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Vanden Broecke E, Van Mulders L, De Paepe E, Daminet S, Vanhaecke L. Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine. Anal Chim Acta 2024; 1310:342694. [PMID: 38811133 DOI: 10.1016/j.aca.2024.342694] [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: 12/07/2023] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed. RESULTS Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices). SIGNIFICANCE This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.
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Affiliation(s)
- Ellen Vanden Broecke
- Ghent University, Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Salisburylaan 133, B-9820, Merelbeke, Belgium; Ghent University, Faculty of Veterinary Medicine, Department of Small Animals, Salisburylaan 133, B-9820, Merelbeke, Belgium
| | - Laurens Van Mulders
- Ghent University, Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Salisburylaan 133, B-9820, Merelbeke, Belgium; Ghent University, Faculty of Veterinary Medicine, Department of Small Animals, Salisburylaan 133, B-9820, Merelbeke, Belgium
| | - Ellen De Paepe
- Ghent University, Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Salisburylaan 133, B-9820, Merelbeke, Belgium
| | - Sylvie Daminet
- Ghent University, Faculty of Veterinary Medicine, Department of Small Animals, Salisburylaan 133, B-9820, Merelbeke, Belgium
| | - Lynn Vanhaecke
- Ghent University, Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Salisburylaan 133, B-9820, Merelbeke, Belgium; Queen's University Belfast, School of Biological Sciences, Institute for Global Food Security, Chlorine Gardens 19, BT9-5DL, Belfast, Northern Ireland, United Kingdom.
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Lin Y, Craig E, Liu X, Ge Y, Brunner J, Wang X, Yang Z, Hopke PK, Miller RK, Barrett ES, Thurston SW, Murphy SK, O'Connor TG, Rich DQ, Zhang JJ. Urinary 1-hydroxypyrene in pregnant women in a Northeastern U.S. city: socioeconomic disparity and contributions from air pollution sources. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2024; 34:407-415. [PMID: 37161057 DOI: 10.1038/s41370-023-00555-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/11/2023]
Abstract
BACKGROUND Maternal exposure to polycyclic aromatic hydrocarbons (PAHs), ubiquitous constituents of air pollution, has been associated with adverse birth outcomes. Yet it remains unclear whether and how socioeconomic status (SES) affects gestational PAH exposure. OBJECTIVE To examine whether there are socioeconomic disparities in PAHs exposure among pregnant women from Rochester, NY, and if so, to what extent disproportionate proximity to air pollution sources, measured by residential distance to transportation-related sources, contributed to the exposure disparity. METHODS We measured 1-hydroxypyrene concentrations in 726 urine samples collected from 305 pregnant women up to three samples throughout pregnancy. Residential distances to transportation-related sources were calculated based on participants' home addresses. We used linear mixed-effects models with random intercepts of participants to examine associations between 1-hydroxypyrene, SES indicators, and distance to transportation-related sources. We used structural equation modelling to assess to what extent distance to transportation-related sources contributes to the socioeconomic disparity in 1-hydroxypyrene concentrations. RESULTS Reduced household income and maternal education level were both significant SES predictors of 1-hydroxypyrene concentrations, after the adjustment for other maternal demographic characteristics. Each interquartile range (IQR) increases in residential proximity to the airport (from 14.3 to 6.0 km), the railroad yard (from 22.3 to 6.0 km), and annual average daily traffic within 300 m (from 3796 to 99,933 vehicles/year) were associated with 15.0% (95%CI: 7.0-22.2%), 15.4% (95%CI: 6.5-23.5%), and 13.6% (95%CI: 4.7-23.3%) increases in 1-hydroxypyrene concentrations, respectively. Proximity to these sources jointly explained 10% (95%CI: 1.6-18.4%) of the 1-hydroxypyrene concentration change associated with decreases in SES as a latent variable defined by both household income and education level. IMPACT STATEMENT Our findings suggest that efforts to address disproportionate residential proximity to transportation-related sources may reduce the socioeconomic disparity in PAH exposure.
