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Ahmadi S, Sedaghat FR, Memar MY, Yekani M. Metabolomics in the Diagnosis of Bacterial Infections. Clin Chim Acta 2025; 565:120020. [PMID: 39489271 DOI: 10.1016/j.cca.2024.120020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Revised: 10/25/2024] [Accepted: 10/25/2024] [Indexed: 11/05/2024]
Abstract
One of the essential factors in the appropriate treatment of infections is accurate and timely laboratory diagnosis. The correct diagnosis of infections plays a vital role in determining desirable therapy and controlling the spread of pathogens. Traditional methods of infection diagnosis are limited by several factors such as insufficient sensitivity and specificity, being time-consuming and laborious, having a low ability to distinguish infection from non-infectious inflammatory conditions and a low potential to predict treatment outcomes. Therefore, it is necessary to find innovative strategies for detecting specific biomarkers in order to diagnose infections. The rapid advancement of metabolomics makes it possible to determine the pattern of metabolite changes in the both of pathogen and the host during an infection. Metabolomics is a method used to assess the levels and type of metabolites in an organism. Metabolites are of low-molecular-weight compounds produced as a result of metabolic processes and pathways within cells. Metabolomics provides valuable data to detect accurate biomarkers of specific biochemical features directly related to certain phenotypes or conditions. This study aimed to review the applications and progress of metabolomics as a biomarker for the diagnosis of bacterial infections.
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Affiliation(s)
- Somayeh Ahmadi
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Bacteriology and Virology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Farzaneh Rafie Sedaghat
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Bacteriology and Virology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad Yousef Memar
- Infectious and Tropical Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Mina Yekani
- Department of Microbiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran.
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Zheng H, Wang C, Yu X, Zheng W, An Y, Zhang J, Zhang Y, Wang G, Qi M, Lin H, Wang F. The Role of Metabolomics and Microbiology in Urinary Tract Infection. Int J Mol Sci 2024; 25:3134. [PMID: 38542107 PMCID: PMC10969911 DOI: 10.3390/ijms25063134] [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: 01/11/2024] [Revised: 02/26/2024] [Accepted: 03/05/2024] [Indexed: 08/25/2024] Open
Abstract
One of the common illnesses that affect women's physical and mental health is urinary tract infection (UTI). The disappointing results of empirical anti-infective treatment and the lengthy time required for urine bacterial culture are two issues. Antibiotic misuse is common, especially in females who experience recurrent UTI (rUTI). This leads to a higher prevalence of antibiotic resistance in the microorganisms that cause the infection. Antibiotic therapy will face major challenges in the future, prompting clinicians to update their practices. New testing techniques are making the potential association between the urogenital microbiota and UTIs increasingly apparent. Monitoring changes in female urinary tract (UT) microbiota, as well as metabolites, may be useful in exploring newer preventive treatments for UTIs. This review focuses on advances in urogenital microbiology and organismal metabolites relevant to the identification and handling of UTIs in an attempt to provide novel methods for the identification and management of infections of the UT. Particular attention is paid to the microbiota and metabolites in the patient's urine in relation to their role in supporting host health.
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Affiliation(s)
- Haoyu Zheng
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Chao Wang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Xiao Yu
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Wenxue Zheng
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Yiming An
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Jiaqi Zhang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Yuhan Zhang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Guoqiang Wang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Mingran Qi
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Hongqiang Lin
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
| | - Fang Wang
- Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun 130021, China; (H.Z.); (C.W.); (X.Y.); (W.Z.); (Y.A.); (J.Z.); (Y.Z.); (G.W.); (M.Q.); (H.L.)
- Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun 130021, China
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3
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Loo RL, Lodge S, Kimhofer T, Bong SH, Begum S, Whiley L, Gray N, Lindon JC, Nitschke P, Lawler NG, Schäfer H, Spraul M, Richards T, Nicholson JK, Holmes E. Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples. J Proteome Res 2020; 19:4428-4441. [PMID: 32852212 PMCID: PMC7640974 DOI: 10.1021/acs.jproteome.0c00537] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Indexed: 12/14/2022]
Abstract
Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.
