department of Obstetrics and Gynecology, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan;
| | - Janet P. Raja Xavier
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| | - Jana Pasternak
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| | - Zhiqi Yang
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| | - Cao Hang
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| | - Bakhtiyor Nosirov
- Department of Cancer Research, Luxembourg Institute of Health, L-1210 Luxembourg, Luxembourg
| | - Yogesh Singh
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
- Institute of Medical Genetics and Applied Genomics, Eberhard Karls University, D-72074 Tübingen, Germany;
| | - Jakob Admard
- Institute of Medical Genetics and Applied Genomics, Eberhard Karls University, D-72074 Tübingen, Germany;
| | - Sara Y. Brucker
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| | - Stefan Kommoss
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| | - Satoru Takeda
- Department of Obstetrics and Gynecology, Faculty of Medicine, Juntendo University, Tokyo 113-8421, Japan;
| | - Annette Staebler
- Institute of Pathology, Eberhard Karls University, D-72074 Tübingen, Germany;
| | - Florian Lang
- Institute of Physiology, Eberhard Karls University, D-72074 Tübingen, Germany;
| | - Madhuri S. Salker
- Department of Women’s Health, Tübingen University Hospital, D-72076 Tübingen, Germany; (T.O.); (J.P.R.X.); (J.P.); (C.H.); (Y.S.); (S.Y.B.); (S.K.)
| |
Collapse
|
15
|
Ermshaus A, Piechotta M, Rüter G, Keilholz U, Leser U, Benary M. preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology. Bioinformatics 2024; 40:btae085. [PMID: 38383060 PMCID: PMC10918631 DOI: 10.1093/bioinformatics/btae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 01/15/2024] [Accepted: 02/20/2024] [Indexed: 02/23/2024] Open
Abstract
MOTIVATION In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients. SUMMARY preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. AVAILABILITY AND IMPLEMENTATION preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.
Collapse
Affiliation(s)
- Arik Ermshaus
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Michael Piechotta
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Gina Rüter
- Charite Comprehensive Cancer Center, Charite—Universitätsmedizin Berlin, Berlin 10115, Germany
| | - Ulrich Keilholz
- Charite Comprehensive Cancer Center, Charite—Universitätsmedizin Berlin, Berlin 10115, Germany
| | - Ulf Leser
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Manuela Benary
- Charite Comprehensive Cancer Center, Charite—Universitätsmedizin Berlin, Berlin 10115, Germany
- Core Unit Bioinformatics (CUBI), Berlin Institute of Health, Charite—Universitätsmedizin Berlin, Berlin 10115, Germany
| |
Collapse
|
16
|
Huang DL, Zeng Q, Xiong Y, Liu S, Pang C, Xia M, Fang T, Ma Y, Qiang C, Zhang Y, Zhang Y, Li H, Yuan Y. A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature. Interdiscip Sci 2024:10.1007/s12539-024-00605-2. [PMID: 38340264 DOI: 10.1007/s12539-024-00605-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/12/2024]
Abstract
We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available. The overall Jaccard index was calculated to be 0.866 for the four entity types-gene, variant, disease and species. Both a BERT-based large name entity recognition (NER) model and a DistilBERT-based simplified NER model were trained, validated and tested, respectively. Due to the limited manually annotated corpus, Such NER models were fine-tuned with two phases. The F1-scores of BERT-based NER for gene, variant, disease and species are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those of DistilBERT-based NER are 95.14%, 86.26%, 91.37% and 89.92%, respectively. Most importantly, the entity type of variant has been extracted by a large language model for the first time and a comparable F1-score with the state-of-the-art variant extraction model tmVar has been achieved.
Collapse
Affiliation(s)
- Dao-Ling Huang
- BGI Research, Shenzhen, 518083, China.
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Quanlei Zeng
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yun Xiong
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Shuixia Liu
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Chaoqun Pang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Menglei Xia
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Ting Fang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yanli Ma
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Cuicui Qiang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yi Zhang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yu Zhang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Hong Li
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yuying Yuan
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China
| |
Collapse
|
17
|
Kiran A, Hanachi M, Alsayed N, Fassatoui M, Oduaran OH, Allali I, Maslamoney S, Meintjes A, Zass L, Rocha JD, Kefi R, Benkahla A, Ghedira K, Panji S, Mulder N, Fadlelmola FM, Souiai O. The African Human Microbiome Portal: a public web portal of curated metagenomic metadata. Database (Oxford) 2024; 2024:baad092. [PMID: 38204360 PMCID: PMC10782148 DOI: 10.1093/database/baad092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/03/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL: https://microbiome.h3abionet.org/.
Collapse
Affiliation(s)
| | - Mariem Hanachi
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
- Faculty of Science of Bizerte, University of Carthage, Tunis, Tunisia
| | - Nihad Alsayed
- Kush Centre for Genomics and Biomedical Informatics, Biotechnology Perspectives Organization, Khartoum, Sudan
| | - Meriem Fassatoui
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Ovokeraye H Oduaran
- The Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Imane Allali
- Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
| | - Suresh Maslamoney
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Ayton Meintjes
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Lyndon Zass
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Jorge Da Rocha
- The Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Rym Kefi
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Faisal M Fadlelmola
- Kush Centre for Genomics and Biomedical Informatics, Biotechnology Perspectives Organization, Khartoum, Sudan
| | - Oussema Souiai
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
- Malawi-Liverpool-Wellcome Trust, Blantyre 3, Malawi
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool CH64 7TE, UK
| |
Collapse
|
18
|
Mancuso CA, Johnson KA, Liu R, Krishnan A. Joint representation of molecular networks from multiple species improves gene classification. PLoS Comput Biol 2024; 20:e1011773. [PMID: 38198480 PMCID: PMC10805316 DOI: 10.1371/journal.pcbi.1011773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/23/2024] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts.
Collapse
Affiliation(s)
- Christopher A. Mancuso
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Kayla A. Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| |
Collapse
|
19
|
Lu S, Liang Y, Li L, Miao R, Liao S, Zou Y, Yang C, Ouyang D. Predicting potential microbe-disease associations based on auto-encoder and graph convolution network. BMC Bioinformatics 2023; 24:476. [PMID: 38097930 PMCID: PMC10722760 DOI: 10.1186/s12859-023-05611-7] [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: 10/25/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.
Collapse
Affiliation(s)
- Shanghui Lu
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China
- School of Mathematics and Physics, Hechi University, No. 42, Longjiang, Hechi, 546300, Guangxi, China
| | - Yong Liang
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China.
- Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China.
| | - Le Li
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China
| | - Rui Miao
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhuhai, 519041, Guangdong, China
| | - Shuilin Liao
- Faculty of Innovation Enginee, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao, Macao Special Administrative Region of China, China
| | - Yongfu Zou
- School of Mathematics and Physics, Hechi University, No. 42, Longjiang, Hechi, 546300, Guangxi, China
| | - Chengjun Yang
- School of Artificial Intelligence and Manufacturing, Hechi University, No. 42, Longjiang, Hechi, 546300, Guangxi, China
| | - Dong Ouyang
- School of Biomedical Engineering, Guangdong Medical University, No. 1, Xincheng, Zhanjiang, 523808, Guangdong, China
| |
Collapse
|
20
|
Mohr SE, Kim AR, Hu Y, Perrimon N. Finding information about uncharacterized Drosophila melanogaster genes. Genetics 2023; 225:iyad187. [PMID: 37933691 PMCID: PMC10697813 DOI: 10.1093/genetics/iyad187] [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: 07/31/2023] [Accepted: 10/02/2023] [Indexed: 11/08/2023] Open
Abstract
Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.
Collapse
Affiliation(s)
- Stephanie E Mohr
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Ah-Ram Kim
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Blavatnik Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02115, USA
| |
Collapse
|
21
|
Panyard DJ, McKetney J, Deming YK, Morrow AR, Ennis GE, Jonaitis EM, Van Hulle CA, Yang C, Sung YJ, Ali M, Kollmorgen G, Suridjan I, Bayfield A, Bendlin BB, Zetterberg H, Blennow K, Cruchaga C, Carlsson CM, Johnson SC, Asthana S, Coon JJ, Engelman CD. Large-scale proteome and metabolome analysis of CSF implicates altered glucose and carbon metabolism and succinylcarnitine in Alzheimer's disease. Alzheimers Dement 2023; 19:5447-5470. [PMID: 37218097 PMCID: PMC10663389 DOI: 10.1002/alz.13130] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/23/2023] [Accepted: 04/04/2023] [Indexed: 05/24/2023]
Abstract
INTRODUCTION A hallmark of Alzheimer's disease (AD) is the aggregation of proteins (amyloid beta [A] and hyperphosphorylated tau [T]) in the brain, making cerebrospinal fluid (CSF) proteins of particular interest. METHODS We conducted a CSF proteome-wide analysis among participants of varying AT pathology (n = 137 participants; 915 proteins) with nine CSF biomarkers of neurodegeneration and neuroinflammation. RESULTS We identified 61 proteins significantly associated with the AT category (P < 5.46 × 10-5 ) and 636 significant protein-biomarker associations (P < 6.07 × 10-6 ). Proteins from glucose and carbon metabolism pathways were enriched among amyloid- and tau-associated proteins, including malate dehydrogenase and aldolase A, whose associations with tau were replicated in an independent cohort (n = 717). CSF metabolomics identified and replicated an association of succinylcarnitine with phosphorylated tau and other biomarkers. DISCUSSION These results implicate glucose and carbon metabolic dysregulation and increased CSF succinylcarnitine levels with amyloid and tau pathology in AD. HIGHLIGHTS Cerebrospinal fluid (CSF) proteome enriched for extracellular, neuronal, immune, and protein processing. Glucose/carbon metabolic pathways enriched among amyloid/tau-associated proteins. Key glucose/carbon metabolism protein associations independently replicated. CSF proteome outperformed other omics data in predicting amyloid/tau positivity. CSF metabolomics identified and replicated a succinylcarnitine-phosphorylated tau association.
