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Matsuyama K, Yamada S, Sato H, Zhan J, Shoda T. Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses. J Gastroenterol 2024:10.1007/s00535-024-02151-6. [PMID: 39297956 DOI: 10.1007/s00535-024-02151-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 09/07/2024] [Indexed: 09/21/2024]
Abstract
Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.
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Affiliation(s)
- Kazuhiro Matsuyama
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7028, Cincinnati, OH, 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, USA
| | - Shingo Yamada
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7028, Cincinnati, OH, 45229, USA
| | - Hironori Sato
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7028, Cincinnati, OH, 45229, USA
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Justin Zhan
- Department of Computer Science, University of Cincinnati, Cincinnati, USA
| | - Tetsuo Shoda
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC 7028, Cincinnati, OH, 45229, USA.
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Kamble P, Nagar PR, Bhakhar KA, Garg P, Sobhia ME, Naidu S, Bharatam PV. Cancer pharmacoinformatics: Databases and analytical tools. Funct Integr Genomics 2024; 24:166. [PMID: 39294509 DOI: 10.1007/s10142-024-01445-5] [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: 07/29/2024] [Revised: 08/26/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024]
Abstract
Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.
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Affiliation(s)
- Pradnya Kamble
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prinsa R Nagar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Kaushikkumar A Bhakhar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - M Elizabeth Sobhia
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Srivatsava Naidu
- Center of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Prasad V Bharatam
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
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Gong X, Su L, Huang J, Liu J, Wang Q, Luo X, Yang G, Chi H. An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery. Front Immunol 2024; 15:1381272. [PMID: 39139555 PMCID: PMC11319186 DOI: 10.3389/fimmu.2024.1381272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 07/12/2024] [Indexed: 08/15/2024] Open
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.
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Affiliation(s)
- Xiangjin Gong
- Department of Sports Rehabilitation, Southwest Medical University, Luzhou, China
| | - Lanqian Su
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jinbang Huang
- Clinical Medical College, Southwest Medical University, Luzhou, China
| | - Jie Liu
- Department of Geriatric, Dazhou Central Hospital, Dazhou, China
| | - Qinglai Wang
- Orthopedics and Traumatology Department of TCM, Wenzhou TCM Hospital of Zhejiang Chinese Medical University, Wenzhou, China
| | - Xiufang Luo
- Department of Geriatric, Dazhou Central Hospital, Dazhou, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, China
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Yan H, Ou Q, Chang Y, Liu J, Chen L, Guo D, Zhang S. 5-Fluorouracil resistance-based immune-related gene signature for COAD prognosis. Heliyon 2024; 10:e34535. [PMID: 39130472 PMCID: PMC11315090 DOI: 10.1016/j.heliyon.2024.e34535] [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: 02/21/2024] [Revised: 06/29/2024] [Accepted: 07/11/2024] [Indexed: 08/13/2024] Open
Abstract
Background Drug resistance is the primary obstacle to advanced tumor therapy and the key risk factor for tumor recurrence and death. 5-Fluorouracil (5-FU) chemotherapy is the most common chemotherapy for individuals with colorectal cancer, despite numerous options. Methods The Gene Expression Omnibus database was utilized to extract expression profile data of HCT-8 human colorectal cancer wild-type cells and their 5-FU-induced drug resistance cell line. These data were used to identify 5-FU resistance-related differentially expressed genes (5FRRDEGs), which intersected with the colorectal adenocarcinoma (COAD) transcriptome data provided by the Cancer Genome Atlas Program database. A prognostic signature containing five 5FRRDEGs (GOLGA8A, KLC3, TIGD1, NBPF1, and SERPINE1) was established after conducting a Cox regression analysis. We conducted nomogram development, drug sensitivity analysis, tumor immune microenvironment analysis, and mutation analysis to assess the therapeutic value of the prognostic qualities. Results We identified 166 5FRRDEGs in patients with COAD. Subsequently, we created a prognostic model consisting of five 5FRRDEGs using Cox regression analysis. The patients with COAD were divided into different risk groups by risk score; the high-risk group demonstrated a worse prognosis than the low-risk group. Conclusion In summary, the 5FRRDEG-based prognostic model is an effective tool for targeted therapy and chemotherapy in patients with COAD. It can accurately predict the survival prognosis of these patients as well as to provide the direction for exploring the resistance mechanism underlying COAD.