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Affiliation(s)
- Ruey Leng Loo
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
| | - Samantha Lodge
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
| | - Torben Kimhofer
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
| | - Sze-How Bong
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
| | - Sofina Begum
- Section
for Nutrition Research, Imperial College
London, Sir Alexander Fleming Building, South Kensington, London SW72AZ, U.K.
| | - Luke Whiley
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
- Perron
Institute for Neurological and Translational Science, Nedlands, WA 6009, Australia
| | - Nicola Gray
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
| | - John C. Lindon
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
- Department
of Metabolism, Nutrition and Reproduction, Imperial College London, Sir Alexander Fleming Building, London SW72AZ, U.K.
| | - Philipp Nitschke
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
| | - Nathan G. Lawler
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
| | | | - Manfred Spraul
- Biospin
GmbH, Silberstreifen, 76287 Rheinstetten, Germany
| | - Toby Richards
- Division
of Surgery, Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Harry Perkins Building, Robert Warren Drive, Murdoch, Perth, WA 6150, Australia
- Department
of Endocrinology and Diabetes, Fiona Stanley
Hospital, Harry Perkins
Building, Murdoch, Perth, WA 6150, Australia
| | - Jeremy K. Nicholson
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
- Division
of Surgery, Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Harry Perkins Building, Robert Warren Drive, Murdoch, Perth, WA 6150, Australia
- Institute
of Global Health Innovation, Imperial College
London, Level 1, Faculty Building, South Kensington Campus, London SW72NA, U.K.
| | - Elaine Holmes
- Australian
National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia
- Center
for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Murdoch, Perth, WA 6150, Australia
- Section
for Nutrition Research, Imperial College
London, Sir Alexander Fleming Building, South Kensington, London SW72AZ, U.K.
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Serkova NJ, Davis DM, Steiner J, Agarwal R. Quantitative NMR-Based Metabolomics on Tissue Biomarkers and Its Translation into In Vivo Magnetic Resonance Spectroscopy. Methods Mol Biol 2019; 1978:369-387. [PMID: 31119675 DOI: 10.1007/978-1-4939-9236-2_23] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an established analytical platform for analyzing metabolic profiles of cells, tissues, and body fluids. There are several advantages in introducing an NMR-based study design into metabolomics studies, including a fast and comprehensive detection, characterization, and quantification of dozens of endogenous metabolites in a single NMR spectrum. Quantitative proton 1H-NMR is the most useful NMR-based platform for metabolomics. The frozen tissues can be analyzed noninvasively using a high-resolution magic angle spinning (HR-MAS) 1H-NMR spectroscopy; or several extraction techniques can be applied to detect additional metabolites using a conventional liquid-based NMR technique. In this chapter, we report on tissue collection, handling, extraction methods, and 1H-NMR acquisition protocols developed in the past decades for a precise and quantitative NMR-metabolomics approach. The NMR acquisition protocols (both HR-MAS and conventional 1H-NMR spectroscopy) and spectral analysis steps are also presented. Since NMR can be applied "in vivo" using horizontal bore MRI scanners, several in vivo sequences for localized 1H-MRS (magnetic resonance spectroscopy) are presented which can be directly applied for noninvasive detection of brain metabolites.