Collapse
Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Justin McKetney
- National Center for Quantitative Biology of Complex Systems, University of Wisconsin-Madison; Madison, WI 53706, United States of America
- Department of Biomolecular Chemistry, University of Wisconsin-Madison; Madison, WI 53506, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Autumn R. Morrow
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Gilda E. Ennis
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
| | - Erin M. Jonaitis
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | - Muhammad Ali
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | | | | | | | - Barbara B. Bendlin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg; Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital; Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology; London, UK
- UK Dementia Research Institute at UCL; London, UK
- Hong Kong Center for Neurodegenerative Diseases; Hong Kong, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg; Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital; Mölndal, Sweden
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine; St Louis, MO 63110, United States of America
- NeuroGenomics and Informatics Center, Washington University School of Medicine; St Louis, MO 63110, United States of America
- Hope Center for Neurological Disorders, Washington University School of Medicine; St Louis, MO 63110, United States of America
| | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison; 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison; 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison; 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital; 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Joshua J. Coon
- National Center for Quantitative Biology of Complex Systems, University of Wisconsin-Madison; Madison, WI 53706, United States of America
- Department of Biomolecular Chemistry, University of Wisconsin-Madison; Madison, WI 53506, United States of America
- Morgridge Institute for Research; Madison, WI 53706, United States of America
- Department of Chemistry, University of Wisconsin-Madison; Madison, WI 53506, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison; 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| |
Collapse
|
22
|
Huang ZM, Kang JQ, Chen PZ, Deng LF, Li JX, He YX, Liang J, Huang N, Luo TY, Lan QW, Chen HK, Guo XG. Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning. Biochem Genet 2023:10.1007/s10528-023-10563-x. [PMID: 37991568 DOI: 10.1007/s10528-023-10563-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/25/2023] [Indexed: 11/23/2023]
Abstract
The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein-protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB.
Collapse
Affiliation(s)
- Ze-Min Huang
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Jia-Qi Kang
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The First Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Pei-Zhen Chen
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Lin-Fen Deng
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Jia-Xin Li
- Department of Clinical Medicine, The First Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Ying-Xin He
- Clinical Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510006, China
| | - Jie Liang
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Nan Huang
- Clinical Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510006, China
| | - Tian-Ye Luo
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Qi-Wen Lan
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Hao-Kai Chen
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China
| | - Xu-Guang Guo
- Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
- Department of Clinical Laboratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China.
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, King Med School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510000, China.
| |
Collapse
|
23
|
Cuenca-Guardiola J, Morena-Barrio BDL, Navarro-Manzano E, Stevens J, Ouwehand WH, Gleadall NS, Corral J, Fernández-Breis JT. Detection and annotation of transposable element insertions and deletions on the human genome using nanopore sequencing. iScience 2023; 26:108214. [PMID: 37953943 PMCID: PMC10638045 DOI: 10.1016/j.isci.2023.108214] [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: 02/06/2023] [Revised: 07/28/2023] [Accepted: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Repetitive sequences represent about 45% of the human genome. Some are transposable elements (TEs) with the ability to change their position in the genome, creating genetic variability both as insertions or deletions, with potential pathogenic consequences. We used long-read nanopore sequencing to identify TE variants in the genomes of 24 patients with antithrombin deficiency. We identified 7 344 TE insertions and 3 056 TE deletions, 2 926 were not previously described in publicly available databases. The insertions affected 3 955 genes, with 6 insertions located in exons, 3 929 in introns, and 147 in promoters. Potential functional impact was evaluated with gene annotation and enrichment analysis, which suggested a strong relationship with neuron-related functions and autism. We conclude that this study encourages the generation of a complete map of TEs in the human genome, which will be useful for identifying new TEs involved in genetic disorders.
Collapse
Affiliation(s)
- Javier Cuenca-Guardiola
- Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, IMIB-Pascual Parrilla, Facultad de Informática, Campus de Espinardo, Murcia 30100, Spain
| | - Belén de la Morena-Barrio
- Servicio de Hematología, Hospital Universitario Morales Meseguer, Centro Regional de Hemodonación, Universidad de Murcia, IMIB-Pascual Parrilla, CIBERER-III, Ronda de Garay S/N, Murcia 30003, Spain
| | - Esther Navarro-Manzano
- Servicio de Hematología, Hospital Universitario Morales Meseguer, Centro Regional de Hemodonación, Universidad de Murcia, IMIB-Pascual Parrilla, CIBERER-III, Ronda de Garay S/N, Murcia 30003, Spain
| | - Jonathan Stevens
- Department of Haematology, University of Cambridge, CB2 0PT, Cambridge Biomedical Campus, Cambridge, Cambridge, England, UK
- Blood and Transplant, National Health Service (NHS), CB2 0QQ, Cambridge Biomedical Campus, Cambridge, England, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, CB2 0PT, Cambridge Biomedical Campus, Cambridge, Cambridge, England, UK
- Blood and Transplant, National Health Service (NHS), CB2 0QQ, Cambridge Biomedical Campus, Cambridge, England, UK
- British Heart Foundation Cambridge Centre of Excellence, Division of Cardiovascular Medicine, Cambridge Heart and Lung Research Institute, Cambridge Biomedical Campus, Cambridge, England CB2 0AY, UK
- University College London Hospitals, NHS Foundation Trust, London, England, UK
| | - Nicholas S Gleadall
- Department of Haematology, University of Cambridge, CB2 0PT, Cambridge Biomedical Campus, Cambridge, Cambridge, England, UK
- Blood and Transplant, National Health Service (NHS), CB2 0QQ, Cambridge Biomedical Campus, Cambridge, England, UK
| | - Javier Corral
- Servicio de Hematología, Hospital Universitario Morales Meseguer, Centro Regional de Hemodonación, Universidad de Murcia, IMIB-Pascual Parrilla, CIBERER-III, Ronda de Garay S/N, Murcia 30003, Spain
| | - Jesualdo Tomás Fernández-Breis
- Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, IMIB-Pascual Parrilla, Facultad de Informática, Campus de Espinardo, Murcia 30100, Spain
| |
Collapse
|
24
|
Wu Z, Chen X, Wu S, Liu Z, Li H, Mai K, Peng Y, Zhang H, Zhang X, Zheng Z, Fu Z, Chen D. Transcriptome analysis reveals the impact of NETs activation on airway epithelial cell EMT and inflammation in bronchiolitis obliterans. Sci Rep 2023; 13:19226. [PMID: 37932341 PMCID: PMC10628238 DOI: 10.1038/s41598-023-45617-y] [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: 06/09/2023] [Accepted: 10/21/2023] [Indexed: 11/08/2023] Open
Abstract
Bronchiolitis obliterans (BO) is a chronic airway disease that was often indicated by the pathological presentation of narrowed and irreversible airways. However, the molecular mechanisms of BO pathogenesis remain unknown. Although neutrophil extracellular traps (NETs) can contribute to inflammatory disorders, their involvement in BO is unclear. This study aims to identify potential signaling pathways in BO by exploring the correlations between NETs and BO. GSE52761 and GSE137169 datasets were downloaded from gene expression omnibus (GEO) database. A series of bioinformatics analyses such as differential expression analysis, gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA) were performed on GSE52761 and GSE137169 datasets to identify BO potential signaling pathways. Two different types of BO mouse models were constructed to verify NETs involvements in BO. Additional experiments and bioinformatics analysis using human small airway epithelial cells (SAECs) were also performed to further elucidate differential genes enrichment with their respective signaling pathways in BO. Our study identified 115 differentially expressed genes (DEGs) that were found up-regulated in BO. Pathway enrichment analysis revealed that these genes were primarily involved in inflammatory signaling processes. Besides, we found that neutrophil extracellular traps (NETs) were formed and activated during BO. Our western blot analysis on lung tissue from BO mice further confirmed NETs activation in BO, where neutrophil elastase (NE) and myeloperoxidase (MPO) expression were found significantly elevated. Transcriptomic and bioinformatics analysis of NETs treated-SAECs also revealed that NETs-DEGs were primarily associated through inflammatory and epithelial-to-mesenchymal transition (EMT) -related pathways. Our study provides novel clues towards the understanding of BO pathogenesis, in which NETs contribute to BO pathogenesis through the activation of inflammatory and EMT associated pathways. The completion of our study will provide the basis for potential novel therapeutic targets in BO treatment.
