1
|
Li J, Zhang Z, Liu H, Qu X, Yin X, Chen L, Guo N, Wang C, Zhang Z. Effects of continuous intravenous infusion with propofol on intestinal metabolites in rats. Biomed Rep 2024; 20:25. [PMID: 38169795 PMCID: PMC10758916 DOI: 10.3892/br.2023.1713] [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: 10/10/2022] [Accepted: 05/18/2023] [Indexed: 01/05/2024] Open
Abstract
Microbial metabolites play an important role in regulating intestinal homeostasis and immune responses. Propofol is a common anesthetic in clinic, but it is not clear whether it affects intestinal metabolites in rats. Tail vein puncture was performed after adaptive feeding for 1 month in eight 2-month-old rats and they were given continuous intravenous infusion of propofol for 3 h. The feces of rats were divided into different groups based on time periods, with before and after anesthesia with propofol on days 1, 3 and 7 labeled as groups P, A1, A3 and A7, respectively. The effect of continuous intravenous infusion with propofol on rat fecal metabolites was determined using the non-targeted metabolomics technique gas chromatography coupled with a time-of-flight mass spectrometer analysis. The types and contents of metabolites in rat feces were changed after continuous intravenous infusion with propofol, but the changes were not statistically significant. The contents of the metabolites 3-hydroxyphenylacetic acid and palmitic acid increased from day 3 to 7, and it was shown that the two metabolites were positively correlated at a statistically significant level. Linoleic acid decreased to its lowest level on day 3, and it returned to pre-anesthesia level on day 7. At the same time, linoleic acid metabolism was a metabolic pathway that was co-enriched 7 days after infusion with propofol. Spearman correlation analysis showed that there was significant correlation between some differential metabolites and differential microorganisms. It was observed that zymosterol 1, cytosin and elaidic acid were negatively correlated with Alloprevotella in the A3 vs. P group. In the A7 vs. P group, cortexolone 3 and coprostan-3-one were positively correlated with Faecalibacterium, whilst aconitic acid was negatively correlated with it. In conclusion, the present study revealed statistically insignificant effects of continuous intravenous propofol on the intestinal metabolites in rats.
Collapse
Affiliation(s)
- Jiaying Li
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Zhongjie Zhang
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Hongyu Liu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Xutong Qu
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Xueqing Yin
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Lu Chen
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Nana Guo
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Changsong Wang
- Department of Critical Care Medicine, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| | - Zhaodi Zhang
- Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, P.R. China
| |
Collapse
|
2
|
Liu Y, Liu JE, He H, Qin M, Lei H, Meng J, Liu C, Chen X, Luo W, Zhong S. Characterizing the metabolic divide: distinctive metabolites differentiating CAD-T2DM from CAD patients. Cardiovasc Diabetol 2024; 23:14. [PMID: 38184583 PMCID: PMC10771670 DOI: 10.1186/s12933-023-02102-0] [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: 11/09/2023] [Accepted: 12/25/2023] [Indexed: 01/08/2024] Open
Abstract
OBJECTIVE To delineate the metabolomic differences in plasma samples between patients with coronary artery disease (CAD) and those with concomitant CAD and type 2 diabetes mellitus (T2DM), and to pinpoint distinctive metabolites indicative of T2DM risk. METHOD Plasma samples from CAD and CAD-T2DM patients across three centers underwent comprehensive metabolomic and lipidomic analyses. Multivariate logistic regression was employed to discern the relationship between the identified metabolites and T2DM risk. Characteristic metabolites' metabolic impacts were further probed through hepatocyte cellular experiments. Subsequent transcriptomic analyses elucidated the potential target sites explaining the metabolic actions of these metabolites. RESULTS Metabolomic analysis revealed 192 and 95 significantly altered profiles in the discovery (FDR < 0.05) and validation (P < 0.05) cohorts, respectively, that were associated with T2DM risk in univariate logistic regression. Further multivariate regression analyses identified 22 characteristic metabolites consistently associated with T2DM risk in both cohorts. Notably, pipecolinic acid and L-pipecolic acid, lysine derivatives, exhibited negative association with CAD-T2DM and influenced cellular glucose metabolism in hepatocytes. Transcriptomic insights shed light on potential metabolic action sites of these metabolites. CONCLUSIONS This research underscores the metabolic disparities between CAD and CAD-T2DM patients, spotlighting the protective attributes of pipecolinic acid and L-pipecolic acid. The comprehensive metabolomic and transcriptomic findings provide novel insights into the mechanism research, prophylaxis and treatment of comorbidity of CAD and T2DM.