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Affiliation(s)
- Yan Lin
- Nicholas School of the Environment & Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Emily Craig
- Nicholas School of the Environment & Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Xiaodong Liu
- State Key Laboratory of Respiratory Diseases, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yihui Ge
- Nicholas School of the Environment & Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Jessica Brunner
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - Xiangtian Wang
- Nicholas School of the Environment & Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Zhenchun Yang
- Nicholas School of the Environment & Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Philip K Hopke
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
- Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, USA
| | - Richard K Miller
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
- Department of Pathology and Clinical Laboratory Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Emily S Barrett
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
- Department of Epidemiology and Biostatistics, Environmental and Occupational Health Sciences Institute, Rutgers School of Public Health, Piscataway, NJ, USA
| | - Sally W Thurston
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Susan K Murphy
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC, USA
| | - Thomas G O'Connor
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
- Department of Psychology, University of Rochester, Rochester, NY, USA
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
| | - David Q Rich
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
- Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
- Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Junfeng Jim Zhang
- Nicholas School of the Environment & Duke Global Health Institute, Duke University, Durham, NC, USA.
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Yuan Q, Gu B, Liu W, Wen X, Wang J, Tang J, Usman M, Liu S, Tang Y, Wang L. Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers. J Cell Mol Med 2024; 28:e18292. [PMID: 38652116 PMCID: PMC11037414 DOI: 10.1111/jcmm.18292] [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: 01/12/2024] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.
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Affiliation(s)
- Quan Yuan
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Bin Gu
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Wei Liu
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Xin‐Ru Wen
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Ji‐Liang Wang
- Department of Laboratory MedicineShengli Oilfield Central HospitalDongyingChina
| | - Jia‐Wei Tang
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Muhammad Usman
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
| | - Su‐Ling Liu
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
| | - Yu‐Rong Tang
- Department of Laboratory MedicineShengli Oilfield Central HospitalDongyingChina
| | - Liang Wang
- School of Medical Informatics and EngineeringXuzhou Medical UniversityXuzhouChina
- Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouChina
- Division of Microbiology and Immunology, School of Biomedical SciencesThe University of Western AustraliaCrawleyWestern AustraliaAustralia
- School of Agriculture and Food SustainabilityUniversity of QueenslandBrisbaneQueenslandAustralia
- Centre for Precision Health, School of Medical and Health SciencesEdith Cowan UniversityPerthWestern AustraliaAustralia
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Lonati C, Berezhnoy G, Lawler N, Masuda R, Kulkarni A, Sala S, Nitschke P, Zizmare L, Bucci D, Cannet C, Schäfer H, Singh Y, Gray N, Lodge S, Nicholson J, Merle U, Wist J, Trautwein C. Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD + pathway and SIRT1 activation. Clin Chem Lab Med 2024; 62:770-788. [PMID: 37955280 DOI: 10.1515/cclm-2023-1017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/22/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata. METHODS The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data. RESULTS Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway. CONCLUSIONS Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.