Collapse
Affiliation(s)
- Zhongji Wu
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Xiaowen Chen
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Shangzhi Wu
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Zhenwei Liu
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Hongwei Li
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Kailin Mai
- Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Yinghui Peng
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Haidi Zhang
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Xiaodie Zhang
- Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Zhaocong Zheng
- Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Zian Fu
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China
| | - Dehui Chen
- The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510000, People's Republic of China.
| |
Collapse
|
25
|
Foos G, Blazeska N, Nielsen M, Carter H, Kosaloglu-Yalcin Z, Peters B, Sette A. A meta-analysis of epitopes in prostate-specific antigens identifies opportunities and knowledge gaps. Hum Immunol 2023; 84:578-589. [PMID: 37679223 PMCID: PMC11017785 DOI: 10.1016/j.humimm.2023.08.145] [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: 03/29/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND The Cancer Epitope Database and Analysis Resource (CEDAR) is a newly developed repository of cancer epitope data from peer-reviewed publications, which includes epitope-specific T cell, antibody, and MHC ligand assays. Here we focus on prostate cancer as our first cancer category to demonstrate the capabilities of CEDAR, and to shed light on the advances of epitope-related prostate cancer research. RESULTS The meta-analysis focused on a subset of data describing epitopes from 8 prostate-specific (PS) antigens. A total of 460 epitopes were associated with these proteins, 187 T cell, 109B cell, and 271 MHC ligand epitopes. The number of epitopes was not correlated with the length of the protein; however, we found a significant positive correlation between the number of references per specific PS antigen and the number of reported epitopes. Forty-four different class I and 27 class II restrictions were found, with the most epitopes described for HLA-A*02:01 and HLA-DRB1*01:01. Cytokine assays were mostly limited to IFNg assays and a very limited number of tetramer assays were performed. Monoclonal and polyclonal B cell responses were balanced, with the highest number of epitopes studied in ELISA/Western blot assays. Additionally, epitopes were generically described as associated with prostate cancer, with little granularity specifying diseases state. We found that in vivo and tumor recognition assays were sparse, and the number of epitopes with annotated B/T cell receptor information were limited. Potential immunodominant regions were identified by the use of the ImmunomeBrowser tool. CONCLUSION CEDAR provides a comprehensive repository of epitopes related to prostate-specific antigens. This inventory of epitope data with its wealth of searchable T cell, B cell and MHC ligand information provides a useful tool for the scientific community. At the same time, we identify significant knowledge gaps that could be addressed by experimental analysis.
Collapse
Affiliation(s)
- Gabriele Foos
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Nina Blazeska
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Morten Nielsen
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martín, Argentina; Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Zeynep Kosaloglu-Yalcin
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA 92037, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA 92037, USA; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA 92037, USA.
| |
Collapse
|
26
|
Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie M, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D'Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.18.562964. [PMID: 37904913 PMCID: PMC10614924 DOI: 10.1101/2023.10.18.562964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Disease variant annotation in the context of biological reactions and pathways can provide a standardized overview of molecular phenotypes of pathogenic mutations that is amenable to computational mining and mathematical modeling. Reactome, an open source, manually curated, peer-reviewed database of human biological pathways, provides annotations for over 4000 disease variants of close to 400 genes in the context of ∼800 disease reactions constituting ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics (ACMG). Reactome's pathway-based, reaction-specific disease variant dataset and data model provide a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity.
Collapse
|
27
|
Budu-Aggrey A, Kilanowski A, Sobczyk MK, Shringarpure SS, Mitchell R, Reis K, Reigo A, Mägi R, Nelis M, Tanaka N, Brumpton BM, Thomas LF, Sole-Navais P, Flatley C, Espuela-Ortiz A, Herrera-Luis E, Lominchar JVT, Bork-Jensen J, Marenholz I, Arnau-Soler A, Jeong A, Fawcett KA, Baurecht H, Rodriguez E, Alves AC, Kumar A, Sleiman PM, Chang X, Medina-Gomez C, Hu C, Xu CJ, Qi C, El-Heis S, Titcombe P, Antoun E, Fadista J, Wang CA, Thiering E, Wu B, Kress S, Kothalawala DM, Kadalayil L, Duan J, Zhang H, Hadebe S, Hoffmann T, Jorgenson E, Choquet H, Risch N, Njølstad P, Andreassen OA, Johansson S, Almqvist C, Gong T, Ullemar V, Karlsson R, Magnusson PKE, Szwajda A, Burchard EG, Thyssen JP, Hansen T, Kårhus LL, Dantoft TM, Jeanrenaud ACSN, Ghauri A, Arnold A, Homuth G, Lau S, Nöthen MM, Hübner N, Imboden M, Visconti A, Falchi M, Bataille V, Hysi P, Ballardini N, Boomsma DI, Hottenga JJ, Müller-Nurasyid M, Ahluwalia TS, Stokholm J, Chawes B, Schoos AMM, Esplugues A, Bustamante M, Raby B, Arshad S, German C, Esko T, Milani LA, Metspalu A, Terao C, Abuabara K, Løset M, Hveem K, Jacobsson B, Pino-Yanes M, Strachan DP, Grarup N, Linneberg A, Lee YA, Probst-Hensch N, Weidinger S, Jarvelin MR, Melén E, Hakonarson H, Irvine AD, Jarvis D, Nijsten T, Duijts L, Vonk JM, Koppelmann GH, Godfrey KM, Barton SJ, Feenstra B, Pennell CE, Sly PD, Holt PG, Williams LK, Bisgaard H, Bønnelykke K, Curtin J, Simpson A, Murray C, Schikowski T, Bunyavanich S, Weiss ST, Holloway JW, Min JL, Brown SJ, Standl M, Paternoster L. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Nat Commun 2023; 14:6172. [PMID: 37794016 PMCID: PMC10550990 DOI: 10.1038/s41467-023-41180-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.
Collapse
Affiliation(s)
- Ashley Budu-Aggrey
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | - Anna Kilanowski
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians University Munich, Munich, Germany
| | - Maria K Sobczyk
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | | | - Ruth Mitchell
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | - Kadri Reis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anu Reigo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mari Nelis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Core Facility of Genomics, University of Tartu, Tartu, Estonia
| | - Nao Tanaka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, 7030, Norway
| | - Laurent F Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- Department of Clinical and Molecular Medicine, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Pol Sole-Navais
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Christopher Flatley
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Antonio Espuela-Ortiz
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
| | - Esther Herrera-Luis
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
| | - Jesus V T Lominchar
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Ingo Marenholz
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Aleix Arnau-Soler
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ayoung Jeong
- Swiss Tropical and Public Health Institute, CH-4123, Basel, Switzerland
- University of Basel, CH-4001, Basel, Switzerland
| | - Katherine A Fawcett
- Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Hansjorg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Elke Rodriguez
- Department of Dermatology and Allergy, University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Ashish Kumar
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Solna, Sweden
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Rhythm Pharmaceuticals, 222 Berkley Street, Boston, 02116, USA
| | - Xiao Chang
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Carolina Medina-Gomez
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Chen Hu
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Cheng-Jian Xu
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergy, Beatrix Children's Hospital, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
- Centre for Individualized Infection Medicine, CiiM, a joint venture between Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Cancan Qi
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergy, Beatrix Children's Hospital, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Sarah El-Heis
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Philip Titcombe
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Elie Antoun
- Faculty of Medicine, University of Southampton, Southampton, UK
- Institute of Developmental Sciences, University of Southampton, Southampton, UK
| | - João Fadista
- Department of Bioinformatics & Data Mining, Måløv, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Carol A Wang
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Baojun Wu
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Medicine, Henry Ford Health, Detroit, MI, 48104, USA
| | - Sara Kress
- Environmental Epidemiology of Lung, Brain and Skin Aging, IUF - Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Dilini M Kothalawala
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Latha Kadalayil
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Jiasong Duan
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Hongmei Zhang
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Sabelo Hadebe
- Division of Immunology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Thomas Hoffmann
- Institute for Human Genetics, UCSF, San Francisco, CA, 94143, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, 94158, USA
| | | | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Neil Risch
- Institute for Human Genetics, UCSF, San Francisco, CA, 94143, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, 94158, USA
| | - Pål Njølstad
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, NO-5020, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, NO-5021, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, 0450, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, 0450, Oslo, Norway
| | - Stefan Johansson
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, NO-5020, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, NO-5021, Bergen, Norway
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pediatric Lung and Allergy Unit, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Vilhelmina Ullemar
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Agnieszka Szwajda
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Jacob P Thyssen
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Line L Kårhus
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
| | - Thomas M Dantoft
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
| | - Alexander C S N Jeanrenaud
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ahla Ghauri
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Arnold
- Clinic and Polyclinic of Dermatology, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Susanne Lau
- Department of Pediatric Respiratory Medicine, Immunology, and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Norbert Hübner
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Medea Imboden
- Swiss Tropical and Public Health Institute, CH-4123, Basel, Switzerland
- University of Basel, CH-4001, Basel, Switzerland
| | - Alessia Visconti
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
| | - Mario Falchi
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
| | - Veronique Bataille
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
- Dermatology Department, West Herts NHS Trust, Watford, UK
| | - Pirro Hysi
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
| | - Natalia Ballardini
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Solna, Sweden
| | - Dorret I Boomsma
- Dept Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
- Institute for Health and Care Research (EMGO), VU University, Amsterdam, the Netherlands
| | - Jouke J Hottenga
- Dept Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Tarunveer S Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jakob Stokholm
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Slagelse Hospital, Slagelse, Denmark
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Ann-Marie M Schoos
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Slagelse Hospital, Slagelse, Denmark
| | - Ana Esplugues
- Nursing School, University of Valencia, FISABIO-University Jaume I-University of Valencia, Valencia, Spain
- Joint Research Unit of Epidemiology and Environmental Health, CIBERESP, Valencia, Spain
| | - Mariona Bustamante
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Benjamin Raby
- Channing Division of Network Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Syed Arshad
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- David Hide Asthma and Allergy Research Centre, Isle of Wight, UK
| | | | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lili A Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Katrina Abuabara
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Mari Løset
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- Department of Dermatology, Clinic of Orthopaedy, Rheumatology and Dermatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Young-Ae Lee
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, CH-4123, Basel, Switzerland
- University of Basel, CH-4001, Basel, Switzerland
| | - Stephan Weidinger
- Department of Dermatology, Allergology and Venereology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health,Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Erik Melén
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Solna, Sweden
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Divisions of Human Genetics and Pulmonary Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Faculty of Medicine, University of Iceland, 101, Reykjavík, Iceland
| | - Alan D Irvine
- Department of Clinical Medicine, Trinity College, Dublin, Ireland
| | - Deborah Jarvis
- Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Medical Research Council and Public Health England Centre for Environment and Health, London, United Kingdom
| | - Tamar Nijsten
- Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Liesbeth Duijts
- Department of Pediatrics, division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, division of Neonatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Judith M Vonk
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Gerard H Koppelmann
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergy, Beatrix Children's Hospital, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Centre and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Sheila J Barton
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Peter D Sly
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane, 4101, Queensland, Australia
- Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia, 4072, QLD, Australia
| | - Patrick G Holt
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Medicine, Henry Ford Health, Detroit, MI, 48104, USA
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - John Curtin
- Division of Immunology, Immunity to Infection and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, England
| | - Angela Simpson
- Division of Immunology, Immunity to Infection and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, England
| | - Clare Murray
- Division of Immunology, Immunity to Infection and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, England
| | - Tamara Schikowski
- Environmental Epidemiology of Lung, Brain and Skin Aging, Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Supinda Bunyavanich
- Division of Allergy and Immunology, Department of Pediatrics, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Josine L Min
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | - Sara J Brown
- Centre for Genomics and Experimental Medicine, Institute for Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, UK EH4 2XU, Scotland
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Lung Research (DZL), Munich, Germany
| | - Lavinia Paternoster
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England.