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Affiliation(s)
- Haixia Yan
- Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Qinling Ou
- Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Yonglong Chang
- Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Jinhui Liu
- College of Integrated Traditional Chinese & Western Medicine, Hunan University of Traditional Chinese Medicine, Changsha, Hunan, 410208, China
| | - Linzi Chen
- Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
| | - Duanyang Guo
- College of Integrated Traditional Chinese & Western Medicine, Hunan University of Traditional Chinese Medicine, Changsha, Hunan, 410208, China
| | - Sifang Zhang
- Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China
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Artesani A, Bruno A, Gelardi F, Chiti A. Empowering PET: harnessing deep learning for improved clinical insight. Eur Radiol Exp 2024; 8:17. [PMID: 38321340 PMCID: PMC10847083 DOI: 10.1186/s41747-023-00413-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 02/08/2024] Open
Abstract
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.
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Affiliation(s)
- Alessia Artesani
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20090, Italy
| | - Alessandro Bruno
- Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", IULM Libera Università Di Lingue E Comunicazione, Via P. Filargo 38, Milan, 20143, Italy
| | - Fabrizia Gelardi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Milan, Pieve Emanuele, 20090, Italy.
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.
| | - Arturo Chiti
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
- Department of Nuclear Medicine, IRCCS Ospedale San Raffaele, Via Olgettina 60, Milan, 20132, Italy
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Wang L, Sun T, Liu X, Wang Y, Qiao X, Chen N, Liu F, Zhou X, Wang H, Shen H. Myocarditis: A multi-omics approach. Clin Chim Acta 2024; 554:117752. [PMID: 38184138 DOI: 10.1016/j.cca.2023.117752] [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: 12/14/2023] [Revised: 12/29/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
Myocarditis, an inflammatory condition of weakened heart muscles often triggered by a variety of causes, that can result in heart failure and sudden death. Novel ways to enhance our understanding of myocarditis pathogenesis is available through newer modalities (omics). In this review, we examine the roles of various biomolecules and associated functional pathways across genomics, transcriptomics, proteomics, and metabolomics in the pathogenesis of myocarditis. Our analysis further explores the reproducibility and variability intrinsic to omics studies, underscoring the necessity and significance of employing a multi-omics approach to gain profound insights into myocarditis pathogenesis. This integrated strategy not only enhances our understanding of the disease, but also confirms the critical importance of a holistic multi-omics approach in disease analysis.
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Affiliation(s)
- Lulu Wang
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Tao Sun
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, Jiangsu, China
| | - Xiaolan Liu
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Yan Wang
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Xiaorong Qiao
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Nuo Chen
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Fangqian Liu
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Xiaoxiang Zhou
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Hua Wang
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Hongxing Shen
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
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Li M, Lu Y, Gao Z, Yue D, Hong J, Wu J, Xi D, Deng W, Chong Y. Pan-Omics in Sheep: Unveiling Genetic Landscapes. Animals (Basel) 2024; 14:273. [PMID: 38254442 PMCID: PMC10812798 DOI: 10.3390/ani14020273] [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: 11/28/2023] [Revised: 01/04/2024] [Accepted: 01/14/2024] [Indexed: 01/24/2024] Open
Abstract
Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.
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Affiliation(s)
- Mengfei Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Ying Lu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Zhendong Gao
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Dan Yue
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
- Faculty of Animal Science and Technology, Yuxi Agricultural Vocational and Technical College, Yuxi 653106, China
| | - Jieyun Hong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Jiao Wu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Dongmei Xi
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Weidong Deng
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
| | - Yuqing Chong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China; (M.L.); (Y.L.); (Z.G.); (D.Y.); (J.H.); (J.W.); (D.X.); (W.D.)