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Affiliation(s)
- Caterina Lonati
- Center for Preclinical Research, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Georgy Berezhnoy
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Nathan Lawler
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Reika Masuda
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Aditi Kulkarni
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Samuele Sala
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Philipp Nitschke
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Laimdota Zizmare
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Daniele Bucci
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
| | - Claire Cannet
- Bruker BioSpin GmbH, AIC Division, Ettlingen, Germany
| | | | - Yogesh Singh
- Institute of Medical Genetics and Applied Genomics, University Hospital Tübingen, Tübingen, Germany
| | - Nicola Gray
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Samantha Lodge
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Jeremy Nicholson
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Uta Merle
- Department of Internal Medicine IV, University Hospital Heidelberg, Heidelberg, Germany
| | - Julien Wist
- Australian National Phenome Centre and Computational and Systems Medicine, Health Futures Institute, Murdoch University Perth, Australia
| | - Christoph Trautwein
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University Hospital Tübingen, Tübingen, Germany
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Girel S, Markin PA, Tobolkina E, Boccard J, Moskaleva NE, Rudaz S, Appolonova SA. Comprehensive plasma steroidomics reveals subtle alterations of systemic steroid profile in patients at different stages of prostate cancer disease. Sci Rep 2024; 14:1577. [PMID: 38238434 PMCID: PMC10796437 DOI: 10.1038/s41598-024-51859-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
The steroid submetabolome, or steroidome, is of particular interest in prostate cancer (PCa) as the dependence of PCa growth on androgens is well known and has been routinely exploited in treatment for decades. Nevertheless, the community is still far from a comprehensive understanding of steroid involvement in PCa both at the tissue and at systemic level. In this study we used liquid chromatography/high resolution mass spectrometry (LC/HRMS) backed by a dynamic retention time database DynaSTI to obtain a readout on circulating steroids in a cohort reflecting a progression of the PCa. Hence, 60 relevant compounds were annotated in the resulting LC/HRMS data, including 22 unknown steroid isomers therein. Principal component analysis revealed only subtle alterations of the systemic steroidome in the study groups. Next, a supervised approach allowed for a differentiation between the healthy state and any of the stages of the disease. Subsequent clustering of steroid metabolites revealed two groups responsible for this outcome: one consisted primarily of the androgens, whereas another contained corticosterone and its metabolites. The androgen data supported the currently established involvement of a hypothalamic-pituitary-gonadal axis in the development of PCa, whereas biological role of corticosterone remained elusive. On top of that, current results suggested a need for improvement in the dynamic range of the analytical methods to better understand the role of low abundant steroids, as the analysis revealed an involvement of estrogen metabolites. In particular, 2-hydroxyestradiol-3-methylether, one of the compounds present in the disease phenotype, was annotated and reported for the first time in men.
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Affiliation(s)
- Sergey Girel
- School of Pharmaceutical Sciences, University of Geneva, 1211, Geneva 4, Switzerland
| | - Pavel A Markin
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435, Moscow, Russia
| | - Elena Tobolkina
- School of Pharmaceutical Sciences, University of Geneva, 1211, Geneva 4, Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, 1211, Geneva 4, Switzerland
| | - Natalia E Moskaleva
- World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435, Moscow, Russia
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, 1211, Geneva 4, Switzerland.
| | - Svetlana A Appolonova
- Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, Moscow, Russia
- I.M. Sechenov First Moscow State Medical University, 119435, Moscow, Russia
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Brix F, Demetrowitsch T, Jensen-Kroll J, Zacharias HU, Szymczak S, Laudes M, Schreiber S, Schwarz K. Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data. Anal Chem 2024; 96:33-40. [PMID: 38113356 DOI: 10.1021/acs.analchem.3c01380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.
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Affiliation(s)
- Fynn Brix
- Institute of Human Nutrition and Food Science, Kiel University, Kiel, Heinrich-Hecht-Platz 10, 24118 Kiel, Germany
| | - Tobias Demetrowitsch
- Institute of Human Nutrition and Food Science, Kiel University, Kiel, Heinrich-Hecht-Platz 10, 24118 Kiel, Germany
| | - Julia Jensen-Kroll
- Institute of Human Nutrition and Food Science, Kiel University, Kiel, Heinrich-Hecht-Platz 10, 24118 Kiel, Germany
| | - Helena U Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 30625 Hannover, Germany
- Department of Internal Medicine I, University Medical Centre Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Silke Szymczak
- Institute of Medical Biometry and Statistics, University of Luebeck and Medical Centre Schleswig-Holstein, Campus Luebeck, 23562 Luebeck, Germany
| | - Matthias Laudes
- Department of Internal Medicine I, University Medical Centre Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Diabetes and Clinical Metabolic Research, Kiel University, Düsternbrooker Weg 17, 24105 Kiel, Germany
| | - Stefan Schreiber
- Department of Internal Medicine I, University Medical Centre Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Diabetes and Clinical Metabolic Research, Kiel University, Düsternbrooker Weg 17, 24105 Kiel, Germany