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Affiliation(s)
- Natalie J Serkova
- Department of Radiology, School of Medicine, University of Colorado Denver, Aurora, CO, USA.
| | - Denise M Davis
- Department of Radiology, School of Medicine, University of Colorado Denver, Aurora, CO, USA
| | - Jenna Steiner
- Department of Radiology, School of Medicine, University of Colorado Denver, Aurora, CO, USA
| | - Rajesh Agarwal
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Denver, Aurora, CO, USA
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Han P, Huang Y, Xie Y, Yang W, Wang Y, Xiang W, Hylands PJ, Legido-Quigley C. Metabolic phenotyping in the mouse model of urinary tract infection shows that 3-hydroxybutyrate in plasma is associated with infection. PLoS One 2017; 12:e0186497. [PMID: 29036204 PMCID: PMC5643114 DOI: 10.1371/journal.pone.0186497] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 10/03/2017] [Indexed: 02/08/2023] Open
Abstract
Urinary tract infection is one of the most common bacterial infections worldwide. Current diagnosis of urinary tract infection chiefly relies on its clinical presentation, urine dipstick tests and urine culture. Small molecules found in bio-fluids related with both infection and recovery would facilitate diagnosis and management of UTI. Mass spectrometry-based fingerprinting of plasma and urine at 3 time points, pre-infection (t = -24h), infection (t = 24h) and post 3-day treatment (t = 112h), were acquired in the following four groups: mice which were healthy, infected but not treated, infected and treated with ciprofloxacin, and infected and treated with Relinqing® granules (n = 6 per group). A metabolomics workflow including multivariate analysis and ROC regression was employed to select metabolic features that correlated with UTI and its treatment. Circa 4,000 molecular features were acquired for each sample. The small acid 3-hydroxybutyrate in plasma was found to be differentiated for urinary tract infection, with an area under the curve = 0.97 (95% confidence interval: 0.93–1.00, accuracy = 0.91, sensitivity = 0.92 and specificity = 0.91). The level of 3-hydroxybutyrate in plasma was depleted after infection with a fold change of -22 (q < 0.0001). Correlation between plasma 3-hydroxybutyrate and urine bacterial number in all groups and time points was r = -0.753 (p < 0.0001). The findings show that 3-hydroxybutyrate is depleted in blood and strongly associated with UTI at both infection and post-treatment stage in a UTI mouse model. Further work is envisaged to assess the clinical potential of blood tests to assist with UTI management.
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Affiliation(s)
- Pei Han
- Institute of Pharmaceutical Science, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
| | - Yong Huang
- Provincial Key Laboratory of Pharmaceutics in Guizhou Province, School of Pharmacy, Guiyang Medical University, Guiyang, Guizhou, China
| | - Yumin Xie
- Provincial Key Laboratory of Pharmaceutics in Guizhou Province, School of Pharmacy, Guiyang Medical University, Guiyang, Guizhou, China
| | - Wu Yang
- Provincial Key Laboratory of Pharmaceutics in Guizhou Province, School of Pharmacy, Guiyang Medical University, Guiyang, Guizhou, China
| | - Yaoyao Wang
- Institute of Pharmaceutical Science, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
| | - Wenying Xiang
- Provincial Key Laboratory of Pharmaceutics in Guizhou Province, School of Pharmacy, Guiyang Medical University, Guiyang, Guizhou, China
| | - Peter J. Hylands
- Institute of Pharmaceutical Science, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
- * E-mail: (CLQ); (PJH)
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
- * E-mail: (CLQ); (PJH)
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Lussu M, Camboni T, Piras C, Serra C, Del Carratore F, Griffin J, Atzori L, Manzin A. 1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI). BMC Microbiol 2017; 17:201. [PMID: 28934947 PMCID: PMC5609053 DOI: 10.1186/s12866-017-1108-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 09/13/2017] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. METHODS Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. RESULTS Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. CONCLUSIONS Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.
