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An G, Zhao C, Chen X, Wang W, Bi Y. Casual relationships between circulating metabolites and rheumatoid arthritis: A mendelian randomization study. Heliyon 2024; 10:e33085. [PMID: 38988517 PMCID: PMC11234099 DOI: 10.1016/j.heliyon.2024.e33085] [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: 08/31/2023] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
Background Blood metabolites serve as pivotal indicators in identifying and predicting the course of rheumatoid arthritis (RA). However, empirical substantiation of a direct causal link between these serum biomarkers and the development of RA is still lacking comprehensive support. Method In pursuit of a thorough exploration of the causal links between circulating blood metabolites and RA, we deployed a two-sample Mendelian randomization (MR) approach during our initial investigative phase. This method was utilized to examine the potential connections between 249 distinct circulating metabolites and the prevalence of RA. In the validation phase, we conducted replication analyses with a new metabolic dataset consisting of 123 metabolites. Furthermore, we employed the Mendelian randomization based on Bayesian model averaging (MR-BMA) technique to pinpoint key metabolic characteristics that have significant causal implications. Results In our primary analysis, we found that acetate, acetoacetate and pyruvate exhibited a consistent protective causal association with rheumatoid arthritis, while lactate demonstrated a positive correlation with rheumatoid arthritis risk. It is also noteworthy that a substantial subset of traits related to both saturated and unsaturated fatty acids showed causal influences. Subsequent secondary analyses substantiated these observations, revealing that traits associated with the average number of methylene groups in a fatty acid chain exhibited protective effects. Ultimately, our MR-BMA analyses unveiled that the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids assumes a paramount role in increasing the susceptibility to rheumatoid arthritis. Conclusions By employing systemic MR analyses, our study has successfully generated an all-encompassing atlas elucidating the intricate connections between circulating metabolites and the susceptibility to rheumatoid arthritis. Our results indicate the high unsaturation degree is a dominant risk factors correlated with rheumatoid arthritis.
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Affiliation(s)
- Gaole An
- Information Department, Bethune International Peace Hospital, Shijiazhuang, 050082, Hebei Province, China
| | - Chenghui Zhao
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Research Center for Biomedical Engineering, Medical Innovation & Research Division, Chinese PLA General Hospital, Beijing, China
| | - Xiaoye Chen
- Information Department, Bethune International Peace Hospital, Shijiazhuang, 050082, Hebei Province, China
| | - Weidong Wang
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Research Center for Biomedical Engineering, Medical Innovation & Research Division, Chinese PLA General Hospital, Beijing, China
| | - Yuwang Bi
- Information Department, Bethune International Peace Hospital, Shijiazhuang, 050082, Hebei Province, China
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2
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Sharma SD, Bluett J. Towards Personalized Medicine in Rheumatoid Arthritis. Open Access Rheumatol 2024; 16:89-114. [PMID: 38779469 PMCID: PMC11110814 DOI: 10.2147/oarrr.s372610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.
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Affiliation(s)
- Seema D Sharma
- Centre for Musculoskeletal Research, Division of Musculoskeletal & Dermatological Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - James Bluett
- Centre for Musculoskeletal Research, Division of Musculoskeletal & Dermatological Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
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3
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Ryan MJ, Raby E, Whiley L, Masuda R, Lodge S, Nitschke P, Maker GL, Wist J, Holmes E, Wood FM, Nicholson JK, Fear MW, Gray N. Nonsevere Burn Induces a Prolonged Systemic Metabolic Phenotype Indicative of a Persistent Inflammatory Response Postinjury. J Proteome Res 2023. [PMID: 38104259 DOI: 10.1021/acs.jproteome.3c00516] [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: 12/19/2023]
Abstract