| | - Karin Schwarz
- Institute of Human Nutrition and Food Science, Kiel University, Kiel, Heinrich-Hecht-Platz 10, 24118 Kiel, Germany
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Ma Y, Yao J, Zhou L, Zhao M, Wang W, Liu J, Marchioni E. Comprehensive untargeted lipidomic analysis of sea buckthorn using UHPLC-HR-AM/MS/MS combined with principal component analysis. Food Chem 2024; 430:136964. [PMID: 37531917 DOI: 10.1016/j.foodchem.2023.136964] [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: 02/02/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/04/2023]
Abstract
Sea buckthorn is an important ecological and economic plant which has multiple bioactivities. The fruits and seeds of sea buckthorn are rich in oil. However, there are few studies on the differences of lipid profiles of sea buckthorn varieties. Herein, the lipidomic fingerprints of sea buckthorn was established. First, a mixture solvent of methanol and chloroform (2:1, v/v) was selected to extract the lipid of the flesh and seed of sea buckthorn. Then, global lipidomic analysis of different varieties of sea buckthorn was conducted. A total of 16 lipid classes and 112 lipid molecular species were determined. Several molecular species, such as PE (phosphatidylethanolamine) 18:1/18:3, PE18:0/18:1, PE18:0/18:2, etc. were selected as the potential biomarkers to classify the samples. Our study provides a scientific basis for quality control of sea buckthorn and promotes the development of sea buckthorn oil.
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Affiliation(s)
- Yue Ma
- National Demonstration Center for Experimental Ethnopharmacology Education, School of Pharmaceutical Sciences, South-Central MinZu University, Wuhan 430074, PR China
| | - Jiaxu Yao
- National Demonstration Center for Experimental Ethnopharmacology Education, School of Pharmaceutical Sciences, South-Central MinZu University, Wuhan 430074, PR China
| | - Li Zhou
- National Demonstration Center for Experimental Ethnopharmacology Education, School of Pharmaceutical Sciences, South-Central MinZu University, Wuhan 430074, PR China.
| | - Minjie Zhao
- Equipe de Chimie Analytique des Molécules Bioactives et Pharmacognoise, Institut Pluridisciplinaire Hubert Curien (UMR 7178, CNRS/UDS), 74 route du Rhin, 67400 Illkirch, France
| | - Wei Wang
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, PR China
| | - Jikai Liu
- National Demonstration Center for Experimental Ethnopharmacology Education, School of Pharmaceutical Sciences, South-Central MinZu University, Wuhan 430074, PR China.
| | - Eric Marchioni
- Equipe de Chimie Analytique des Molécules Bioactives et Pharmacognoise, Institut Pluridisciplinaire Hubert Curien (UMR 7178, CNRS/UDS), 74 route du Rhin, 67400 Illkirch, France
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Rosen Vollmar AK, Rattray NJW, Cai Y, Jain A, Yan H, Deziel NC, Calafat AM, Wilcox AJ, Jukic AMZ, Johnson CH. Urinary Paraben Concentrations and Associations with the Periconceptional Urinary Metabolome: Untargeted and Targeted Metabolomics Analyses of Participants from the Early Pregnancy Study. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:97006. [PMID: 37702489 PMCID: PMC10498870 DOI: 10.1289/ehp12125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 08/05/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND Parabens, found in everyday items from personal care products to foods, are chemicals with endocrine-disrupting activity, which has been shown to influence reproductive function. OBJECTIVES This study investigated whether urinary concentrations of methylparaben, propylparaben, or butylparaben were associated with the urinary metabolome during the periconceptional period, a critical window for female reproductive function. Changes to the periconceptional urinary metabolome could provide insights into the mechanisms by which parabens could impact fertility. METHODS Urinary paraben concentrations were measured in paired pre- and postconception urine samples from 42 participants in the Early Pregnancy Study, a prospective cohort of 221 women attempting to conceive. We performed untargeted and targeted metabolomics analyses using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry. We used principal component analysis, orthogonal partial least-squares discriminant analysis, and permutation testing, coupled with univariate statistical analyses, to find metabolites associated with paraben concentration at the two time points. Potential confounders were identified with a directed acyclic graph and used to adjust results with multivariable linear regression. Metabolites were identified using fragmentation data. RESULTS Seven metabolites were associated with paraben concentration (variable importance to projection score > 1 , false discovery rate-corrected q -value < 0.1 ). We identified four diet-related metabolites to the Metabolomics Standards Initiative (MSI) certainty of identification level 2, including metabolites from smoke flavoring, grapes, and olive oil. One metabolite was identified to the class level only (MSI level 3). Two metabolites were unidentified (MSI level 4). After adjustment, three metabolites remained associated with methylparaben and propylparaben, two of which were diet-related. No metabolomic markers of endocrine disruption were associated with paraben concentrations. DISCUSSION This study identified novel relationships between urinary paraben concentrations and diet-related metabolites but not with metabolites on endocrine-disrupting pathways, as hypothesized. It demonstrates the feasibility of integrating untargeted metabolomics data with environmental exposure information and epidemiological adjustment for confounders. The findings underscore a potentially important connection between diet and paraben exposure, with applications to nutritional epidemiology and dietary exposure assessment. https://doi.org/10.1289/EHP12125.