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Affiliation(s)
- Milena Lussu
- Department of Biomedical Sciences, Microbiology and Virology Unit, University of Cagliari, S.S. 554, Bivio per Sestu, I-09042, Monserrato, Cagliari, Italy
| | - Tania Camboni
- Department of Biomedical Sciences, Microbiology and Virology Unit, University of Cagliari, S.S. 554, Bivio per Sestu, I-09042, Monserrato, Cagliari, Italy
| | - Cristina Piras
- Department of Biomedical Sciences, Microbiology and Virology Unit, University of Cagliari, S.S. 554, Bivio per Sestu, I-09042, Monserrato, Cagliari, Italy
| | - Corrado Serra
- Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Francesco Del Carratore
- Department of Biomedical Sciences, Microbiology and Virology Unit, University of Cagliari, S.S. 554, Bivio per Sestu, I-09042, Monserrato, Cagliari, Italy.,Faculty of Life Sciences, University of Manchester, Manchester, UK
| | - Julian Griffin
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Luigi Atzori
- Department of Biomedical Sciences, Microbiology and Virology Unit, University of Cagliari, S.S. 554, Bivio per Sestu, I-09042, Monserrato, Cagliari, Italy
| | - Aldo Manzin
- Department of Biomedical Sciences, Microbiology and Virology Unit, University of Cagliari, S.S. 554, Bivio per Sestu, I-09042, Monserrato, Cagliari, Italy.
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Grochocki W, Markuszewski MJ, Quirino JP. Simultaneous determination of creatinine and acetate by capillary electrophoresis with contactless conductivity detector as a feasible approach for urinary tract infection diagnosis. J Pharm Biomed Anal 2017; 137:178-181. [PMID: 28131056 DOI: 10.1016/j.jpba.2017.01.032] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 12/28/2016] [Accepted: 01/16/2017] [Indexed: 12/19/2022]
Abstract
Urinary tract infection (UTI) is one of the most common bacterial infection in human but its diagnosis is difficult. Metabolomic studies with nuclear magnetic resonance of urine have shown that acetic acid/creatinine ratio may be used for early UTI diagnosis. Here, a method for simultaneous determination of acetate and creatinine by capillary zone electrophoresis with contactless conductivity detector was developed for the first time. The separation was with 40mM MES and 20mM l-histidine as a background solution. The total time of a single run, including capillary conditioning, was less than 12min. The method was successfully demonstrated for analysis of actual and fortified human urine samples after methanol dilution. Analytical figures of merit such as linearity, LOQ, and repeatability (intraday and interday) were studied.
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Affiliation(s)
- Wojciech Grochocki
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdansk, Gdansk, Poland; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Hobart, 7001, Australia
| | - Michał J Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdansk, Gdansk, Poland.
| | - Joselito P Quirino
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Hobart, 7001, Australia.
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8
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NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients. Anal Bioanal Chem 2016; 409:1405-1413. [PMID: 27900420 DOI: 10.1007/s00216-016-0074-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 10/05/2016] [Accepted: 10/31/2016] [Indexed: 12/27/2022]
Abstract
The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using 1H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.
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Palama TL, Canard I, Rautureau GJP, Mirande C, Chatellier S, Elena-Herrmann B. Identification of bacterial species by untargeted NMR spectroscopy of the exo-metabolome. Analyst 2016; 141:4558-61. [PMID: 27349704 DOI: 10.1039/c6an00393a] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Identification of bacterial species is a crucial bottleneck for clinical diagnosis of infectious diseases. Quick and reliable identification is a key factor to provide suitable antibiotherapies and avoid the development of multiple-drug resistance. We propose a novel nuclear magnetic resonance (NMR)-based metabolomics strategy for rapid discrimination and identification of several bacterial species that relies on untargeted metabolic profiling of supernatants from bacterial culture media. We show that six bacterial species (Gram negative: Escherichia coli, Pseudomonas aeruginosa, Proteus mirabilis; Gram positive: Enterococcus faecalis, Staphylococcus aureus, and Staphylococcus saprophyticus) can be well discriminated from multivariate statistical analysis, opening new prospects for NMR applications to microbial clinical diagnosis.
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Affiliation(s)
- T L Palama
- Université de Lyon, Institut des Sciences Analytiques (CNRS/ENS Lyon/UCB Lyon1), Centre de RMN à Très Hauts Champs, 69100 Villeurbanne, France.
