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Anh NK, Phat NK, Thu NQ, Tien NTN, Eunsu C, Kim HS, Nguyen DN, Kim DH, Long NP, Oh JY. Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning. Sci Rep 2024; 14:15312. [PMID: 38961191 PMCID: PMC11222504 DOI: 10.1038/s41598-024-66113-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024] Open
Abstract
Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.
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Affiliation(s)
- Nguyen Ky Anh
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
- Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Nguyen Ky Phat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Quang Thu
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Tran Nam Tien
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Cho Eunsu
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Ho-Sook Kim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Duc Ninh Nguyen
- Section for Comparative Pediatrics and Nutrition, Department of Veterinary and Animal Sciences, University of Copenhagen, 1870, Frederiksberg, Denmark
| | - Dong Hyun Kim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, 47392, Republic of Korea.
| | - Jee Youn Oh
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, 08308, Republic of Korea.
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Olivier C, Allen B, Luies L. Optimising a urinary extraction method for non-targeted GC-MS metabolomics. Sci Rep 2023; 13:17591. [PMID: 37845360 PMCID: PMC10579216 DOI: 10.1038/s41598-023-44690-7] [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: 07/13/2023] [Accepted: 10/11/2023] [Indexed: 10/18/2023] Open
Abstract
Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.
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Affiliation(s)
- Cara Olivier
- Human Metabolomics, North-West University, Potchefstroom Campus, Private Bag X6001, Box 269, Potchefstroom, 2520, NW, South Africa
| | - Bianca Allen
- Human Metabolomics, North-West University, Potchefstroom Campus, Private Bag X6001, Box 269, Potchefstroom, 2520, NW, South Africa
| | - Laneke Luies
- Human Metabolomics, North-West University, Potchefstroom Campus, Private Bag X6001, Box 269, Potchefstroom, 2520, NW, South Africa.
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Houske EA, Glimm MG, Bergstrom AR, Slipher SK, Welhaven HD, Greenwood MC, Linse GM, June RK, Yu ASL, Wallace DP, Hahn AK. Metabolomic profiling to identify early urinary biomarkers and metabolic pathway alterations in autosomal dominant polycystic kidney disease. Am J Physiol Renal Physiol 2023; 324:F590-F602. [PMID: 37141147 PMCID: PMC10281782 DOI: 10.1152/ajprenal.00301.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 04/06/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023] Open
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the formation of numerous fluid-filled cysts that lead to progressive loss of functional nephrons. Currently, there is an unmet need for diagnostic and prognostic indicators of early stages of the disease. Metabolites were extracted from the urine of patients with early-stage ADPKD (n = 48 study participants) and age- and sex-matched normal controls (n = 47) and analyzed by liquid chromatography-mass spectrometry. Orthogonal partial least squares-discriminant analysis was used to generate a global metabolomic profile of early ADPKD for the identification of metabolic pathway alterations and discriminatory metabolites as candidates of diagnostic and prognostic biomarkers. The global metabolomic profile exhibited alterations in steroid hormone biosynthesis and metabolism, fatty acid metabolism, pyruvate metabolism, amino acid metabolism, and the urea cycle. A panel of 46 metabolite features was identified as candidate diagnostic biomarkers. Notable putative identities of candidate diagnostic biomarkers for early detection include creatinine, cAMP, deoxycytidine monophosphate, various androgens (testosterone; 5-α-androstane-3,17,dione; trans-dehydroandrosterone), betaine aldehyde, phosphoric acid, choline, 18-hydroxycorticosterone, and cortisol. Metabolic pathways associated with variable rates of disease progression included steroid hormone biosynthesis and metabolism, vitamin D3 metabolism, fatty acid metabolism, the pentose phosphate pathway, tricarboxylic acid cycle, amino acid metabolism, sialic acid metabolism, and chondroitin sulfate and heparin sulfate degradation. A panel of 41 metabolite features was identified as candidate prognostic biomarkers. Notable putative identities of candidate prognostic biomarkers include ethanolamine, C20:4 anandamide phosphate, progesterone, various androgens (5-α-dihydrotestosterone, androsterone, etiocholanolone, and epiandrosterone), betaine aldehyde, inflammatory lipids (eicosapentaenoic acid, linoleic acid, and stearolic acid), and choline. Our exploratory data support metabolic reprogramming in early ADPKD and demonstrate the ability of liquid chromatography-mass spectrometry-based global metabolomic profiling to detect metabolic pathway alterations as new therapeutic targets and biomarkers for early diagnosis and tracking disease progression of ADPKD.NEW & NOTEWORTHY To our knowledge, this study is the first to generate urinary global metabolomic profiles from individuals with early-stage ADPKD with preserved renal function for biomarker discovery. The exploratory dataset reveals metabolic pathway alterations that may be responsible for early cystogenesis and rapid disease progression and may be potential therapeutic targets and pathway sources for candidate biomarkers. From these results, we generated a panel of candidate diagnostic and prognostic biomarkers of early-stage ADPKD for future validation.