| |
Collapse
|
28
|
Hu X, Liu D, Zhang J, Fan Y, Ouyang T, Luo Y, Zhang Y, Deng L. A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations. Brief Bioinform 2023; 24:bbad410. [PMID: 37985451 DOI: 10.1093/bib/bbad410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/07/2023] [Accepted: 10/25/2023] [Indexed: 11/22/2023] Open
Abstract
Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.
Collapse
Affiliation(s)
- Xiaowen Hu
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Jiaxuan Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego,92093 CA, USA
| | - Yanhao Fan
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Tianxiang Ouyang
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Yue Luo
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Yuanpeng Zhang
- school of software, Xinjiang University, 830046 Urumqi, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| |
Collapse
|
29
|
Wu J, Ning Z, Ding Y, Wang Y, Peng Q, Fu L. KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations. Brief Bioinform 2023; 24:bbad292. [PMID: 37587836 DOI: 10.1093/bib/bbad292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/27/2023] [Accepted: 07/27/2023] [Indexed: 08/18/2023] Open
Abstract
Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at https://github.com/jinyangwu/KGETCDA.
Collapse
Affiliation(s)
- Jinyang Wu
- School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China
| | - Zhiwei Ning
- School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China
| | - Yidong Ding
- School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China
| | - Ying Wang
- School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China
| | - Qinke Peng
- School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China
| | - Laiyi Fu
- School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China
- Research Institute of Xi'an Jiaotong University, 311200, Zhejiang, China
- Sichuan Digital Economy Industry Development Research Institute, 610036, Sichuan, China
| |
Collapse
|
30
|
Pividori M, Lu S, Li B, Su C, Johnson ME, Wei WQ, Feng Q, Namjou B, Kiryluk K, Kullo IJ, Luo Y, Sullivan BD, Voight BF, Skarke C, Ritchie MD, Grant SFA, Greene CS. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nat Commun 2023; 14:5562. [PMID: 37689782 PMCID: PMC10492839 DOI: 10.1038/s41467-023-41057-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 08/18/2023] [Indexed: 09/11/2023] Open
Abstract
Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.
Collapse
Affiliation(s)
- Milton Pividori
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, 10032, USA
| | | | - Yuan Luo
- Northwestern University, Chicago, IL, 60611, USA
| | - Blair D Sullivan
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
| | - Benjamin F Voight
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Struan F A Grant
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
| |
Collapse
|
31
|
Rumpler É, Göcz B, Skrapits K, Sárvári M, Takács S, Farkas I, Póliska S, Papp M, Solymosi N, Hrabovszky E. Development of a versatile LCM-Seq method for spatial transcriptomics of fluorescently tagged cholinergic neuron populations. J Biol Chem 2023; 299:105121. [PMID: 37536628 PMCID: PMC10477691 DOI: 10.1016/j.jbc.2023.105121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/29/2023] [Accepted: 07/20/2023] [Indexed: 08/05/2023] Open
Abstract
Single-cell transcriptomics are powerful tools to define neuronal cell types based on co-expressed gene clusters. Limited RNA input in these technologies necessarily compromises transcriptome coverage and accuracy of differential expression analysis. We propose that bulk RNA-Seq of neuronal pools defined by spatial position offers an alternative strategy to overcome these technical limitations. We report a laser-capture microdissection (LCM)-Seq method which allows deep transcriptome profiling of fluorescently tagged neuron populations isolated with LCM from histological sections of transgenic mice. Mild formaldehyde fixation of ZsGreen marker protein, LCM sampling of ∼300 pooled neurons, followed by RNA isolation, library preparation and RNA-Seq with methods optimized for nanogram amounts of moderately degraded RNA enabled us to detect ∼15,000 different transcripts in fluorescently labeled cholinergic neuron populations. The LCM-Seq approach showed excellent accuracy in quantitative studies, allowing us to detect 2891 transcripts expressed differentially between the spatially defined and clinically relevant cholinergic neuron populations of the dorsal caudate-putamen and medial septum. In summary, the LCM-Seq method we report in this study is a versatile, sensitive, and accurate bulk sequencing approach to study the transcriptome profile and differential gene expression of fluorescently tagged neuronal populations isolated from transgenic mice with high spatial precision.
Collapse
Affiliation(s)
- Éva Rumpler
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary.
| | - Balázs Göcz
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary.
| | - Katalin Skrapits
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Miklós Sárvári
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Szabolcs Takács
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Imre Farkas
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary
| | - Szilárd Póliska
- Faculty of Medicine, Department of Biochemistry and Molecular Biology, University of Debrecen, Debrecen, Hungary
| | - Márton Papp
- Centre for Bioinformatics, University of Veterinary Medicine, Budapest, Hungary
| | - Norbert Solymosi
- Centre for Bioinformatics, University of Veterinary Medicine, Budapest, Hungary; Department of Physics of Complex Systems, Eötvös Loránd University, Budapest, Hungary
| | - Erik Hrabovszky
- Laboratory of Reproductive Neurobiology, Institute of Experimental Medicine, Budapest, Hungary.
| |
Collapse
|
32
|
Xu W, Duan L, Zheng H, Li-Ling J, Jiang W, Zhang Y, Wang T, Qin R. An Integrative Disease Information Network Approach to Similar Disease Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2724-2735. [PMID: 34478379 DOI: 10.1109/tcbb.2021.3110127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrative approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established first. Then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.
Collapse
|
33
|
Wang X, Zhao W, Zhang X, Wang Z, Han C, Xu J, Yang G, Peng J, Li Z. An integrative analysis to predict the active compounds and explore polypharmacological mechanisms of Orthosiphon stamineus Benth. Comput Biol Med 2023; 163:107160. [PMID: 37321099 DOI: 10.1016/j.compbiomed.2023.107160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Orthosiphon stamineus Benth is a dietary supplement and traditional Chinese herb with widespread clinical applications, but a comprehensive understanding of its active compounds and polypharmacological mechanisms is lacking. This study aimed to systematically investigate the natural compounds and molecular mechanisms of O. stamineus via network pharmacology. METHODS Information on compounds from O. stamineus was collected via literature retrieval, while physicochemical properties and drug-likeness were evaluated using SwissADME. Protein targets were screened using SwissTargetPrediction, while the compound-target networks were constructed and analyzed via Cytoscape with CytoHubba for seed compounds and core targets. Enrichment analysis and disease ontology analysis were then carried out, generating target-function and compound-target-disease networks to intuitively explore potential pharmacological mechanisms. Lastly, the relationship between active compounds and targets was confirmed via molecular docking and dynamics simulation. RESULTS A total of 22 key active compounds and 65 targets were identified and the main polypharmacological mechanisms of O. stamineus were addressed. The molecular docking results suggested that nearly all core compounds and their targets possess good binding affinity. In addition, the separation of receptor and ligands was not observed in all dynamics simulation processes, whereas complexes of orthosiphol Z-AR and Y-AR performed best in simulations of molecular dynamics. CONCLUSION This study successfully identified the polypharmacological mechanisms of the main compounds in O. stamineus, and predicted five seed compounds along with 10 core targets. Moreover, orthosiphol Z, orthosiphol Y, and their derivatives can be utilized as lead compounds for further research and development. The findings here provide improved guidance for subsequent experiments, and we identified potential active compounds for drug discovery or health promotion.