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Zou X, Holmes E, Nicholson JK, Loo RL. Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses. Anal Chem 2016; 88:5670-9. [PMID: 27149575 DOI: 10.1021/acs.analchem.5b04020] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data.
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Affiliation(s)
- Xin Zou
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University , 800 Dongchuan Road, Shanghai 200240, China.,Medway Metabonomics Research Group, Medway School of Pharmacy, Universities of Kent and Greenwich , Chatham Maritime, Kent, ME4 4TB, U.K
| | - Elaine Holmes
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K.,MRC-NIHR Phenome Centre, Imperial College London , London SW7 2AZ, U.K
| | - Jeremy K Nicholson
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K.,MRC-NIHR Phenome Centre, Imperial College London , London SW7 2AZ, U.K
| | - Ruey Leng Loo
- Medway Metabonomics Research Group, Medway School of Pharmacy, Universities of Kent and Greenwich , Chatham Maritime, Kent, ME4 4TB, U.K.,Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , London SW7 2AZ, U.K
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11
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Abstract
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.
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Affiliation(s)
- David Antcliffe
- Department of Surgery & Cancer, Charing Cross Hospital / Imperial College London, Section of Anaesthetics, Pain Medicine & Intensive Care, London, UK
| | - Anthony C Gordon
- Department of Surgery & Cancer, Charing Cross Hospital / Imperial College London, Section of Anaesthetics, Pain Medicine & Intensive Care, London, UK.
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12
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Serum procalcitonin levels in combination with (1)H NMR spectroscopy: A rapid indicator for differentiation of urosepsis. Clin Chim Acta 2015; 453:205-14. [PMID: 26719034 DOI: 10.1016/j.cca.2015.12.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2015] [Revised: 12/01/2015] [Accepted: 12/17/2015] [Indexed: 11/20/2022]
Abstract
BACKGROUND Urosepsis, a severe form of sepsis requires immediate medical attention for prognosis. It is clinically diagnosed by estimating serum procalcitonin (PCT) levels along with time taking urine and blood cultures. We explored NMR based profiling, deriving metabolites that could potentially aid diagnosis. METHODS The proton NMR of serum and urine samples of healthy control subjects (n=32) and urosepsis cases (n=35) based on PCT levels, were analyzed. Four clinically identified non-urosepsis cases with high PCT levels were also differentiated through principal component analysis (PCA) of the serum samples. RESULTS Quantification of serum and urine through Discriminant Function Analysis (DFA) afforded 93.7% and 91.7% correct classification respectively, along with identification of malonate and urea as potential biomarkers for the disease in both urine and serum samples. The partial least square discriminant analysis (PLS-DA) showed an R(2) value of 0.97 in both biofluids with Q(2)=0.87 and 0.85 for serum and urine respectively. The training set of serum samples provided precise prediction of the test set in a small cohort through random re-sampling method, while in urine samples, the predictions were inconclusive. CONCLUSIONS Our pilot study reveals that (1)H NMR of serum metabolic profiling in combination with PCT levels may provide a rapid method for differentiation of urosepsis.
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13
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The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol Biol 2015; 1277:161-93. [PMID: 25677154 DOI: 10.1007/978-1-4939-2377-9_13] [Citation(s) in RCA: 333] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.