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Affiliation(s)
- Eden A Houske
- Department of Biological and Environmental Science, Carroll College, Helena, Montana, United States
| | - Matthew G Glimm
- Department of Biological and Environmental Science, Carroll College, Helena, Montana, United States
| | - Annika R Bergstrom
- Department of Chemical and Biological Engineering, Villanova University, Villanova, Pennsylvania, United States
| | - Sally K Slipher
- Department of Mathematical Sciences, Montana State University, Bozeman, Montana, United States
| | - Hope D Welhaven
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, United States
- Molecular Biosciences Program, Montana State University, Bozeman, Montana, United States
| | - Mark C Greenwood
- Department of Mathematical Sciences, Montana State University, Bozeman, Montana, United States
| | - Greta M Linse
- Department of Mathematical Sciences, Montana State University, Bozeman, Montana, United States
| | - Ronald K June
- Department of Mechanical and Industrial Engineering, Montana State University, Bozeman, Montana, United States
| | - Alan S L Yu
- Department of Internal Medicine, Jared Grantham Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, United States
| | - Darren P Wallace
- Department of Internal Medicine, Jared Grantham Kidney Institute, University of Kansas Medical Center, Kansas City, Kansas, United States
| | - Alyssa K Hahn
- Department of Biological and Environmental Science, Carroll College, Helena, Montana, United States
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Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment: A Pilot Study. Metabolites 2020; 10:metabo10090357. [PMID: 32878308 PMCID: PMC7569858 DOI: 10.3390/metabo10090357] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 12/12/2022] Open
Abstract
The lack of sensitive and specific biomarkers for the early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) is a major hurdle to improving patient management. A targeted, quantitative metabolomics approach using both 1H NMR and mass spectrometry was employed to investigate the performance of urine metabolites as potential biomarkers for MCI and AD. Correlation-based feature selection (CFS) and least absolute shrinkage and selection operator (LASSO) methods were used to develop biomarker panels tested using support vector machine (SVM) and logistic regression models for diagnosis of each disease state. Metabolic changes were investigated to identify which biochemical pathways were perturbed as a direct result of MCI and AD in urine. Using SVM, we developed a model with 94% sensitivity, 78% specificity, and 78% AUC to distinguish healthy controls from AD sufferers. Using logistic regression, we developed a model with 85% sensitivity, 86% specificity, and an AUC of 82% for AD diagnosis as compared to cognitively healthy controls. Further, we identified 11 urinary metabolites that were significantly altered to include glucose, guanidinoacetate, urocanate, hippuric acid, cytosine, 2- and 3-hydroxyisovalerate, 2-ketoisovalerate, tryptophan, trimethylamine N oxide, and malonate in AD patients, which are also capable of diagnosing MCI, with a sensitivity value of 76%, specificity of 75%, and accuracy of 81% as compared to healthy controls. This pilot study suggests that urine metabolomics may be useful for developing a test capable of diagnosing and distinguishing MCI and AD from cognitively healthy controls.
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Khodadadi M, Pourfarzam M. A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry. Metabolomics 2020; 16:66. [PMID: 32419109 DOI: 10.1007/s11306-020-01687-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 04/30/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Human urine gives evidence of the metabolism in the body and contains different metabolites at various concentrations. A number of analytical techniques including mass spectrometry (MS) and nuclear magnetic resonance (NMR) have been used to obtain metabolites levels in urine samples. However, gas chromatography-mass spectrometry (GC-MS) is one of the most widely used techniques for urinary metabolomics studies due to its higher sensitivity, resolution, reproducibility, reliability, relatively low cost and ease of operation compared to liquid chromatography-mass spectrometry and NMR. AIM OF REVIEW This review looks at various aspects of urine preparation prior to analysis by GC-MS including sample storage, urease pretreatment, derivatization, use of internal standard and quality control samples for data correction. In addition, most common types of inlet liners, ionization techniques and columns are discussed and a summary of mass analyzers are also highlighted. Lastly, the role of retention index in metabolite identification and data normalization methods are presented. KEY SCIENTIFIC CONCEPTS OF REVIEW The purpose of this review is summarizing methods of sample storage, pretreatment, and GC-MS analysis that are mostly used in urine metabolomics studies. Specific emphasis is given to the critical steps within the GC-MS urine metabolomics that those new to this field need to be aware of and the remaining challenges that require further attention and studies.
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Affiliation(s)
- Mohammad Khodadadi
- Department of Clinical Biochemistry, School of Pharmacy & Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Morteza Pourfarzam
- Department of Clinical Biochemistry, School of Pharmacy & Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran.
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