Collapse
Affiliation(s)
- Yingjian Liu
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Ju-E Liu
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Huafeng He
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Min Qin
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Heping Lei
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jinxiu Meng
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Chen Liu
- Department of Cardiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China
| | - Wenwei Luo
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
| | - Shilong Zhong
- School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
| |
Collapse
|
3
|
Sun X, Jia Z, Zhang Y, Zhao X, Zhao C, Lu X, Xu G. A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry. Metabolites 2023; 13:metabo13030460. [PMID: 36984900 PMCID: PMC10057860 DOI: 10.3390/metabo13030460] [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: 03/04/2023] [Revised: 03/18/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.
Collapse
Affiliation(s)
- Xiaoshan Sun
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Zhen Jia
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- Department of Cell Biology, College of Life Sciences, China Medical University, Shenyang 110122, China
| | - Yuqing Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- Zhang Dayu School of Chemistry, Dalian University of Technology, Dalian 116024, China
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Chunxia Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| |
Collapse
|
4
|
Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
Collapse
Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
| |
Collapse
|
5
|
Lin Y, Yuan Y, Ouyang Y, Wang H, Xiao Y, Zhao X, Yang H, Li X, Guo H, He M, Zhang X, Xu G, Qiu G, Wu T. Metabolome-Wide Association Study of Multiple Plasma Metals with Serum Metabolomic Profile among Middle-to-Older-Aged Chinese Adults. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:16001-16011. [PMID: 36269707 PMCID: PMC9671050 DOI: 10.1021/acs.est.2c05547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/04/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and metabolic responses may reveal novel clues to the underlying mechanisms. Based on a metabolome-wide association study of 17 plasma metals with untargeted metabolomic profiling of 189 serum metabolites among 1992 participants within the Dongfeng-Tongji cohort, we replicated two metal-associated pathways, linoleic acid metabolism and aminoacyl-tRNA biosynthesis, with novel metal associations (false discovery rate, FDR < 0.05), and we also identified two novel pathways, including biosynthesis of unsaturated fatty acids and alpha-linolenic acid metabolism, as associated with metal exposure (FDR < 0.05). Moreover, two-way orthogonal partial least-squares analysis showed that five metabolites, including aspartylphenylalanine, free fatty acid 14:1, uridine, carnitine C14:2, and LPC 18:2, contributed most to the joint covariation between the two data matrices (12.3%, 8.3%, 8.0%, 7.4%, and 7.3%, respectively). Further BKMR analysis showed significant positive joint associations of plasma Al, As, Ba, and Zn with aspartylphenylalanine and of plasma Ba, Co, Mn, and Pb with carnitine C14:2, when all the metals were at the 55th percentiles or above, compared with the median. We also found significant interactions between As and Ba in the association with aspartylphenylalanine (P for interaction = 0.048) and between Ba and Pb in the association with carnitine C14:2 (P for interaction < 0.001). Together, these findings may provide new insights into the mechanisms underlying the adverse health effects induced by metal exposure.