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14
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Hanna MH, Brophy PD. Metabolomics in pediatric nephrology: emerging concepts. Pediatr Nephrol 2015; 30:881-7. [PMID: 25027575 PMCID: PMC4297580 DOI: 10.1007/s00467-014-2880-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2014] [Revised: 06/04/2014] [Accepted: 06/04/2014] [Indexed: 10/25/2022]
Abstract
Metabolomics, the latest of the "omics" sciences, refers to the systematic study of metabolites and their changes in biological samples due to physiological stimuli and/or genetic modification. Because metabolites represent the downstream expression of genome, transcriptome, and proteome, they can closely reflect the phenotype of an organism at a specific time. As an emerging field in analytical biochemistry, metabolomics has the potential to play a major role in monitoring real-time kidney function and detecting adverse renal events. Additionally, small molecule metabolites can provide mechanistic insights into novel biomarkers of kidney diseases, given the limitations of the current traditional markers. The clinical utility of metabolomics in the field of pediatric nephrology includes biomarker discovery, defining as yet unrecognized biological therapeutic targets, linking of metabolites to relevant standard indices and clinical outcomes, and providing a window of opportunity to investigate the intricacies of environment/genetic interplay in specific disease states.
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Affiliation(s)
- Mina H Hanna
- Department of Pediatrics, University of Kentucky, Lexington, KY, USA
| | - Patrick D Brophy
- Department of Pediatrics, University of Iowa, Iowa City, IA, USA,Corresponding Author: Patrick D. Brophy, MD, Director Pediatric Nephrology, University of Iowa Children’s Hospital, 285 Newton Rd, 1269A CBRB, Iowa City, IA, 52242, Tel: 319-384-3090, Fax: 319-384-3050,
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15
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Whiteside SA, Razvi H, Dave S, Reid G, Burton JP. The microbiome of the urinary tract--a role beyond infection. Nat Rev Urol 2015; 12:81-90. [PMID: 25600098 DOI: 10.1038/nrurol.2014.361] [Citation(s) in RCA: 401] [Impact Index Per Article: 40.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Urologists rarely need to consider bacteria beyond their role in infectious disease. However, emerging evidence shows that the microorganisms inhabiting many sites of the body, including the urinary tract--which has long been assumed sterile in healthy individuals--might have a role in maintaining urinary health. Studies of the urinary microbiota have identified remarkable differences between healthy populations and those with urologic diseases. Microorganisms at sites distal to the kidney, bladder and urethra are likely to have a profound effect on urologic health, both positive and negative, owing to their metabolic output and other contributions. Connections between the gut microbiota and renal stone formation have already been discovered. In addition, bacteria are also used in the prevention of bladder cancer recurrence. In the future, urologists will need to consider possible influences of the microbiome in diagnosis and treatment of certain urological conditions. New insights might provide an opportunity to predict the risk of developing certain urological diseases and could enable the development of innovative therapeutic strategies.
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Affiliation(s)
- Samantha A Whiteside
- Department of Microbiology and Immunology, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Hassan Razvi
- Division of Urology, Department of Surgery, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Sumit Dave
- Division of Urology, Department of Surgery, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Gregor Reid
- Canadian Centre for Human Microbiome and Probiotic Research, Lawson Health Research Institute, 268 Grosvenor Street, London, ON N6A 4V2, Canada
| | - Jeremy P Burton
- Division of Urology, Department of Surgery, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
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16
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Pacchiarotta T, Derks RJ, Nevedomskaya E, van der Starre W, van Dissel J, Deelder A, Mayboroda OA. Exploratory analysis of urinary tract infection using a GC-APCI-MS platform. Analyst 2015; 140:2834-41. [DOI: 10.1039/c5an00033e] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This study shows the first application of GC-APCI-MS in a clinical setting specifically in the context of urinary tract infection.
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Affiliation(s)
- Tiziana Pacchiarotta
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Rico J. Derks
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Ekaterina Nevedomskaya
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | | | - Jaap van Dissel
- Department of Infectious Diseases
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - André Deelder
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
| | - Oleg A. Mayboroda
- Center for Proteomics and Metabolomics
- Leiden University Medical Center
- Leiden
- The Netherlands
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17
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Emwas AH, Luchinat C, Turano P, Tenori L, Roy R, Salek RM, Ryan D, Merzaban JS, Kaddurah-Daouk R, Zeri AC, Nagana Gowda GA, Raftery D, Wang Y, Brennan L, Wishart DS. Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review. Metabolomics 2015; 11:872-894. [PMID: 26109927 PMCID: PMC4475544 DOI: 10.1007/s11306-014-0746-7] [Citation(s) in RCA: 157] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 10/27/2014] [Indexed: 02/08/2023]
Abstract
The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.