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Tsoungos A, Pemaj V, Slavko A, Kapolos J, Papadelli M, Papadimitriou K. The Rising Role of Omics and Meta-Omics in Table Olive Research. Foods 2023; 12:3783. [PMID: 37893676 PMCID: PMC10606081 DOI: 10.3390/foods12203783] [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: 09/07/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Table olives are often the result of fermentation, a process where microorganisms transform raw materials into the final product. The microbial community can significantly impact the organoleptic characteristics and safety of table olives, and it is influenced by various factors, including the processing methods. Traditional culture-dependent techniques capture only a fraction of table olives' intricate microbiota, prompting a shift toward culture-independent methods to address this knowledge gap. This review explores recent advances in table olive research through omics and meta-omics approaches. Genomic analysis of microorganisms isolated from table olives has revealed multiple genes linked to technological and probiotic attributes. An increasing number of studies concern metagenomics and metabolomics analyses of table olives. The former offers comprehensive insights into microbial diversity and function, while the latter identifies aroma and flavor determinants. Although proteomics and transcriptomics studies remain limited in the field, they have the potential to reveal deeper layers of table olives' microbiome composition and functionality. Despite the challenges associated with implementing multi-omics approaches, such as the reliance on advanced bioinformatics tools and computational resources, they hold the promise of groundbreaking advances in table olive processing technology.
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Affiliation(s)
- Anastasios Tsoungos
- Department of Food Science and Technology, University of the Peloponnese, 24100 Kalamata, Greece; (A.T.); (V.P.); (A.S.); (J.K.); (M.P.)
| | - Violeta Pemaj
- Department of Food Science and Technology, University of the Peloponnese, 24100 Kalamata, Greece; (A.T.); (V.P.); (A.S.); (J.K.); (M.P.)
| | - Aleksandra Slavko
- Department of Food Science and Technology, University of the Peloponnese, 24100 Kalamata, Greece; (A.T.); (V.P.); (A.S.); (J.K.); (M.P.)
| | - John Kapolos
- Department of Food Science and Technology, University of the Peloponnese, 24100 Kalamata, Greece; (A.T.); (V.P.); (A.S.); (J.K.); (M.P.)
| | - Marina Papadelli
- Department of Food Science and Technology, University of the Peloponnese, 24100 Kalamata, Greece; (A.T.); (V.P.); (A.S.); (J.K.); (M.P.)
| | - Konstantinos Papadimitriou
- Laboratory of Food Quality Control and Hygiene, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
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Applications of Genomics and Transcriptomics in Precision Medicine for Myopia Control or Prevention. Biomolecules 2023; 13:biom13030494. [PMID: 36979429 PMCID: PMC10046175 DOI: 10.3390/biom13030494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/18/2023] [Accepted: 03/02/2023] [Indexed: 03/12/2023] Open
Abstract
Myopia is a globally emerging concern accompanied by multiple medical and socio-economic burdens with no well-established causal treatment to control thus far. The study of the genomics and transcriptomics of myopia treatment is crucial to delineate disease pathways and provide valuable insights for the design of precise and effective therapeutics. A strong understanding of altered biochemical pathways and underlying pathogenesis leading to myopia may facilitate early diagnosis and treatment of myopia, ultimately leading to the development of more effective preventive and therapeutic measures. In this review, we summarize current data about the genomics and transcriptomics of myopia in human and animal models. We also discuss the potential applicability of these findings to precision medicine for myopia treatment.
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Using Artificial Intelligence to Better Predict and Develop Biomarkers. Clin Lab Med 2023; 43:99-114. [PMID: 36764811 DOI: 10.1016/j.cll.2022.09.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.
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11
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Li J, Li Z, Wang Y, Lin H, Wu B. TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion. Front Genet 2023; 14:1181391. [PMID: 37205123 PMCID: PMC10185877 DOI: 10.3389/fgene.2023.1181391] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/11/2023] [Indexed: 05/21/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA-lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA-lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.