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Affiliation(s)
- Ana K Rosen Vollmar
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Nicholas J W Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Yuping Cai
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Abhishek Jain
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Hong Yan
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Nicole C Deziel
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
| | - Antonia M Calafat
- Organic Analytical Toxicology Branch, National Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Allen J Wilcox
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Anne Marie Z Jukic
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Caroline H Johnson
- Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA
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9
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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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10
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Giallourou N, Arnold J, McQuade ETR, Awoniyi M, Becket RVT, Walsh K, Herzog J, Gulati AS, Carroll IM, Montgomery S, Quintela PH, Faust AM, Singer SM, Fodor AA, Ahmad T, Mahfuz M, Mduma E, Walongo T, Guerrant RL, Balfour Sartor R, Swann JR, Kosek MN, Bartelt LA. Giardia hinders growth by disrupting nutrient metabolism independent of inflammatory enteropathy. Nat Commun 2023; 14:2840. [PMID: 37202423 PMCID: PMC10195804 DOI: 10.1038/s41467-023-38363-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/27/2023] [Indexed: 05/20/2023] Open
Abstract
Giardia lamblia (Giardia) is among the most common intestinal pathogens in children in low- and middle-income countries (LMICs). Although Giardia associates with early-life linear growth restriction, mechanistic explanations for Giardia-associated growth impairments remain elusive. Unlike other intestinal pathogens associated with constrained linear growth that cause intestinal or systemic inflammation or both, Giardia seldom associates with chronic inflammation in these children. Here we leverage the MAL-ED longitudinal birth cohort and a model of Giardia mono-association in gnotobiotic and immunodeficient mice to propose an alternative pathogenesis of this parasite. In children, Giardia results in linear growth deficits and gut permeability that are dose-dependent and independent of intestinal markers of inflammation. The estimates of these findings vary between children in different MAL-ED sites. In a representative site, where Giardia associates with growth restriction, infected children demonstrate broad amino acid deficiencies, and overproduction of specific phenolic acids, byproducts of intestinal bacterial amino acid metabolism. Gnotobiotic mice require specific nutritional and environmental conditions to recapitulate these findings, and immunodeficient mice confirm a pathway independent of chronic T/B cell inflammation. Taken together, we propose a new paradigm that Giardia-mediated growth faltering is contingent upon a convergence of this intestinal protozoa with nutritional and intestinal bacterial factors.