Collapse
Affiliation(s)
- Yuhui Lin
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yu Yuan
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yang Ouyang
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Wang
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yang Xiao
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xinjie Zhao
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Handong Yang
- Department
of Cardiovascular Disease, Dongfeng Central
Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Xiulou Li
- Department
of Cardiovascular Disease, Dongfeng Central
Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Huan Guo
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Meian He
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xiaomin Zhang
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guowang Xu
- CAS
Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy
of Sciences, Dalian 116023, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Gaokun Qiu
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Tangchun Wu
- Ministry
of Education and State Key Laboratory of Environmental Health (Incubating),
School of Public Health, Tongji Medical
College, Huazhong University of Science and Technology, Wuhan 430030, China
| |
Collapse
|
6
|
Qiu G, Wang H, Yan Q, Ma H, Niu R, Lei Y, Xiao Y, Zhou L, Yang H, Xu C, Zhang X, He M, Tang H, Hu Z, Pan A, Shen H, Wu T. A Lipid Signature with Perturbed Triacylglycerol Co-Regulation, Identified from Targeted Lipidomics, Predicts Risk for Type 2 Diabetes and Mediates the Risk from Adiposity in Two Prospective Cohorts of Chinese Adults. Clin Chem 2022; 68:1094-1107. [PMID: 35708664 DOI: 10.1093/clinchem/hvac090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/18/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The roles of individual and co-regulated lipid molecular species in the development of type 2 diabetes (T2D) and mediation from metabolic risk factors remain unknown. METHODS We conducted profiling of 166 plasma lipid species in 2 nested case-control studies within 2 independent cohorts of Chinese adults, the Dongfeng-Tongji and the Jiangsu non-communicable disease cohorts. After 4.61 (0.15) and 7.57 (1.13) years' follow-up, 1039 and 520 eligible participants developed T2D in these 2 cohorts, respectively, and controls were 1:1 matched to cases by age and sex. RESULTS We found 27 lipid species, including 10 novel ones, consistently associated with T2D risk in the 2 cohorts. Differential correlation network analysis revealed significant correlations of triacylglycerol (TAG) 50:3, containing at least one oleyl chain, with 6 TAGs, at least 3 of which contain the palmitoyl chain, all downregulated within cases relative to controls among the 27 lipids in both cohorts, while the networks also both identified the oleyl chain-containing TAG 50:3 as the central hub. We further found that 13 of the 27 lipids consistently mediated the association between adiposity indicators (body mass index, waist circumference, and waist-to-height ratio) and diabetes risk in both cohorts (all P < 0.05; proportion mediated: 20.00%, 17.70%, and 17.71%, and 32.50%, 28.73%, and 33.86%, respectively). CONCLUSIONS Our findings suggested notable perturbed co-regulation, inferred from differential correlation networks, between oleyl chain- and palmitoyl chain-containing TAGs before diabetes onset, with the oleyl chain-containing TAG 50:3 at the center, and provided novel etiological insight regarding lipid dysregulation in the progression from adiposity to overt T2D.
Collapse
Affiliation(s)
- Gaokun Qiu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hao Wang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Qi Yan
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hongxia Ma
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Rundong Niu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yanshou Lei
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yang Xiao
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Lue Zhou
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Handong Yang
- Department of Cardiovascular Disease, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan 442008, China
| | - Chengwei Xu
- Department of Cardiovascular Disease, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan 442008, China
| | - Xiaomin Zhang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Meian He
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200433, China.,CAS Key Laboratory of Magnetic Resonance in Biological Systems, University of Chinese Academy of Sciences, Wuhan 430071, China
| | - Zhibin Hu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - An Pan
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Tangchun Wu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| |
Collapse
|
7
|
Jung T, Jung Y, Moon MK, Kwon O, Hwang GS, Park T. Integrative Pathway Analysis of SNP and Metabolite Data Using a Hierarchical Structural Component Model. Front Genet 2022; 13:814412. [PMID: 35401680 PMCID: PMC8987531 DOI: 10.3389/fgene.2022.814412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 01/13/2022] [Indexed: 11/16/2022] Open
Abstract
Integrative multi-omics analysis has become a useful tool to understand molecular mechanisms and drug discovery for treatment. Especially, the couplings of genetics to metabolomics have been performed to identify the associations between SNP and metabolite. However, while the importance of integrative pathway analysis is increasing, there are few approaches to utilize pathway information to analyze phenotypes using SNP and metabolite. We propose an integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model considering the structural relationships of SNPs, metabolites, pathways, and phenotypes. The proposed method utilizes genome-wide association studies on metabolites and constructs the genetic risk scores for metabolites referred to as genetic metabolomic scores. It is based on the hierarchical model using the genetic metabolomic scores and pathways. Furthermore, this method adopts a ridge penalty to consider the correlations between genetic metabolomic scores and between pathways. We apply our method to the SNP and metabolite data from the Korean population to identify pathways associated with type 2 diabetes (T2D). Through this application, we identified well-known pathways associated with T2D, demonstrating that this method adds biological insights into disease-related pathways using genetic predispositions of metabolites.