Collapse
Affiliation(s)
- Xingqiang Wang
- Department of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650021, PR China; Yunnan Provincial Clinical Medicine Research Center of Rheumatism in TCM, Yunnan Provincial Hospital of Traditional Chinese Medicine, Yunnan, 650021, PR China.
| | - Weiqing Zhao
- Department of Rheumatology and Immunology, The First People's Hospital of Yunnan Province and The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650034, PR China
| | - Xiaoyu Zhang
- Department of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650021, PR China
| | - Zongqing Wang
- Department of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650021, PR China
| | - Chang Han
- Department of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650021, PR China
| | - Jiapeng Xu
- Department of Yi Medicine, Traditional Chinese Medicine Hospital of Chuxiong Yi Autonomous Prefecture (Traditional Yi Medicine Hospital of Yunnan Province), Chuxiong, Yunnan, 675000, PR China
| | - Guohui Yang
- Department of Medical Research Information, Traditional Chinese Medicine Hospital of Chuxiong Yi Autonomous Prefecture (Traditional Yi Medicine Hospital of Yunnan Province), Chuxiong, Yunnan, 675000, PR China
| | - Jiangyun Peng
- Department of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650021, PR China; Yunnan Provincial Clinical Medicine Research Center of Rheumatism in TCM, Yunnan Provincial Hospital of Traditional Chinese Medicine, Yunnan, 650021, PR China.
| | - Zhaofu Li
- Department of Rheumatology, The No.1 Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Kunming, Yunnan, 650021, PR China; Yunnan Provincial Clinical Medicine Research Center of Rheumatism in TCM, Yunnan Provincial Hospital of Traditional Chinese Medicine, Yunnan, 650021, PR China.
| |
Collapse
|
34
|
Morovat P, Morovat S, Hosseinpour M, Moslabeh FGZ, Kamali MJ, Samadani AA. Survival-based bioinformatics analysis to identify hub long non-coding RNAs along with lncRNA-miRNA-mRNA network for potential diagnosis/prognosis of thyroid cancer. J Cell Commun Signal 2023; 17:639-655. [PMID: 36149574 PMCID: PMC10409689 DOI: 10.1007/s12079-022-00697-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 09/07/2022] [Indexed: 11/26/2022] Open
Abstract
Thyroid cancer (TC) is the most common endocrine cancer, accounting for 1.7% of all cancer cases. It has been reported that the existing approach to diagnosing TC is problematic. Therefore, it is essential to develop molecular biomarkers to improve the accuracy of the diagnosis. This study aimed to screen hub lncRNAs in the ceRNA network (ceRNET) connected to TC formation and progression based on the overall survival rate. In this study, first, RNA-seq data from the GDC database were collected. A package called edgeR in R programming language was then used to obtain differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) in TC patients' samples compared to normal samples. Second, DEmRNAs were analyzed for their functional enrichment. Third, to identify RNAs associated with overall survival, the overall survival of these RNAs was analyzed using the Kaplan-Meier plotter database to create a survival associated with the ceRNA network (survival-related ceRNET). Next, the GeneMANIA plugin was used to construct a PPI network to better understand survival-related DEmRNA interactions. The survival ceRNET was then visualized with the Cytoscape software, and hub genes, including hub lncRNAs and hub mRNAs, were identified using the CytoHubba plugin. We found 45 DElncRNAs, 28 DEmiRNAs, and 723 DEmRNAs among thyroid tumor tissue and non-tumor tissue samples. According to KEGG, GO and DO analyses, 723 DEmRNAs were mainly enriched in cancer-related pathways. Importantly, the results found that ten DElncRNAs, four DEmiRNAs, and 68 DEmRNAs are associated with overall survival. In this account, the PPI network was constructed for 68 survival-related DEmRNAs, and ADAMTS9, DTX4, and CLDN10 were identified as hub genes. The ceRNET was created by combining six lncRNAs, 109 miRNAs, and 22 mRNAs related to survival using Cytoscape. in this network, ten hub RNAs were identified by the CytoHubba plugin, including mRNAs (CTXND1, XKRX, IGFBP2, ENTPD1, GALNT7, ADAMTS9) and lncRNAs (AC090673.1, AL162511.1, LINC02454, AL365259.1). This study suggests that three lncRNAs, including AL162511.1, AC090673.1, and AL365259.1, could be reliable diagnostic biomarkers for TC. The findings of this study provide a basis for future studies on the therapeutic potential of these lncRNAs.
Collapse
Affiliation(s)
- Pejman Morovat
- Department of Medical Biotechnology, School of Medicine, Babol University of Medical Sciences, Babol, Iran
- Student Research Committee, Babol University of Medical Sciences, Babol, Iran
| | - Saman Morovat
- Department of Medical Genetics and Molecular Biology, Faculty of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Milad Hosseinpour
- Department of Medical Genetics and Molecular Biology, Faculty of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | | | - Mohammad Javad Kamali
- Department of Medical Genetics, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Ali Akbar Samadani
- Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran.
| |
Collapse
|
35
|
Karatzas E, Baltoumas FA, Aplakidou E, Kontou PI, Stathopoulos P, Stefanis L, Bagos PG, Pavlopoulos GA. Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources. Bioinformatics 2023; 39:btad490. [PMID: 37540207 PMCID: PMC10423032 DOI: 10.1093/bioinformatics/btad490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/31/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023] Open
Abstract
Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.
Collapse
Affiliation(s)
- Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Panagiota I Kontou
- Department of Mathematics, University of Thessaly, Lamia, 35100, Greece
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Panos Stathopoulos
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
- School of Medicine, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | - Leonidas Stefanis
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, 11527, Greece
- Hellenic Army Academy, Vari, 16673, Greece
| |
Collapse
|
36
|
Cenikj G, Eftimov T, Koroušić Seljak B. FooDis: A food-disease relation mining pipeline. Artif Intell Med 2023; 142:102586. [PMID: 37316100 DOI: 10.1016/j.artmed.2023.102586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 04/07/2023] [Accepted: 05/16/2023] [Indexed: 06/16/2023]
Abstract
Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx.
Collapse
Affiliation(s)
- Gjorgjina Cenikj
- Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia.
| | - Tome Eftimov
- Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia.
| | | |
Collapse
|
37
|
Gao Y, Wu Z, Liu S, Chen Y, Zhao G, Lin HP. Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification. Front Endocrinol (Lausanne) 2023; 14:1190012. [PMID: 37576963 PMCID: PMC10420078 DOI: 10.3389/fendo.2023.1190012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/10/2023] [Indexed: 08/15/2023] Open
Abstract
Background Preeclampsia (PE) is the primary cause of perinatal maternal-fetal mortality and morbidity. The exact molecular mechanisms of PE pathogenesis are largely unknown. This study aims to identify the hub genes in PE and explore their potential molecular regulatory network. Methods We downloaded the GSE148241, GSE190971, GSE74341, and GSE114691 datasets for the placenta and performed a differential expression analysis to identify hub genes. We performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) Analysis to determine functional roles and regulatory networks of differentially expressed genes (DEGs). We then verified the DEGs at transcriptional and translational levels by analyzing the GSE44711 and GSE177049 datasets and our clinical samples, respectively. Results We identified 60 DEGs in the discovery phase, consisting of 7 downregulated genes and 53 upregulated genes. We then identified seven hub genes using Cytoscape software. In the verification phase, 4 and 3 of the seven genes exhibited the same variation patterns at the transcriptional level in the GSE44711 and GSE177049 datasets, respectively. Validation of our clinical samples showed that CADM3 has the best discriminative performance for predicting PE. Conclusion These findings may enhance the understanding of PE and provide new insight into identifying potential therapeutic targets for PE.
Collapse
Affiliation(s)
- Yongqi Gao
- Department of Basic Medical Research, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| | - Zhongji Wu
- Department of Basic Medical Research, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| | - Simin Liu
- Department of Basic Medical Research, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| | - Yiwen Chen
- Department of Basic Medical Research, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| | - Guojun Zhao
- The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan City People’s Hospital, Qingyuan, Guangdong, China
| | - Hui-Ping Lin
- Department of Basic Medical Research, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
| |
Collapse
|
38
|
Amir A, Ozel E, Haberman Y, Shental N. Achieving pan-microbiome biological insights via the dbBact knowledge base. Nucleic Acids Res 2023; 51:6593-6608. [PMID: 37326027 PMCID: PMC10359611 DOI: 10.1093/nar/gkad527] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/26/2023] [Accepted: 06/08/2023] [Indexed: 06/17/2023] Open
Abstract
16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact - a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes.