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Affiliation(s)
- Natasa Giallourou
- Division of Digestive Diseases, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
- Centre of Excellence in Biobanking and Biomedical Research, Molecular Medicine Research Center, University of Cyprus, Nicosia, Cyprus.
| | - Jason Arnold
- Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Molecular Genetics and Microbiology, Duke Microbiome Center, Duke University School of Medicine, Durham, NC, 27710, USA
| | | | - Muyiwa Awoniyi
- Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rose Viguna Thomas Becket
- Departments of Pediatrics and Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kenneth Walsh
- Institute for Infectious Diseases and Global Health and the Division of Infectious Diseases, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jeremy Herzog
- Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ajay S Gulati
- Departments of Pediatrics and Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ian M Carroll
- Department of Nutrition, Gillings School of Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephanie Montgomery
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Steven M Singer
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Anthony A Fodor
- The University of North Carolina Charlotte, Department of Bioinformatics and Genomics, Charlotte, USA
| | - Tahmeed Ahmad
- International Center for Diarrheal Disease Research, Dhaka, Bangladesh
| | - Mustafa Mahfuz
- International Center for Diarrheal Disease Research, Dhaka, Bangladesh
| | - Esto Mduma
- Haydom Global Health Research Centre, Haydom Lutheran Hospital, Haydom, Tanzania
| | - Thomas Walongo
- Haydom Global Health Research Centre, Haydom Lutheran Hospital, Haydom, Tanzania
| | - Richard L Guerrant
- Division of Infectious Diseases and International Health, Department of Medicine, The University of Virginia Charlottesville, Charlottesville, VA, USA
| | - R Balfour Sartor
- Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jonathan R Swann
- School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Margaret N Kosek
- Division of Infectious Diseases and International Health, Department of Medicine, The University of Virginia Charlottesville, Charlottesville, VA, USA
| | - Luther A Bartelt
- Center for Gastrointestinal Biology and Disease, Division of Gastroenterology and Hepatology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Institute for Infectious Diseases and Global Health and the Division of Infectious Diseases, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Microbiology & Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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11
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Wen Q, Myridakis A, Boshier PR, Zuffa S, Belluomo I, Parker AG, Chin ST, Hakim S, Markar SR, Hanna GB. A Complete Pipeline for Untargeted Urinary Volatolomic Profiling with Sorptive Extraction and Dual Polar and Nonpolar Column Methodologies Coupled with Gas Chromatography Time-of-Flight Mass Spectrometry. Anal Chem 2023; 95:758-765. [PMID: 36602225 PMCID: PMC9850407 DOI: 10.1021/acs.analchem.2c02873] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Volatolomics offers an opportunity for noninvasive detection and monitoring of human disease. While gas chromatography-mass spectrometry (GC-MS) remains the technique of choice for analyzing volatile organic compounds (VOCs), barriers to wider adoption in clinical practice still exist, including: sample preparation and introduction techniques, VOC extraction, throughput, volatolome coverage, biological interpretation, and quality control (QC). Therefore, we developed a complete pipeline for untargeted urinary volatolomic profiling. We optimized a novel extraction technique using HiSorb sorptive extraction, which exhibited high analytical performance and throughput. We achieved a broader VOC coverage by using HiSorb coupled with a set of complementary chromatographic methods and time-of-flight mass spectrometry. Furthermore, we developed a data preprocessing strategy by evaluating internal standard normalization, batch correction, and we adopted strict QC measures including removal of nonlinearly responding, irreproducible, or contaminated metabolic features, ensuring the acquisition of high-quality data. The applicability of this pipeline was evaluated in a clinical cohort consisting of pancreatic ductal adenocarcinoma (PDAC) patients (n = 28) and controls (n = 33), identifying four urinary candidate biomarkers (2-pentanone, hexanal, 3-hexanone, and p-cymene), which can successfully discriminate the cancer and noncancer subjects. This study presents an optimized, high-throughput, and quality-controlled pipeline for untargeted urinary volatolomic profiling. Use of the pipeline to discriminate PDAC from control subjects provides proof of principal of its clinical utility and potential for application in future biomarker discovery studies.