Globally, burns are a significant cause of injury that can cause substantial acute trauma as well as lead to increased incidence of chronic comorbidity and disease. To date, research has primarily focused on the systemic response to severe injury, with little in the literature reported on the impact of nonsevere injuries (<15% total burn surface area; TBSA). To elucidate the metabolic consequences of a nonsevere burn injury, longitudinal plasma was collected from adults (n = 35) who presented at hospital with a nonsevere burn injury at admission, and at 6 week follow up. A cross-sectional baseline sample was also collected from nonburn control participants (n = 14). Samples underwent multiplatform metabolic phenotyping using 1H nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry to quantify 112 lipoprotein and glycoprotein signatures and 852 lipid species from across 20 subclasses. Multivariate data modeling (orthogonal projections to latent structures-discriminate analysis; OPLS-DA) revealed alterations in lipoprotein and lipid metabolism when comparing the baseline control to hospital admission samples, with the phenotypic signature found to be sustained at follow up. Univariate (Mann-Whitney U) testing and OPLS-DA indicated specific increases in GlycB (p-value < 1.0e-4), low density lipoprotein-2 subfractions (variable importance in projection score; VIP > 6.83e-1) and monoacyglyceride (20:4) (p-value < 1.0e-4) and decreases in circulating anti-inflammatory high-density lipoprotein-4 subfractions (VIP > 7.75e-1), phosphatidylcholines, phosphatidylglycerols, phosphatidylinositols, and phosphatidylserines. The results indicate a persistent systemic metabolic phenotype that occurs even in cases of a nonsevere burn injury. The phenotype is indicative of an acute inflammatory profile that continues to be sustained postinjury, suggesting an impact on systems health beyond the site of injury. The phenotypes contained metabolic signatures consistent with chronic inflammatory states reported to have an elevated incidence postburn injury. Such phenotypic signatures may provide patient stratification opportunities, to identify individual responses to injury, personalize intervention strategies, and improve acute care, reducing the risk of chronic comorbidity.
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Affiliation(s)
- Monique J Ryan
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Edward Raby
- Burns Service of Western Australia, WA Department of Health, Murdoch, Western Australia 6150, Australia
- Department of Microbiology, PathWest Laboratory Medicine, Perth, Western Australia 6009, Australia
- Department of Infectious Diseases, Fiona Stanley Hospital, Perth, Western Australia 6150, Australia
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Reika Masuda
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Samantha Lodge
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Philipp Nitschke
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Garth L Maker
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
| | - Julien Wist
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Chemistry Department, Universidad del Valle, Cali 76001, Colombia
| | - Elaine Holmes
- Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Department of Metabolism Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, United Kingdom
| | - Fiona M Wood
- Burns Service of Western Australia, WA Department of Health, Murdoch, Western Australia 6150, Australia
- Burn Injury Research Unit, School of Biomedical Sciences, University of Western Australia, Perth, Western Australia 6009, Australia
- Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, United Kingdom
| | - Mark W Fear
- Burn Injury Research Unit, School of Biomedical Sciences, University of Western Australia, Perth, Western Australia 6009, Australia
- Fiona Wood Foundation, Perth, Western Australia 6150, Australia
| | - Nicola Gray
- Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, Western Australia 6150, Australia
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4
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Xu L, Chang C, Jiang P, Wei K, Zhang R, Jin Y, Zhao J, Xu L, Shi Y, Guo S, He D. Metabolomics in rheumatoid arthritis: Advances and review. Front Immunol 2022; 13:961708. [PMID: 36032122 PMCID: PMC9404373 DOI: 10.3389/fimmu.2022.961708] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 07/25/2022] [Indexed: 12/11/2022] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.
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Affiliation(s)
- Lingxia Xu
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Runrun Zhang
- Department of Rheumatology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yehua Jin
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Linshuai Xu
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yiming Shi
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Shicheng Guo
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, United States
- *Correspondence: Shicheng Guo, ; Dongyi He,
| | - Dongyi He
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Shicheng Guo, ; Dongyi He,
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5
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Bhat GR, Sethi I, Rah B, Kumar R, Afroze D. Innovative in Silico Approaches for Characterization of Genes and Proteins. Front Genet 2022; 13:865182. [PMID: 35664302 PMCID: PMC9159363 DOI: 10.3389/fgene.2022.865182] [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: 01/29/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.