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Affiliation(s)
- Abdul-Hamid Emwas
- Imaging and Characterization Core Lab, King Abdullah University of Science and Technology, KSA, Thuwal, Saudi Arabia
| | - Claudio Luchinat
- Centro Risonanze Magnetiche – CERM, University of Florence, Florence, Italy
| | - Paola Turano
- Centro Risonanze Magnetiche – CERM, University of Florence, Florence, Italy
| | | | - Raja Roy
- Centre of Biomedical Research, Formerly known as Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Lucknow, India
| | - Reza M. Salek
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, CB10 1SD UK
| | - Danielle Ryan
- School of Agricultural and Wine Sciences, Charles Sturt University, Wagga Wagga, Australia
| | - Jasmeen S. Merzaban
- Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, KSA, Thuwal, Saudi Arabia
| | - Rima Kaddurah-Daouk
- Pharmacometabolomics Center, School of Medicine, Duke University, Durham, USA
| | - Ana Carolina Zeri
- Brazilian Biosciences National Laboratory, LNBio, Campinas, SP Brazil
| | - G. A. Nagana Gowda
- Department of Anethesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, 850 Republican St., Seattle, WA 98109 USA
| | - Daniel Raftery
- Department of Anethesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, 850 Republican St., Seattle, WA 98109 USA
| | - Yulan Wang
- Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Beijing, China
| | - Lorraine Brennan
- Institute of Food and Health and Conway Institute, School of Agriculture & Food Science, Dublin 4, Ireland
| | - David S. Wishart
- Department of Computing Science, University of Alberta, Edmonton, Alberta Canada
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18
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Lam CW, Law CY, To KKW, Cheung SKK, Lee KC, Sze KH, Leung KF, Yuen KY. NMR-based metabolomic urinalysis: A rapid screening test for urinary tract infection. Clin Chim Acta 2014; 436:217-23. [DOI: 10.1016/j.cca.2014.05.014] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 05/06/2014] [Accepted: 05/15/2014] [Indexed: 02/01/2023]
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19
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Zou X, Holmes E, Nicholson JK, Loo RL. Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection. Anal Chem 2014; 86:5308-15. [PMID: 24773160 PMCID: PMC4110102 DOI: 10.1021/ac500161k] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Accepted: 04/28/2014] [Indexed: 12/24/2022]
Abstract
We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data.
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Affiliation(s)
- Xin Zou
- Medway
School of Pharmacy, Universities of Kent
and Greenwich, Anson
Building, Central Avenue, Chatham, Kent ME4 4TB, U.K.
| | - Elaine Holmes
- Section
of Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
- MRC-HPA
Centre for Environment and Health, Imperial
College London, 150 Stamford
Street, London SE1 9NH, U.K.
| | - Jeremy K. Nicholson
- Section
of Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
- MRC-HPA
Centre for Environment and Health, Imperial
College London, 150 Stamford
Street, London SE1 9NH, U.K.
| | - Ruey Leng Loo
- Medway
School of Pharmacy, Universities of Kent
and Greenwich, Anson
Building, Central Avenue, Chatham, Kent ME4 4TB, U.K.