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Affiliation(s)
- Jianwei Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
- *Correspondence: Jianwei Li,
| | - Zhiguang Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Yinfei Wang
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Hongxin Lin
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Baoqin Wu
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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12
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Pang XM, Peng ZY, Zheng X, Shi JJ, Zhou BC. Analysis of research hotspots in COVID-19 genomics based on citespace software: Bibliometric analysis. Front Cell Infect Microbiol 2022; 12:1060031. [PMID: 36579345 PMCID: PMC9791043 DOI: 10.3389/fcimb.2022.1060031] [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: 10/07/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
Introduction To analyze the current state, hotspots, and cutting-edge trends of genomics research on the outbreak of Corona Virus Disease 2019 (COVID-19) from 2019 to the present (March 2022). Methods Statistical and visual analysis of COVID-19 genomics results published in the 2019-2022 Web of Science Core Collection Database (WOSCC) was performed using CiteSpace software, including data on countries, institutions, authors, journals, co-citations, keywords, etc. Results A total of 9133 English literature were included. The number of publications has significantly increased in 2021, and it is expected that this upward trend will last into the future. The research hotspots of COVID-19 revolve around quarantine, biological management, angiotensin-converting enzyme-2, RNA-dependent RNA polymerase, etc. Research frontiers and trends focus on molecular docking, messenger RNA, functional receptor, etc. Conclusion The last two years have seen a significant increase in research interest in the field of novel coronavirus pneumonia genomics.
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Affiliation(s)
- Xue meng Pang
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Zhao yun Peng
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China,The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China,*Correspondence: Zhao yun Peng, ; Xin Zheng,
| | - Xin Zheng
- Qingdao Hospital of Traditional Chinese Medicine (Qingdao Hiser Hospital), Qingdao, Shandong, China,*Correspondence: Zhao yun Peng, ; Xin Zheng,
| | - Jing jing Shi
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Bao chen Zhou
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
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Athanasopoulou K, Daneva GN, Boti MA, Dimitroulis G, Adamopoulos PG, Scorilas A. The Transition from Cancer "omics" to "epi-omics" through Next- and Third-Generation Sequencing. LIFE (BASEL, SWITZERLAND) 2022; 12:life12122010. [PMID: 36556377 PMCID: PMC9785810 DOI: 10.3390/life12122010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/05/2022]
Abstract
Deciphering cancer etiopathogenesis has proven to be an especially challenging task since the mechanisms that drive tumor development and progression are far from simple. An astonishing amount of research has revealed a wide spectrum of defects, including genomic abnormalities, epigenomic alterations, disturbance of gene transcription, as well as post-translational protein modifications, which cooperatively promote carcinogenesis. These findings suggest that the adoption of a multidimensional approach can provide a much more precise and comprehensive picture of the tumor landscape, hence serving as a powerful tool in cancer research and precision oncology. The introduction of next- and third-generation sequencing technologies paved the way for the decoding of genetic information and the elucidation of cancer-related cellular compounds and mechanisms. In the present review, we discuss the current and emerging applications of both generations of sequencing technologies, also referred to as massive parallel sequencing (MPS), in the fields of cancer genomics, transcriptomics and proteomics, as well as in the progressing realms of epi-omics. Finally, we provide a brief insight into the expanding scope of sequencing applications in personalized cancer medicine and pharmacogenomics.