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Affiliation(s)
- Gh. Rasool Bhat
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
| | - Itty Sethi
- Institute of Human Genetics, University of Jammu, Jammu, India
| | - Bilal Rah
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
| | - Rakesh Kumar
- School of Biotechnology, Shri Mata Vaishno Devi University, Katra, India
| | - Dil Afroze
- Advanced Centre for Human Genetics, Sher-I- Kashmir Institute of Medical Sciences, Soura, India
- *Correspondence: Dil Afroze,
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6
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Chen X, Ye J, Lei H, Wang C. Novel Potential Diagnostic Serum Biomarkers of Metabolomics in Osteoarticular Tuberculosis Patients: A Preliminary Study. Front Cell Infect Microbiol 2022; 12:827528. [PMID: 35402287 PMCID: PMC8992656 DOI: 10.3389/fcimb.2022.827528] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/21/2022] [Indexed: 11/17/2022] Open
Abstract
Osteoarticular tuberculosis is one of the extrapulmonary tuberculosis, which is mainly caused by direct infection of Mycobacterium tuberculosis or secondary infection of tuberculosis in other parts. Due to the low specificity of the current detection method, it is leading to a high misdiagnosis rate and subsequently affecting the follow-up treatment and prognosis. Metabolomics is mainly used to study the changes of the body’s metabolites in different states, so it can serve as an important means in the discovery of disease-related metabolic biomarkers and the corresponding mechanism research. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to detect and analyze metabolites in the serum with osteoarticular tuberculosis patients, disease controls, and healthy controls to find novel metabolic biomarkers that could be used in the diagnosis of osteoarticular tuberculosis. Our results showed that 68 differential metabolites (p<0.05, fold change>1.0) were obtained in osteoarticular tuberculosis serum after statistical analysis. Then, through the evaluation of diagnostic efficacy, PC[o-16:1(9Z)/18:0], PC[20:4(8Z,11Z,14Z,17Z)/18:0], PC[18:0/22:5(4Z,7Z,10Z,13Z,16Z)], SM(d18:1/20:0), and SM[d18:1/18:1(11Z)] were found as potential biomarkers with high diagnostic efficacy. Using bioinformatics analysis, we further found that these metabolites share many lipid metabolic signaling pathways, such as choline metabolism, sphingolipid signaling, retrograde endocannabinoid signaling, and sphingolipid and glycerophospholipid metabolism; these results suggest that lipid metabolism plays an important role in the pathological process of tuberculosis. This study can provide certain reference value for the study of metabolic biomarkers of osteoarticular tuberculosis and the mechanism of lipid metabolism in osteoarticular tuberculosis and even other tuberculosis diseases.
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Affiliation(s)
- Ximeng Chen
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Department of Clinical Laboratory Medicine, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Jingyun Ye
- Department of Clinical Laboratory Medicine, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Hong Lei
- Department of Clinical Laboratory Medicine, The Eighth Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Chengbin Wang, ; Hong Lei,
| | - Chengbin Wang
- Department of Clinical Laboratory Medicine, The First Medical Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Chengbin Wang, ; Hong Lei,
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7
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Nitschke P, Lodge S, Kimhofer T, Masuda R, Bong SH, Hall D, Schäfer H, Spraul M, Pompe N, Diercks T, Bernardo-Seisdedos G, Mato JM, Millet O, Susic D, Henry A, El-Omar EM, Holmes E, Lindon JC, Nicholson JK, Wist J. J-Edited DIffusional Proton Nuclear Magnetic Resonance Spectroscopic Measurement of Glycoprotein and Supramolecular Phospholipid Biomarkers of Inflammation in Human Serum. Anal Chem 2022; 94:1333-1341. [DOI: 10.1021/acs.analchem.1c04576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Philipp Nitschke
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
| | - Samantha Lodge
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
| | - Torben Kimhofer
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
| | - Reika Masuda
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
| | - Sze-How Bong
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
| | - Drew Hall
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
| | - Hartmut Schäfer
- Bruker Biospin GmbH, Silberstreifen, 76287, Rheinstetten 76287, Germany
| | - Manfred Spraul
- Bruker Biospin GmbH, Silberstreifen, 76287, Rheinstetten 76287, Germany
| | - Niels Pompe
- Bruker Biospin GmbH, Silberstreifen, 76287, Rheinstetten 76287, Germany
| | - Tammo Diercks
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160, Derio 48160, Spain
| | - Ganeko Bernardo-Seisdedos
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160, Derio 48160, Spain
| | - José M. Mato
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160, Derio 48160, Spain
| | - Oscar Millet
- Precision Medicine and Metabolism Laboratory, CIC bioGUNE, Parque Tecnológico de Bizkaia, Bld. 800, 48160, Derio 48160, Spain
| | - Daniella Susic
- School of Women’s and Children’s Health, University of New South Wales, Sydney, New South Wales 2052, Australia
- UNSW Microbiome Research Centre, St George Hospital, Kogarah, New South Wales 2217, Australia