- Section
of Biomolecular Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
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20
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Duarte IF, Diaz SO, Gil AM. NMR metabolomics of human blood and urine in disease research. J Pharm Biomed Anal 2014; 93:17-26. [DOI: 10.1016/j.jpba.2013.09.025] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Revised: 09/16/2013] [Accepted: 09/24/2013] [Indexed: 02/06/2023]
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21
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Lacy P, McKay RT, Finkel M, Karnovsky A, Woehler S, Lewis MJ, Chang D, Stringer KA. Signal intensities derived from different NMR probes and parameters contribute to variations in quantification of metabolites. PLoS One 2014; 9:e85732. [PMID: 24465670 PMCID: PMC3897511 DOI: 10.1371/journal.pone.0085732] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Accepted: 12/02/2013] [Indexed: 12/29/2022] Open
Abstract
We discovered that serious issues could arise that may complicate interpretation of metabolomic data when identical samples are analyzed at more than one NMR facility, or using slightly different NMR parameters on the same instrument. This is important because cross-center validation metabolomics studies are essential for the reliable application of metabolomics to clinical biomarker discovery. To test the reproducibility of quantified metabolite data at multiple sites, technical replicates of urine samples were assayed by 1D-1H-NMR at the University of Alberta and the University of Michigan. Urine samples were obtained from healthy controls under a standard operating procedure for collection and processing. Subsequent analysis using standard statistical techniques revealed that quantitative data across sites can be achieved, but also that previously unrecognized NMR parameter differences can dramatically and widely perturb results. We present here a confirmed validation of NMR analysis at two sites, and report the range and magnitude that common NMR parameters involved in solvent suppression can have on quantitated metabolomics data. Specifically, saturation power levels greatly influenced peak height intensities in a frequency-dependent manner for a number of metabolites, which markedly impacted the quantification of metabolites. We also investigated other NMR parameters to determine their effects on further quantitative accuracy and precision. Collectively, these findings highlight the importance of and need for consistent use of NMR parameter settings within and across centers in order to generate reliable, reproducible quantified NMR metabolomics data.
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Affiliation(s)
- Paige Lacy
- Pulmonary Research Group, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | | | - Michael Finkel
- Department of Clinical, Social and Administrative Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Scott Woehler
- Department of Medicinal Chemistry and the Biochemical Nuclear Magnetic Resonance Core, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America
| | | | | | - Kathleen A. Stringer
- Department of Clinical, Social and Administrative Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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22
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Atzler D, Schwedhelm E, Zeller T. Integrated genomics and metabolomics in nephrology. Nephrol Dial Transplant 2013; 29:1467-74. [DOI: 10.1093/ndt/gft492] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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23
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Fanos V, Fanni C, Ottonello G, Noto A, Dessì A, Mussap M. Metabolomics in adult and pediatric nephrology. Molecules 2013; 18:4844-57. [PMID: 23615531 PMCID: PMC6270081 DOI: 10.3390/molecules18054844] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 03/26/2013] [Accepted: 04/18/2013] [Indexed: 12/22/2022] Open
Abstract
Metabolomics, the latest of the “omics” sciences, has a non-selective approach and can thus lead to the identification of all the metabolites (molecules < 1 kDa) in a biological system. The metabolomic profile can be considered the most predictive phenotype capable of evaluating epigenetic modifications determined by external factors. It is so close to the phenotype as to be considered the phenotype itself in its unique individuality (fingerprinting), both in health (phenome), and disease (diseasome). Urine, compared to other biological liquids, has the advantage of being a complex fluid with many components, including intermediate metabolites. Metabolomics may thus play a role in the study of different kidney diseases and overcome diagnostic difficulties. We shall present the studies that to our knowledge have been published on Nephrology and Pediatric Nephrology. Some are experimental while others are clinical. We have not considered carcinomas and transplantations. Although scarce, the data on adults and the very few ones in pediatrics are quite interesting. Further studies on kidneys are needed to determine the practical clinical impact of metabolomics in kidney renal pathologies. The “multiplatform” “omic” study of urine and namely metabolomics can contribute to improving early diagnosis and the outcome of kidney diseases.
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Affiliation(s)
- Vassilios Fanos
- Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, Azienda Ospedaliera Universitaria, Cagliari 09131, Italy.
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