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Nie QQ, Zheng ZQ, Liao J, Li YC, Chen YT, Wang TY, Yuan GQ, Wang Z, Xue Q. SPP1/AnxA1/TIMP1 as Essential Genes Regulate the Inflammatory Response in the Acute Phase of Cerebral Ischemia-Reperfusion in Rats. J Inflamm Res 2022; 15:4873-4890. [PMID: 36046663 PMCID: PMC9420928 DOI: 10.2147/jir.s369690] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 08/16/2022] [Indexed: 11/27/2022] Open
Abstract
Background Ischemic injury in stroke is followed by extensive neurovascular inflammation and changes in ischemic penumbra gene expression patterns. However, the key molecules involved in the inflammatory response during the acute phase of ischemic stroke remain unclear. Methods Gene expression profiles of two rat ischemic stroke-related data sets, GSE61616 and GSE97537, were downloaded from the GEO database for Gene Set Enrichment Analysis (GSEA). Then, GEO2R was used to screen differentially expressed genes (DEGs). Furthermore, 170 differentially expressed intersection genes were screened and analyzed for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Candidate genes and miRNAs were obtained by DAVID, Metascape, Cytoscape, STRING, and TargetScan. Finally, the rat middle cerebral artery occlusion-reperfusion (MCAO/R) model was constructed, and qRT-PCR was used to verify the predicted potential miRNA molecule and its target genes. Results GO and KEGG analyses showed that 170 genes were highly associated with inflammatory cell activation and cytokine production. After cluster analysis, seven hub genes highly correlated with post-stroke neuroinflammation were obtained: Cxcl1, Kng1, Il6, AnxA1, TIMP1, SPP1, and Ccl6. The results of TargetScan further suggested that miR-340-5p may negatively regulate SPP1, AnxA1, and TIMP1 simultaneously. In the ischemic penumbra of rats 24 h after MCAO/R, the level of miR-340-5p significantly decreased compared with the control group, while the concentration of SPP1, AnxA1, and TIMP1 increased. Time-course studies demonstrated that the mRNA expression levels of SPP1, AnxA1, and TIMP1 fluctuated dramatically throughout the acute phase of cerebral ischemia-reperfusion (I/R). Conclusion Our study suggests that differentially expressed genes SPP1, TIMP1, and ANXA1 may play a vital role in the inflammatory response during the acute phase of cerebral ischemia-reperfusion injury. These genes may be negatively regulated by miR-340-5p. Our results may provide new insights into the complex pathophysiological mechanisms of secondary inflammation after stroke.
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Affiliation(s)
- Qian-Qian Nie
- Department of Neurology & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Zong-Qing Zheng
- Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Juan Liao
- Department of Neurology & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Yu-Chao Li
- Department of Nuclear Medicine, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, People's Republic of China
| | - Yan-Ting Chen
- Department of Neurology & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Tian-Ye Wang
- Department of Neurology & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Gui-Qiang Yuan
- Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Changshu Second People's Hospital, Suzhou, People's Republic of China
| | - Zhong Wang
- Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Qun Xue
- Department of Neurology & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
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Bergasa NV. Research in the pruritus of cholestasis: Genetics, behavioral studies, and physiomimetic interorgan models. Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Michelhaugh SA, Januzzi JL. Using Artificial Intelligence to Better Predict and Develop Biomarkers. Heart Fail Clin 2022; 18:275-285. [PMID: 35341540 DOI: 10.1016/j.hfc.2021.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.
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17
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Chen F, Wang J, Wu Y, Gao Q, Zhang S. Potential Biomarkers for Liver Cancer Diagnosis Based on Multi-Omics Strategy. Front Oncol 2022; 12:822449. [PMID: 35186756 PMCID: PMC8851237 DOI: 10.3389/fonc.2022.822449] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/17/2022] [Indexed: 12/11/2022] Open
Abstract
Liver cancer is the fourth leading cause of cancer-related death worldwide. Hepatocellular carcinoma (HCC) accounts for about 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients are eligible for curative therapy mainly due to the lack of early-detection strategies, highlighting the significance of reliable and accurate biomarkers. The integration of multi-omics became an important tool for biomarker screening and unique alterations in tumor-associated genes, transcripts, proteins, post-translational modifications and metabolites have been observed. We here summarized the novel biomarkers for HCC diagnosis based on multi-omics technology as well as the clinical significance of these potential biomarkers in the early detection of HCC.