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Affiliation(s)
- Qing Wen
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Antonis Myridakis
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Piers R. Boshier
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Simone Zuffa
- Department
of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, United Kingdom
| | - Ilaria Belluomo
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Aaron G. Parker
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Sung-Tong Chin
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Stephanie Hakim
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom
| | - Sheraz R. Markar
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom,Nuffield
Department of Surgical Sciences, University
of Oxford, Oxford OX3 9DU, United Kingdom
| | - George B. Hanna
- Department
of Surgery and Cancer, Imperial College
London, London W12 0HS, United Kingdom,
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12
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Grootveld M, Page G, Bhogadia M, Hunwin K, Edgar M. Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part III: Implementations for the Diagnosis of Non-Cancerous Disorders, Both Oral and Systemic. Metabolites 2023; 13:metabo13010066. [PMID: 36676991 PMCID: PMC9864626 DOI: 10.3390/metabo13010066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
This communication represents Part III of our series of reports based on the applications of human saliva as a useful and conveniently collectable medium for the discovery, identification and monitoring of biomarkers, which are of some merit for the diagnosis of human diseases. Such biomarkers, or others reflecting the dysfunction of specific disease-associated metabolic pathways, may also be employed for the prognostic pathological tracking of these diseases. Part I of this series set the experimental and logistical groundwork for this report, and the preceding paper, Part II, featured the applications of newly developed metabolomics technologies to the diagnosis and severity grading of human cancer conditions, both oral and systemic. Clearly, there are many benefits, both scientific and economic, associated with the donation of human saliva samples (usually as whole mouth saliva) from humans consenting to and participating in investigations focused on the discovery of biomolecular markers of diseases. These include usually non-invasive collection protocols, relatively low cost when compared against blood sample collection, and no requirement for clinical supervision during collection episodes. This paper is centred on the employment and value of 'state-of-the-art' metabolomics technologies to the diagnosis and prognosis of a wide range of non-cancerous human diseases. Firstly, these include common oral diseases such as periodontal diseases (from type 1 (gingivitis) to type 4 (advanced periodontitis)), and dental caries. Secondly, a wide range of extra-oral (systemic) conditions are covered, most notably diabetes types 1 and 2, cardiovascular and neurological diseases, and Sjögren's syndrome, along with a series of viral infections, e.g., pharyngitis, influenza, HIV and COVID-19. Since the authors' major research interests lie in the area of the principles and applications of NMR-linked metabolomics techniques, many, but not all, of the studies reviewed were conducted using these technologies, with special attention being given to recommended protocols for their operation and management, for example, satisfactory experimental model designs; sample collection and laboratory processing techniques; the selection of sample-specific NMR pulse sequences for saliva analysis; and strategies available for the confirmation of resonance assignments for both endogenous and exogenous molecules in this biofluid. This article also features an original case study, which is focussed on the use of NMR-based salivary metabolomics techniques to provide some key biomarkers for the diagnosis of pharyngitis, and an example of how to 'police' such studies and to recognise participants who perceive that they actually have this disorder but do not from their metabolic profiles and multivariate analysis pattern-based clusterings. The biochemical and clinical significance of these multidimensional metabolomics investigations are discussed in detail.
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13
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Llambrich M, Brezmes J, Cumeras R. The untargeted urine volatilome for biomedical applications: methodology and volatilome database. Biol Proced Online 2022; 24:20. [PMID: 36456991 PMCID: PMC9714113 DOI: 10.1186/s12575-022-00184-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.
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Affiliation(s)
- Maria Llambrich
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira I Virgili, 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
| | - Jesús Brezmes
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira I Virgili, 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
| | - Raquel Cumeras
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira I Virgili, 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
- Oncology Department, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
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14
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Evaluation of Normalization Approaches for Quantitative Analysis of Bile Acids in Human Feces. Metabolites 2022; 12:metabo12080723. [PMID: 36005595 PMCID: PMC9416035 DOI: 10.3390/metabo12080723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/29/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022] Open
Abstract
Quantitative analysis of bile acids in human feces can potentially help to better understand the influence of the gut microbiome and diet on human health. Feces is a highly heterogeneous sample matrix, mainly consisting of water and indigestible solid material (as plant fibers) that show high inter-individual variability. To compare bile acid concentrations among different individuals, a reliable normalization approach is needed. Here, we compared the impact of three normalization approaches, namely sample wet weight, dry weight, and protein concentration, on the absolute concentrations of fecal bile acids. Bile acid concentrations were determined in 70 feces samples from healthy humans. Our data show that bile acid concentrations normalized by the three different approaches are substantially different for each individual sample. Fecal bile acid concentrations normalized by wet weight show the narrowest distribution. Therefore, our analysis will provide the basis for the selection of a suitable normalization approach for the quantitative analysis of bile acids in feces.