Collapse
Affiliation(s)
- Taeyeong Jung
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Youngae Jung
- Korea Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, South Korea
| | - Min Kyong Moon
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Oran Kwon
- Department of Nutritional Science and Food Management, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea
| | - Geum-Sook Hwang
- Korea Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, South Korea
- *Correspondence: Geum-Sook Hwang, ; Taesung Park,
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
- Department of Statistics, Seoul National University, Seoul, South Korea
- *Correspondence: Geum-Sook Hwang, ; Taesung Park,
| |
Collapse
|
8
|
The WWOX/HIF1A Axis Downregulation Alters Glucose Metabolism and Predispose to Metabolic Disorders. Int J Mol Sci 2022; 23:ijms23063326. [PMID: 35328751 PMCID: PMC8955937 DOI: 10.3390/ijms23063326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/10/2022] [Accepted: 03/17/2022] [Indexed: 02/01/2023] Open
Abstract
Recent reports indicate that the hypoxia-induced factor (HIF1α) and the Warburg effect play an initiating role in glucotoxicity, which underlies disorders in metabolic diseases. WWOX has been identified as a HIF1α regulator. WWOX downregulation leads to an increased expression of HIF1α target genes encoding glucose transporters and glycolysis’ enzymes. It has been proven in the normoglycemic mice cells and in gestational diabetes patients. The aim of the study was to determine WWOX’s role in glucose metabolism regulation in hyperglycemia and hypoxia to confirm its importance in the development of metabolic disorders. For this purpose, the WWOX gene was silenced in human normal fibroblasts, and then cells were cultured under different sugar and oxygen levels. Thereafter, it was investigated how WWOX silencing alters the genes and proteins expression profile of glucose transporters and glycolysis pathway enzymes, and their activity. In normoxia normoglycemia, higher glycolysis genes expression, their activity, and the lactate concentration were observed in WWOX KO fibroblasts in comparison to control cells. In normoxia hyperglycemia, it was observed a decrease of insulin-dependent glucose uptake and a further increase of lactate. It likely intensifies hyperglycemia condition, which deepen the glucose toxic effect. Then, in hypoxia hyperglycemia, WWOX KO caused weaker glucose uptake and elevated lactate production. In conclusion, the WWOX/HIF1A axis downregulation alters glucose metabolism and probably predispose to metabolic disorders.
Collapse
|
9
|
Jin Q, Ma RCW. Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies. Cells 2021; 10:cells10112832. [PMID: 34831057 PMCID: PMC8616415 DOI: 10.3390/cells10112832] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/11/2021] [Accepted: 10/13/2021] [Indexed: 12/18/2022] Open
Abstract
The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.
Collapse
Affiliation(s)
- Qiao Jin
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;
| | - Ronald Ching Wan Ma
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
- Correspondence: ; Fax: +852-26373852
| |
Collapse
|