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Affiliation(s)
- Fanghua Chen
- Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
| | - Junming Wang
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
| | - Yingcheng Wu
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
| | - Qiang Gao
- Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
| | - Shu Zhang
- Center for Tumor Diagnosis & Therapy, Jinshan Hospital, Fudan University, Shanghai, China
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
- *Correspondence: Shu Zhang,
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Identifying Obstructive Sleep Apnea Syndrome-Associated Genes and Pathways through Weighted Gene Coexpression Network Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3993509. [PMID: 35132330 PMCID: PMC8817882 DOI: 10.1155/2022/3993509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/05/2022] [Indexed: 11/17/2022]
Abstract
Background Obstructive sleep apnea syndrome (OSAS) is the most common type of sleep apnea disorder. The disease seriously affects the patient's respiratory system. At present, the prognosis of the disease is poor and there is a lack of effective treatments. Therefore, it is urgent to explore its pathogenesis and treatment methods. Method We downloaded a set of expression profile data from GSE75097 related to OSAS based on the Gene Expression Omnibus (GEO) database and selected the representative differentially expressed genes (DEGs) from the sample of the GSE75097 dataset. WGCNA was used to find genes related to OSAS and obtain coexpression modules. The Gene Ontology (GO) function and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were used to analyze genes from key modules. Finally, Cytoscape software was used to construct a protein-protein interaction (PPI) network and analyze the hub genes. Result We obtained a total of 7565 DEGs. Through WGCNA, we got four coexpression modules and the modules most related to OSAS were green-yellow, magenta, purple, and turquoise, and we screened out eight hub genes (DDX46, RNF115, COPA, FBXO4, PA2G4, NHP2L1, CDC20, and PCNA). GO and KEGG analyses indicated that the key modules were mainly enriched in tRNA modification, nucleobase metabolic process, DNA ligation, regulation of cellular component movement, basal transcription factors, Huntington disease, and vitamin digestion and absorption. Conclusion These pathways and hub genes can facilitate understanding the molecular mechanism of OSAS and provide a meaningful reference for finding biological targets of OSAS treatment.
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Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol 2022; 66:102111. [PMID: 34999476 DOI: 10.1016/j.cbpa.2021.102111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging biomarkers that are associated with both genomic features and clinical outcomes. Deep learning is an advanced computer science technique that has been applied in many fields, including medical image and genomic data analysis. This review summarizes the current state of deep learning in pan-cancer radiogenomic research, discusses its limitations, and indicates the potential future directions. Traditional machine learning in radiomics, genomics, and radiogenomics have also been briefly discussed. We also summarize the main pan-cancer radiogenomic research resources. Two characteristics of deep learning are emphasized when discussing its application to pan-cancer radiogenomics, which are extendibility and explainability.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
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Yu X, Yan H, Li W. Recent advances in neuropeptide-related omics and gene editing: Spotlight on NPY and somatostatin and their roles in growth and food intake of fish. Front Endocrinol (Lausanne) 2022; 13:1023842. [PMID: 36267563 PMCID: PMC9576932 DOI: 10.3389/fendo.2022.1023842] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Feeding and growth are two closely related and important physiological processes in living organisms. Studies in mammals have provided us with a series of characterizations of neuropeptides and their receptors as well as their roles in appetite control and growth. The central nervous system, especially the hypothalamus, plays an important role in the regulation of appetite. Based on their role in the regulation of feeding, neuropeptides can be classified as orexigenic peptide and anorexigenic peptide. To date, the regulation mechanism of neuropeptide on feeding and growth has been explored mainly from mammalian models, however, as a lower and diverse vertebrate, little is known in fish regarding the knowledge of regulatory roles of neuropeptides and their receptors. In recent years, the development of omics and gene editing technology has accelerated the speed and depth of research on neuropeptides and their receptors. These powerful techniques and tools allow a more precise and comprehensive perspective to explore the functional mechanisms of neuropeptides. This paper reviews the recent advance of omics and gene editing technologies in neuropeptides and receptors and their progresses in the regulation of feeding and growth of fish. The purpose of this review is to contribute to a comparative understanding of the functional mechanisms of neuropeptides in non-mammalians, especially fish.
