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Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
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Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
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Li N, Liang XR, Bai X, Liang XH, Dang LH, Jin QQ, Cao J, Du QX, Sun JH. Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle. Int J Legal Med 2024; 138:197-206. [PMID: 37804331 DOI: 10.1007/s00414-023-03095-x] [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: 12/13/2022] [Accepted: 09/18/2023] [Indexed: 10/09/2023]
Abstract
Given that combination with multiple biomarkers may well raise the predictive value of wound age, it appears critically essential to identify new features under the limited cost. For this purpose, the present study explored whether the gene expression ratios provide unique time information as an additional indicator for wound age estimation not requiring the detection of new biomarkers and allowing full use of the available data. The expression levels of four wound-healing genes (Arid5a, Ier3, Stom, and Lcp1) were detected by real-time polymerase chain reaction, and a total of six expression ratios were calculated among these four genes. The results showed that the expression levels of four genes and six ratios of expression changed time-dependent during wound repair. The six expression ratios provided additional temporal information, distinct from the four genes analyzed separately by principal component analysis. The overall performance metrics for cross-validation and external validation of four typical prediction models were improved when six ratios of expression were added as additional input variables. Overall, expression ratios among genes provide temporal information and have excellent potential as predictive markers for wound age estimation. Combining the expression levels of genes with ratio-expression of genes may allow for more accurate estimates of the time of injury.
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Affiliation(s)
- Na Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Xin-Rui Liang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Xue Bai
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Xin-Hua Liang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Li-Hong Dang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Qian-Qian Jin
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Jie Cao
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China
| | - Qiu-Xiang Du
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China.
| | - Jun-Hong Sun
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030604, Shanxi, China.
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Huang X, Su B, Li M, Zhou Y, He X. Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma. Sci Rep 2023; 13:22472. [PMID: 38110715 PMCID: PMC10728109 DOI: 10.1038/s41598-023-50156-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/15/2023] [Indexed: 12/20/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.
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Affiliation(s)
- Xin Huang
- School of Artificial Intelligence, Anshan Normal University, Pingan Street, Anshan, 114007, Liaoning, China.
- Biomedical Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China.
- Postdoctoral Workstation of Dalian Yongjia Electronic Technology Co., Ltd, Dalian, Liaoning, China.
| | - Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China
| | - Mengjun Li
- School of Artificial Intelligence, Anshan Normal University, Pingan Street, Anshan, 114007, Liaoning, China
| | - Yang Zhou
- Ningbo Institute of Innovation for Combined Medicine and Engineering, Ningbo Medical Center Li Huili Hospital, Ningbo, Zhejiang, China
| | - Xinyu He
- School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China
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Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope? Digit Health 2023; 9:20552076231203879. [PMID: 37786401 PMCID: PMC10541760 DOI: 10.1177/20552076231203879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023] Open
Abstract
Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.
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Affiliation(s)
- Xiantong Zou
- Xiantong Zou, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
| | | | - Linong Ji
- Linong Ji, Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, 100044, China.
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