Collapse
Affiliation(s)
- Amnon Amir
- Microbiome center, Sheba Medical Center, Israel
| | - Eitan Ozel
- Dept. of Computer Science, The Open University of Israel, Israel
| | - Yael Haberman
- Pediatric Gastroenterology, Hepatology and Nutrition Unit, Sheba Medical Center, Israel
| | - Noam Shental
- Dept. of Computer Science, The Open University of Israel, Israel
| |
Collapse
|
39
|
Seneviratne O, Das AK, Chari S, Agu NN, Rashid SM, McCusker J, Franklin JS, Qi M, Bennett KP, Chen CH, Hendler JA, McGuinness DL. Semantically enabling clinical decision support recommendations. J Biomed Semantics 2023; 14:8. [PMID: 37464259 DOI: 10.1186/s13326-023-00285-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 03/28/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding guideline recommendations into decision support systems, much of the previous work has not focused on enabling system generated recommendations through the formalization of changes in a guideline, the provenance of a recommendation, and applicability of the evidence. Prior work indicates that healthcare providers may not find that guideline-derived recommendations always meet their needs for reasons such as lack of relevance, transparency, time pressure, and applicability to their clinical practice. RESULTS We introduce several semantic techniques that model diseases based on clinical practice guidelines, provenance of the guidelines, and the study cohorts they are based on to enhance the capabilities of clinical decision support systems. We have explored ways to enable clinical decision support systems with semantic technologies that can represent and link to details in related items from the scientific literature and quickly adapt to changing information from the guidelines, identifying gaps, and supporting personalized explanations. Previous semantics-driven clinical decision systems have limited support in all these aspects, and we present the ontologies and semantic web based software tools in three distinct areas that are unified using a standard set of ontologies and a custom-built knowledge graph framework: (i) guideline modeling to characterize diseases, (ii) guideline provenance to attach evidence to treatment decisions from authoritative sources, and (iii) study cohort modeling to identify relevant research publications for complicated patients. CONCLUSIONS We have enhanced existing, evidence-based knowledge by developing ontologies and software that enables clinicians to conveniently access updates to and provenance of guidelines, as well as gather additional information from research studies applicable to their patients' unique circumstances. Our software solutions leverage many well-used existing biomedical ontologies and build upon decades of knowledge representation and reasoning work, leading to explainable results.
Collapse
Affiliation(s)
| | | | - Shruthi Chari
- Rensselaer Polytechnic Institute, 110 8th St, 12180, Troy, NY, USA
| | | | - Sabbir M Rashid
- Rensselaer Polytechnic Institute, 110 8th St, 12180, Troy, NY, USA
| | - Jamie McCusker
- Rensselaer Polytechnic Institute, 110 8th St, 12180, Troy, NY, USA
| | - Jade S Franklin
- Rensselaer Polytechnic Institute, 110 8th St, 12180, Troy, NY, USA
| | - Miao Qi
- Rensselaer Polytechnic Institute, 110 8th St, 12180, Troy, NY, USA
| | | | | | - James A Hendler
- Rensselaer Polytechnic Institute, 110 8th St, 12180, Troy, NY, USA
| | | |
Collapse
|
40
|
Wang T, Chen M, Li H, Ding G, Song Y, Hou B, Yao B, Wang Z, Hou Y, Liang J, Wei C, Jia Z. Repositioning of clinically approved drug Bazi Bushen capsule for treatment of Aizheimer's disease using network pharmacology approach and in vitro experimental validation. Heliyon 2023; 9:e17603. [PMID: 37449101 PMCID: PMC10336525 DOI: 10.1016/j.heliyon.2023.e17603] [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: 02/05/2023] [Revised: 06/17/2023] [Accepted: 06/22/2023] [Indexed: 07/18/2023] Open
Abstract
Aims To explore the new indications and key mechanism of Bazi Bushen capsule (BZBS) by network pharmacology and in vitro experiment. Methods The ingredients library of BZBS was constructed by retrieving multiple TCM databases. The potential target profiles of the components were predicted by target prediction algorithms based on different principles, and validated by using known activity data. The target spectrum of BZBS with high reliability was screened by considering the source of the targets and the node degree in compound-target (C-T) network. Subsequently, new indications for BZBS were predicted by disease ontology (DO) enrichment analysis and initially validated by GO and KEGG pathway enrichment analysis. Furthermore, the target sets of BZBS acting on AD signaling pathway were identified by intersection analysis. Based on STRING database, the PPI network of target was constructed and their node degree was calculated. Two Alzheimer's disease (AD) cell models, BV-2 and SH-SY5Y, were used to preliminarily verify the anti-AD efficacy and mechanism of BZBS in vitro. Results In total, 1499 non-repeated ingredients were obtained from 16 herbs in BZBS formula, and 1320 BZBS targets with high confidence were predicted. Disease enrichment results strongly suggested that BZBS formula has the potential to be used in the treatment of AD. GO and KEGG enrichment results provide a preliminary verification of this point. Among them, 113 functional targets of BZBS belong to AD pathway. A PPI network containing 113 functional targets and 1051 edges for the treatment of AD was constructed. In vitro experiments showed that BZBS could significantly reduce the release of TNF-α and IL-6 and the expression of COX-2 and PSEN1 in Aβ25-35-induced BV-2 cells, which may be related to the regulation of ERK1/2/NF-κB signaling pathway. BZBS reduced the apoptosis rate of Aβ25-35 induced SH-SY5Y cells, significantly increased mitochondrial membrane potential, reduced the expression of Caspase3 active fragment and PSEN1, and increased the expression of IDE. This may be related to the regulation of GSK-3β/β-catenin signaling pathway. Conclusions BZBS formula has a potential use in the treatment of AD, which is achieved through regulation of ERK1/2, NF-κB signaling pathways, and GSK-3β/β-catenin signaling pathway. Furthermore, the network pharmacology technology is a feasible drug repurposing strategy to reposition new clinical use of approved TCM and explore the mechanism of action. The study lays a foundation for the subsequent in-depth study of BZBS in the treatment of AD and provides a basis for its application in the clinical treatment of AD.
Collapse
Affiliation(s)
- Tongxing Wang
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Meng Chen
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Huixin Li
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Guoyuan Ding
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Yanfei Song
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Bin Hou
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Bing Yao
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Zhixin Wang
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Yunlong Hou
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Junqing Liang
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
| | - Cong Wei
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
- Key Disciplines of State Administration of TCM for Collateral Disease, Shijiazhuang, 050035, PR China
| | - Zhenhua Jia
- National Key Laboratory of Collateral Disease Research and Innovative Chinese Medicine, Shijiazhuang, 050035, PR China
- Hebei Yiling Pharmaceutical Research Institute, Key Laboratory of State Administration of TCM (Cardio-Cerebral Vessel Collateral Diseases), Shijiazhuang, 050035, PR China
- Key Disciplines of State Administration of TCM for Collateral Disease, Shijiazhuang, 050035, PR China
| |
Collapse
|
41
|
Han GD, Dai J, Hui HX, Zhu J. ALOX5AP suppresses osteosarcoma progression via Wnt/β-catenin/EMT pathway and associates with clinical prognosis and immune infiltration. J Orthop Surg Res 2023; 18:446. [PMID: 37344882 DOI: 10.1186/s13018-023-03919-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 06/08/2023] [Indexed: 06/23/2023] Open
Abstract
Osteosarcoma (OS) is one of the most common malignant neoplasms in children and adolescents. Immune infiltration into the microenvironment of the tumor has a positive correlation with overall survival in patients with OS. The purpose of this study was to search for potential diagnostic markers that are involved in immune cell infiltration for OS. Patients with OS who acquired metastases within 5 years (n = 34) were compared to patients who did not develop metastases within 5 years (n = 19). Differentially expressed genes (DEGs) were tested for in both patient groups. To discover possible biomarkers, the LASSO regression model and the SVM-RFE analysis were both carried out. With the assistance of CIBERSORT, the compositional patterns of the 22 different types of immune cell fraction in OS were estimated. In this research, a total of 33 DEGs were obtained: 33 genes were significantly downregulated. Moreover, we identified six critical genes, including ALOX5AP, HLA-DOA, HLA-DMA, HLA-DRB4, HCLS1 and LOC647450. ROC assays confirmed their diagnostic value with AUC > 0.7. In addition, we found that the six critical genes were associated with immune infiltration. Then, we confirmed the expression of ALOX5AP was distinctly decreased in OS specimens and cell lines. High expression of ALOX5AP predicted an advanced clinical stage and overall survival of OS patients. Functionally, we found that overexpression of ALOX5AP distinctly suppressed the proliferation, migration, invasion and EMT via modulating Wnt/β-catenin signaling. Overall, we found that ALOX5AP overexpression inhibits OS development via regulation of Wnt/β-catenin signaling pathways, suggesting ALOX5AP as a novel molecular biomarker for enhanced therapy of OS.