| | - Amanda Henry
- School of Women’s and Children’s Health, University of New South Wales, Sydney, New South Wales 2052, Australia
- UNSW Microbiome Research Centre, St George Hospital, Kogarah, New South Wales 2217, Australia
| | - Emad M El-Omar
- Microbiome Research Centre, St George & Sutherland Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Elaine Holmes
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, U.K
| | - John C. Lindon
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, U.K
| | - Jeremy K. Nicholson
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
- Institute of Global Health Innovation Faculty of Medicine, Imperial College London, Level 1, Faculty Building, South Kensington Campus, London SW7 2NA, U.K
| | - Julien Wist
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, Western Australia 6150, Australia
- Chemistry Department, Universidad del Valle, Cali 76001, Colombia
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8
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Du X, Yang L, Kong L, Sun Y, Shen K, Cai Y, Sun H, Zhang B, Guo S, Zhang A, Wang X. Metabolomics of various samples advancing biomarker discovery and pathogenesis elucidation for diabetic retinopathy. Front Endocrinol (Lausanne) 2022; 13:1037164. [PMID: 36387907 PMCID: PMC9646596 DOI: 10.3389/fendo.2022.1037164] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/10/2022] [Indexed: 11/28/2022] Open
Abstract
Diabetic retinopathy (DR) is a universal microvascular complication of diabetes mellitus (DM), which is the main reason for global sight damage/loss in middle-aged and/or older people. Current clinical analyses, like hemoglobin A1c, possess some importance as prognostic indicators for DR severity, but no effective circulating biomarkers are used for DR in the clinic currently, and studies on the latent pathophysiology remain lacking. Recent developments in omics, especially metabolomics, continue to disclose novel potential biomarkers in several fields, including but not limited to DR. Therefore, based on the overview of metabolomics, we reviewed progress in analytical technology of metabolomics, the prominent roles and the current status of biomarkers in DR, and the update of potential biomarkers in various DR-related samples via metabolomics, including tear as well as vitreous humor, aqueous humor, retina, plasma, serum, cerebrospinal fluid, urine, and feces. In this review, we underscored the in-depth analysis and elucidation of the common biomarkers in different biological samples based on integrated results, namely, alanine, lactate, and glutamine. Alanine may participate in and regulate glucose metabolism through stimulating N-methyl-D-aspartate receptors and subsequently suppressing insulin secretion, which is the potential pathogenesis of DR. Abnormal lactate could cause extensive oxidative stress and neuroinflammation, eventually leading to retinal hypoxia and metabolic dysfunction; on the other hand, high-level lactate may damage the structure and function of the retinal endothelial cell barrier via the G protein-coupled receptor 81. Abnormal glutamine indicates a disturbance of glutamate recycling, which may affect the activation of Müller cells and proliferation via the PPP1CA-YAP-GS-Gln-mTORC1 pathway.
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Affiliation(s)
- Xiaohui Du
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Le Yang
- State Key Laboratory of Dampness Syndrome, the Second Affiliated Hospital Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ling Kong
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ye Sun
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
- State Key Laboratory of Dampness Syndrome, the Second Affiliated Hospital Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Kunshuang Shen
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ying Cai
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Hui Sun
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
- *Correspondence: Hui Sun, ; Xijun Wang,
| | - Bo Zhang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Sifan Guo
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Aihua Zhang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xijun Wang
- National Chinmedomics Research Center, National TCM Key Laboratory of Serum Pharmacochemistry, Metabolomics Laboratory, Department of Pharmaceutical Analysis, Heilongjiang University of Chinese Medicine, Harbin, China
- State Key Laboratory of Dampness Syndrome, the Second Affiliated Hospital Guangzhou University of Chinese Medicine, Guangzhou, China
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, Macau SAR, China
- *Correspondence: Hui Sun, ; Xijun Wang,
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Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis. Metabolites 2021; 11:metabo11120812. [PMID: 34940570 PMCID: PMC8708401 DOI: 10.3390/metabo11120812] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 12/18/2022] Open
Abstract
The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.
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Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases. Metabolites 2021; 11:metabo11070418. [PMID: 34201929 PMCID: PMC8305588 DOI: 10.3390/metabo11070418] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/29/2022] Open
Abstract
Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.
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