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21
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Padilla-Martinez F, Wojciechowska G, Szczerbinski L, Kretowski A. Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes. Int J Mol Sci 2021; 23:ijms23010295. [PMID: 35008723 PMCID: PMC8745431 DOI: 10.3390/ijms23010295] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/25/2021] [Accepted: 12/26/2021] [Indexed: 11/23/2022] Open
Abstract
Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.
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Affiliation(s)
- Felipe Padilla-Martinez
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
| | - Gladys Wojciechowska
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
- Correspondence:
| | - Lukasz Szczerbinski
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15276 Białystok, Poland
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15276 Białystok, Poland
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22
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Senevirathna JDM, Asakawa S. Multi-Omics Approaches and Radiation on Lipid Metabolism in Toothed Whales. Life (Basel) 2021; 11:364. [PMID: 33923876 PMCID: PMC8074237 DOI: 10.3390/life11040364] [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: 03/13/2021] [Revised: 04/09/2021] [Accepted: 04/17/2021] [Indexed: 11/25/2022] Open
Abstract
Lipid synthesis pathways of toothed whales have evolved since their movement from the terrestrial to marine environment. The synthesis and function of these endogenous lipids and affecting factors are still little understood. In this review, we focused on different omics approaches and techniques to investigate lipid metabolism and radiation impacts on lipids in toothed whales. The selected literature was screened, and capacities, possibilities, and future approaches for identifying unusual lipid synthesis pathways by omics were evaluated. Omics approaches were categorized into the four major disciplines: lipidomics, transcriptomics, genomics, and proteomics. Genomics and transcriptomics can together identify genes related to unique lipid synthesis. As lipids interact with proteins in the animal body, lipidomics, and proteomics can correlate by creating lipid-binding proteome maps to elucidate metabolism pathways. In lipidomics studies, recent mass spectroscopic methods can address lipid profiles; however, the determination of structures of lipids are challenging. As an environmental stress, the acoustic radiation has a significant effect on the alteration of lipid profiles. Radiation studies in different omics approaches revealed the necessity of multi-omics applications. This review concluded that a combination of many of the omics areas may elucidate the metabolism of lipids and possible hazards on lipids in toothed whales by radiation.
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Affiliation(s)
- Jayan D. M. Senevirathna
- Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan;
- Department of Animal Science, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka
| | - Shuichi Asakawa
- Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan;
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Zhang SQ, Pan SM, Liang SX, Han YS, Chen HB, Li JC. Research status and prospects of biomarkers for nasopharyngeal carcinoma in the era of high‑throughput omics (Review). Int J Oncol 2021; 58:9. [PMID: 33649830 PMCID: PMC7910009 DOI: 10.3892/ijo.2021.5188] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput '‑omics' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.
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Affiliation(s)
- Shan-Qiang Zhang
- Medical Research Center, Yue Bei People's Hospital, Shantou University Medical College, Wujiang, Shaoguan, Guangdong 512025, P.R. China
| | - Su-Ming Pan
- Department of Radiotherapy, Yue Bei People's Hospital, Shantou University Medical College, Wujiang, Shaoguan, Guangdong 512025, P.R. China
| | - Si-Xian Liang
- Department of Radiotherapy, Yue Bei People's Hospital, Shantou University Medical College, Wujiang, Shaoguan, Guangdong 512025, P.R. China
| | - Yu-Shuai Han
- Institute of Cell Biology, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, P.R. China
| | - Hai-Bin Chen
- Department of Histology and Embryology, Shantou University Medical College, Shantou, Guangdong 515041, P.R. China
| | - Ji-Cheng Li
- Medical Research Center, Yue Bei People's Hospital, Shantou University Medical College, Wujiang, Shaoguan, Guangdong 512025, P.R. China
- Institute of Cell Biology, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, P.R. China
- Correspondence to: Professor Ji-Cheng Li, Medical Research Center, Yue Bei People's Hospital, Shantou University Medical College, 133 Huimin South Road, Wujiang, Shaoguan, Guangdong 512025, P.R. China, E-mail:
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