Collapse
Affiliation(s)
- Guo-Dong Han
- Department of Orthopedics, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China
| | - Jian Dai
- Department of Orthopedics, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China
| | - Hong-Xia Hui
- Department of Medical Oncology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China
| | - Jing Zhu
- Department of Medical Oncology, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, China.
| |
Collapse
|
42
|
Bang D, Lim S, Lee S, Kim S. Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nat Commun 2023; 14:3570. [PMID: 37322032 PMCID: PMC10272215 DOI: 10.1038/s41467-023-39301-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
Computational drug repurposing aims to identify new indications for existing drugs by utilizing high-throughput data, often in the form of biomedical knowledge graphs. However, learning on biomedical knowledge graphs can be challenging due to the dominance of genes and a small number of drug and disease entities, resulting in less effective representations. To overcome this challenge, we propose a "semantic multi-layer guilt-by-association" approach that leverages the principle of guilt-by-association - "similar genes share similar functions", at the drug-gene-disease level. Using this approach, our model DREAMwalk: Drug Repurposing through Exploring Associations using Multi-layer random walk uses our semantic information-guided random walk to generate drug and disease-populated node sequences, allowing for effective mapping of both drugs and diseases in a unified embedding space. Compared to state-of-the-art link prediction models, our approach improves drug-disease association prediction accuracy by up to 16.8%. Moreover, exploration of the embedding space reveals a well-aligned harmony between biological and semantic contexts. We demonstrate the effectiveness of our approach through repurposing case studies for breast carcinoma and Alzheimer's disease, highlighting the potential of multi-layer guilt-by-association perspective for drug repurposing on biomedical knowledge graphs.
Collapse
Affiliation(s)
- Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea
- AIGENDRUG Co., Ltd., Seoul, 08826, Republic of Korea
| | - Sangsoo Lim
- School of Artificial Intelligence Convergence, Dongguk University, Seoul, 04620, Republic of Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.
- AIGENDRUG Co., Ltd., Seoul, 08826, Republic of Korea.
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, 08826, Republic of Korea.
| |
Collapse
|
43
|
Zhao L, Zhang H, Li N, Chen J, Xu H, Wang Y, Liang Q. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula. JOURNAL OF ETHNOPHARMACOLOGY 2023; 309:116306. [PMID: 36858276 DOI: 10.1016/j.jep.2023.116306] [Citation(s) in RCA: 95] [Impact Index Per Article: 95.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/06/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Network pharmacology is a new discipline based on systems biology theory, biological system network analysis, and multi-target drug molecule design specific signal node selection. The mechanism of action of TCM formula has the characteristics of multiple targets and levels. The mechanism is similar to the integrity, systematization and comprehensiveness of network pharmacology, so network pharmacology is suitable for the study of the pharmacological mechanism of Chinese medicine compounds. AIM OF THE STUDY The paper summarizes the present application status and existing problems of network pharmacology in the field of Chinese medicine formula, and formulates the research ideas, up-to-date key technology and application method and strategy of network pharmacology. Its purpose is to provide guidance and reference for using network pharmacology to reveal the modern scientific connotation of Chinese medicine. MATERIALS AND METHODS Literatures in this review were searched in PubMed, China National Knowledge Infrastructure (CNKI), Web of Science, ScienceDirect and Google Scholar using the keywords "traditional Chinese medicine", "Chinese herb medicine" and "network pharmacology". The literature cited in this review dates from 2002 to 2022. RESULTS Using network pharmacology methods to predict the basis and mechanism of pharmacodynamic substances of traditional Chinese medicines has become a trend. CONCLUSION Network pharmacology is a promising approach to reveal the pharmacology mechanism of Chinese medicine formula.
Collapse
Affiliation(s)
- Li Zhao
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hong Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Ning Li
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Jinman Chen
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Hao Xu
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Yongjun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| | - Qianqian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China; Spine Institute, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China; Key Laboratory of Ministry of Education of Theory and Therapy of Muscles and Bones, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China.
| |
Collapse
|
44
|
Lin Y, Lai X, Huang S, Pu L, Zeng Q, Wang Z, Huang W. Identification of diagnostic hub genes related to neutrophils and infiltrating immune cell alterations in idiopathic pulmonary fibrosis. Front Immunol 2023; 14:1078055. [PMID: 37334348 PMCID: PMC10272521 DOI: 10.3389/fimmu.2023.1078055] [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: 10/24/2022] [Accepted: 05/18/2023] [Indexed: 06/20/2023] Open
Abstract
Background There is still a lack of specific indicators to diagnose idiopathic pulmonary fibrosis (IPF). And the role of immune responses in IPF is elusive. In this study, we aimed to identify hub genes for diagnosing IPF and to explore the immune microenvironment in IPF. Methods We identified differentially expressed genes (DEGs) between IPF and control lung samples using the GEO database. Combining LASSO regression and SVM-RFE machine learning algorithms, we identified hub genes. Their differential expression were further validated in bleomycin-induced pulmonary fibrosis model mice and a meta-GEO cohort consisting of five merged GEO datasets. Then, we used the hub genes to construct a diagnostic model. All GEO datasets met the inclusion criteria, and verification methods, including ROC curve analysis, calibration curve (CC) analysis, decision curve analysis (DCA) and clinical impact curve (CIC) analysis, were performed to validate the reliability of the model. Through the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm (CIBERSORT), we analyzed the correlations between infiltrating immune cells and hub genes and the changes in diverse infiltrating immune cells in IPF. Results A total of 412 DEGs were identified between IPF and healthy control samples, of which 283 were upregulated and 129 were downregulated. Through machine learning, three hub genes (ASPN, SFRP2, SLCO4A1) were screened. We confirmed their differential expression using pulmonary fibrosis model mice evaluated by qPCR, western blotting and immunofluorescence staining and analysis of the meta-GEO cohort. There was a strong correlation between the expression of the three hub genes and neutrophils. Then, we constructed a diagnostic model for diagnosing IPF. The areas under the curve were 1.000 and 0.962 for the training and validation cohorts, respectively. The analysis of other external validation cohorts, as well as the CC analysis, DCA, and CIC analysis, also demonstrated strong agreement. There was also a significant correlation between IPF and infiltrating immune cells. The frequencies of most infiltrating immune cells involved in activating adaptive immune responses were increased in IPF, and a majority of innate immune cells showed reduced frequencies. Conclusion Our study demonstrated that three hub genes (ASPN, SFRP2, SLCO4A1) were associated with neutrophils, and the model constructed with these genes showed good diagnostic value in IPF. There was a significant correlation between IPF and infiltrating immune cells, indicating the potential role of immune regulation in the pathological process of IPF.
Collapse
Affiliation(s)
- Yingying Lin
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaofan Lai
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shaojie Huang
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lvya Pu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Qihao Zeng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Zhongxing Wang
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenqi Huang
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
45
|
Cenikj G, Strojnik L, Angelski R, Ogrinc N, Koroušić Seljak B, Eftimov T. From language models to large-scale food and biomedical knowledge graphs. Sci Rep 2023; 13:7815. [PMID: 37188766 DOI: 10.1038/s41598-023-34981-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/10/2023] [Indexed: 05/17/2023] Open
Abstract
Knowledge about the interactions between dietary and biomedical factors is scattered throughout uncountable research articles in an unstructured form (e.g., text, images, etc.) and requires automatic structuring so that it can be provided to medical professionals in a suitable format. Various biomedical knowledge graphs exist, however, they require further extension with relations between food and biomedical entities. In this study, we evaluate the performance of three state-of-the-art relation-mining pipelines (FooDis, FoodChem and ChemDis) which extract relations between food, chemical and disease entities from textual data. We perform two case studies, where relations were automatically extracted by the pipelines and validated by domain experts. The results show that the pipelines can extract relations with an average precision around 70%, making new discoveries available to domain experts with reduced human effort, since the domain experts should only evaluate the results, instead of finding, and reading all new scientific papers.
Collapse
Affiliation(s)
- Gjorgjina Cenikj
- Jožef Stefan Institute, Ljubljana, 1000, Slovenia.
- Jožef Stefan International Postgraduate School, Ljubljana, 1000, Slovenia.
| | | | | | - Nives Ogrinc
- Jožef Stefan Institute, Ljubljana, 1000, Slovenia
| | | | - Tome Eftimov
- Jožef Stefan Institute, Ljubljana, 1000, Slovenia
| |
Collapse
|
46
|
Bradford YM, Van Slyke CE, Howe DG, Fashena D, Frazer K, Martin R, Paddock H, Pich C, Ramachandran S, Ruzicka L, Singer A, Taylor R, Tseng WC, Westerfield M. From multiallele fish to nonstandard environments, how ZFIN assigns phenotypes, human disease models, and gene expression annotations to genes. Genetics 2023; 224:iyad032. [PMID: 36864549 PMCID: PMC10158835 DOI: 10.1093/genetics/iyad032] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/13/2023] [Indexed: 03/04/2023] Open
Abstract
Danio rerio is a model organism used to investigate vertebrate development. Manipulation of the zebrafish genome and resultant gene products by mutation or targeted knockdown has made the zebrafish a good system for investigating gene function, providing a resource to investigate genetic contributors to phenotype and human disease. Phenotypic outcomes can be the result of gene mutation, targeted knockdown of gene products, manipulation of experimental conditions, or any combination thereof. Zebrafish have been used in various genetic and chemical screens to identify genetic and environmental contributors to phenotype and disease outcomes. The Zebrafish Information Network (ZFIN, zfin.org) is the central repository for genetic, genomic, and phenotypic data that result from research using D. rerio. Here we describe how ZFIN annotates phenotype, expression, and disease model data across various experimental designs, how we computationally determine wild-type gene expression, the phenotypic gene, and how these results allow us to propagate gene expression, phenotype, and disease model data to the correct gene, or gene related entity.
