{"title":"慢性丙型肝炎进展的代谢指纹:代谢组变化和尖端诊断选择。","authors":"Amar Deep, Suchit Swaroop, Durgesh Dubey, Atul Rawat, Ajay Verma, Bikash Baisya, Rashmi Parihar, Amit Goel, Sumit Rungta","doi":"10.1002/jmr.3066","DOIUrl":null,"url":null,"abstract":"<p>Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine—were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and <i>p</i> < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.</p>","PeriodicalId":16531,"journal":{"name":"Journal of Molecular Recognition","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options\",\"authors\":\"Amar Deep, Suchit Swaroop, Durgesh Dubey, Atul Rawat, Ajay Verma, Bikash Baisya, Rashmi Parihar, Amit Goel, Sumit Rungta\",\"doi\":\"10.1002/jmr.3066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine—were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and <i>p</i> < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.</p>\",\"PeriodicalId\":16531,\"journal\":{\"name\":\"Journal of Molecular Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Molecular Recognition\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jmr.3066\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Recognition","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jmr.3066","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options
Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine—were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.
期刊介绍:
Journal of Molecular Recognition (JMR) publishes original research papers and reviews describing substantial advances in our understanding of molecular recognition phenomena in life sciences, covering all aspects from biochemistry, molecular biology, medicine, and biophysics. The research may employ experimental, theoretical and/or computational approaches.
The focus of the journal is on recognition phenomena involving biomolecules and their biological / biochemical partners rather than on the recognition of metal ions or inorganic compounds. Molecular recognition involves non-covalent specific interactions between two or more biological molecules, molecular aggregates, cellular modules or organelles, as exemplified by receptor-ligand, antigen-antibody, nucleic acid-protein, sugar-lectin, to mention just a few of the possible interactions. The journal invites manuscripts that aim to achieve a complete description of molecular recognition mechanisms between well-characterized biomolecules in terms of structure, dynamics and biological activity. Such studies may help the future development of new drugs and vaccines, although the experimental testing of new drugs and vaccines falls outside the scope of the journal. Manuscripts that describe the application of standard approaches and techniques to design or model new molecular entities or to describe interactions between biomolecules, but do not provide new insights into molecular recognition processes will not be considered. Similarly, manuscripts involving biomolecules uncharacterized at the sequence level (e.g. calf thymus DNA) will not be considered.