Collapse
Affiliation(s)
- Yvonne M Bradford
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Ceri E Van Slyke
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Douglas G Howe
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - David Fashena
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Ken Frazer
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Ryan Martin
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Holly Paddock
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Christian Pich
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | | | - Leyla Ruzicka
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Amy Singer
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Ryan Taylor
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Wei-Chia Tseng
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| | - Monte Westerfield
- The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA
| |
Collapse
|
47
|
Wagle MM, Kedige AR, Kabekkodu SP, Mallya S. Integrated Bioinformatics Analysis Identifies Crucial Biochemical Processes Shared between Pancreatitis and Pancreatic Ductal Adenocarcinoma. Asian Pac J Cancer Prev 2023; 24:1601-1610. [PMID: 37247279 PMCID: PMC10495878 DOI: 10.31557/apjcp.2023.24.5.1601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy associated with rapid progression and an abysmal prognosis. Previous research has shown that chronic pancreatitis can significantly increase the risk of developing PDAC. The overarching hypothesis is that some of the biological processes disrupted during the inflammatory stage tend to show significant dysregulation, even in cancer. This might explain why chronic inflammation increases the risk of carcinogenesis and uncontrolled proliferation. Here, we try to pinpoint such complex processes by comparing the expression profiles of pancreatitis and PDAC tissues. METHODS We analyzed a total of six gene expression datasets retrieved from the EMBL-EBI ArrayExpress and NCBI GEO databases, which included 306 PDAC, 68 pancreatitis and 172 normal pancreatic samples. The disrupted genes identified were used to perform downstream analysis for ontology, interaction, enriched pathways, potential druggability, promoter methylation, and the associated prognostic value. Further, we performed expression analysis based on gender, patient's drinking habit, race, and pancreatitis status. RESULTS Our study identified 45 genes with altered expression levels shared between PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption, ECM-receptor interaction, PI3k-Akt signaling, and proteoglycans in cancer pathways as significantly enriched. Module analysis identified 15 hub genes, of which 14 were found to be in the druggable genome category. CONCLUSION In summary, we have identified critical genes and various biochemical processes disrupted at a molecular level. These results can provide valuable insights into certain events leading to carcinogenesis, and therefore help identify novel therapeutic targets to improve PDAC treatment in the future.
Collapse
Affiliation(s)
- Manoj M Wagle
- School of Mathematics and Statistics and Computational Systems Biology Group, Children’s Medical Research Institute, University of Sydney, New South Wales, Australia.
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, India.
| | - Ananya Rao Kedige
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, India.
- Université Grenoble Alpes, CNRS, CEA, Grenoble, France.
| | - Shama P Kabekkodu
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, India.
| | - Sandeep Mallya
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, India.
| |
Collapse
|
48
|
Slenter DN, Hemel IMGM, Evelo CT, Bierau J, Willighagen EL, Steinbusch LKM. Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations. Orphanet J Rare Dis 2023; 18:95. [PMID: 37101200 PMCID: PMC10131334 DOI: 10.1186/s13023-023-02683-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/02/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs. METHODS Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis. RESULTS The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis. CONCLUSION The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.
Collapse
Affiliation(s)
- Denise N Slenter
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands.
| | - Irene M G M Hemel
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Chris T Evelo
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Jörgen Bierau
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Egon L Willighagen
- Department of Bioinformatics (BiGCaT), NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Laura K M Steinbusch
- Department of Clinical Genetics, Maastricht University Medical Center, Maastricht, The Netherlands
| |
Collapse
|
49
|
Liang KYH, Farjoun Y, Forgetta V, Chen Y, Yoshiji S, Lu T, Richards JB. Predicting ExWAS findings from GWAS data: a shorter path to causal genes. Hum Genet 2023; 142:749-758. [PMID: 37009933 DOI: 10.1007/s00439-023-02548-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/22/2023] [Indexed: 04/04/2023]
Abstract
GWAS has identified thousands of loci associated with disease, yet the causal genes within these loci remain largely unknown. Identifying these causal genes would enable deeper understanding of the disease and assist in genetics-based drug development. Exome-wide association studies (ExWAS) are more expensive but can pinpoint causal genes offering high-yield drug targets, yet suffer from a high false-negative rate. Several algorithms have been developed to prioritize genes at GWAS loci, such as the Effector Index (Ei), Locus-2-Gene (L2G), Polygenic Prioritization score (PoPs), and Activity-by-Contact score (ABC) and it is not known if these algorithms can predict ExWAS findings from GWAS data. However, if this were the case, thousands of associated GWAS loci could potentially be resolved to causal genes. Here, we quantified the performance of these algorithms by evaluating their ability to identify ExWAS significant genes for nine traits. We found that Ei, L2G, and PoPs can identify ExWAS significant genes with high areas under the precision recall curve (Ei: 0.52, L2G: 0.37, PoPs: 0.18, ABC: 0.14). Furthermore, we found that for every unit increase in the normalized scores, there was an associated 1.3-4.6-fold increase in the odds of a gene reaching exome-wide significance (Ei: 4.6, L2G: 2.5, PoPs: 2.1, ABC: 1.3). Overall, we found that Ei, L2G, and PoPs can anticipate ExWAS findings from widely available GWAS results. These techniques are therefore promising when well-powered ExWAS data are not readily available and can be used to anticipate ExWAS findings, allowing for prioritization of genes at GWAS loci.
Collapse
Affiliation(s)
- Kevin Y H Liang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, QC, H3A 0G4, Canada
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada
- 5 Prime Sciences Incorporated, Montréal, Canada
- Broad Institute, Cambridge, MA, 02142, USA
- Fulcrum Genomics LLC, Boulder, CO, 80302, USA
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada
- 5 Prime Sciences Incorporated, Montréal, Canada
| | - Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal, QC, H3A 0G4, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal, QC, H3A 0G4, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, QC, H3A 0G4, Canada
- 5 Prime Sciences Incorporated, Montréal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, H3T 1E2, Canada.
- Quantitative Life Sciences Program, McGill University, Montréal, QC, H3A 0G4, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, H3A 0G4, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, H3A 0G4, Canada.
- Department of Medicine, McGill University, Montréal, QC, H3A 0G4, Canada.
- Department of Twin Research, King's College London, London, UK.
- 5 Prime Sciences Incorporated, Montréal, Canada.
| |
Collapse
|
50
|
Tariq Z, Qadeer MI, Anjum I, Hano C, Anjum S. Thalassemia and Nanotheragnostics: Advanced Approaches for Diagnosis and Treatment. BIOSENSORS 2023; 13:bios13040450. [PMID: 37185525 PMCID: PMC10136341 DOI: 10.3390/bios13040450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 05/17/2023]
Abstract
Thalassemia is a monogenic autosomal recessive disorder caused by mutations, which lead to abnormal or reduced production of hemoglobin. Ineffective erythropoiesis, hemolysis, hepcidin suppression, and iron overload are common manifestations that vary according to genotypes and dictate, which diagnosis and therapeutic modalities, including transfusion therapy, iron chelation therapy, HbF induction, gene therapy, and editing, are performed. These conventional therapeutic methods have proven to be effective, yet have several disadvantages, specifically iron toxicity, associated with them; therefore, there are demands for advanced therapeutic methods. Nanotechnology-based applications, such as the use of nanoparticles and nanomedicines for theragnostic purposes have emerged that are simple, convenient, and cost-effective methods. The therapeutic potential of various nanoparticles has been explored by developing artificial hemoglobin, nano-based iron chelating agents, and nanocarriers for globin gene editing by CRISPR/Cas9. Au, Ag, carbon, graphene, silicon, porous nanoparticles, dendrimers, hydrogels, quantum dots, etc., have been used in electrochemical biosensors development for diagnosis of thalassemia, quantification of hemoglobin in these patients, and analysis of conventional iron chelating agents. This review summarizes the potential of nanotechnology in the development of various theragnostic approaches to determine thalassemia-causing gene mutations using various nano-based biosensors along with the employment of efficacious nano-based therapeutic procedures, in contrast to conventional therapies.
Collapse
Affiliation(s)
- Zahra Tariq
- Department of Biotechnology, Kinnaird College for Women, 92-Jail Road, Lahore 54000, Pakistan
| | | | - Iram Anjum
- Department of Biotechnology, Kinnaird College for Women, 92-Jail Road, Lahore 54000, Pakistan
| | - Christophe Hano
- Department of Chemical Biology, Eure & Loir Campus, University of Orleans, 28000 Chartres, France
| | - Sumaira Anjum
- Department of Biotechnology, Kinnaird College for Women, 92-Jail Road, Lahore 54000, Pakistan
| |
Collapse
|