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15
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Contrepois K, Chen S, Ghaemi MS, Wong RJ, Jehan F, Sazawal S, Baqui AH, Nisar MI, Dhingra U, Khanam R, Ilyas M, Dutta A, Mehmood U, Deb S, Hotwani A, Ali SM, Rahman S, Nizar A, Ame SM, Muhammad S, Chauhan A, Khan W, Raqib R, Das S, Ahmed S, Hasan T, Khalid J, Juma MH, Chowdhury NH, Kabir F, Aftab F, Quaiyum MA, Manu A, Yoshida S, Bahl R, Rahman A, Pervin J, Price JT, Rahman M, Kasaro MP, Litch JA, Musonda P, Vwalika B, Stringer JSA, Shaw G, Stevenson DK, Aghaeepour N, Snyder MP. Prediction of gestational age using urinary metabolites in term and preterm pregnancies. Sci Rep 2022; 12:8033. [PMID: 35577875 PMCID: PMC9110694 DOI: 10.1038/s41598-022-11866-6] [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: 08/12/2021] [Accepted: 04/25/2022] [Indexed: 11/23/2022] Open
Abstract
Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC–MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.
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16
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Kong Q, Gu J, Lu R, Huang C, Hu X, Wu W, Lin D. NMR-Based Metabolomic Analysis of Sera in Mouse Models of CVB3-Induced Viral Myocarditis and Dilated Cardiomyopathy. Biomolecules 2022; 12:biom12010112. [PMID: 35053260 PMCID: PMC8773787 DOI: 10.3390/biom12010112] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/04/2023] Open
Abstract
Viral myocarditis (VMC) is an inflammatory heart condition which can induce dilated cardiomyopathy (DCM). However, molecular mechanisms underlying the progression of VMC into DCM remain exclusive. Here, we established mouse models of VMC and DCM by infecting male BALB/c mice with Coxsackievirus B3 (CVB3), and performed NMR-based metabonomic analyses of mouse sera. The mouse models covered three pathological stages including: acute VMC (aVMC), chronic VMC (cVMC) and DCM. We recorded 1D 1H-NMR spectra on serum samples and conducted multivariate statistical analysis on the NMR data. We found that metabolic profiles of these three pathological stages were distinct from their normal controls (CON), and identified significant metabolites primarily responsible for the metabolic distinctions. We identified significantly disturbed metabolic pathways in the aVMC, cVMC and DCM stages relative to CON, including: taurine and hypotaurine metabolism; pyruvate metabolism; glycine, serine and threonine metabolism; glycerolipid metabolism. Additionally, we identified potential biomarkers for discriminating a VMC, cVMC and DCM from CON including: taurine, valine and acetate for aVMC; glycerol, valine and leucine for cVMC; citrate, glycine and isoleucine for DCM. This work lays the basis for mechanistically understanding the progression from acute VMC to DCM, and is beneficial to exploitation of potential biomarkers for prognosis and diagnosis of heart diseases.
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Affiliation(s)
- Qing Kong
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China;
| | - Jinping Gu
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (J.G.); (R.L.); (X.H.)
- Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Ruohan Lu
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (J.G.); (R.L.); (X.H.)
| | - Caihua Huang
- Research and Communication Center of Exercise and Health, Xiamen University of Technology, Xiamen 361024, China;
| | - Xiaomin Hu
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (J.G.); (R.L.); (X.H.)
| | - Weifeng Wu
- Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China;
- Correspondence: (W.W.); (D.L.); Tel.: +86-771-5358955 (W.W.); +86-592-2186078 (D.L.)
| | - Donghai Lin
- Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China; (J.G.); (R.L.); (X.H.)
- Correspondence: (W.W.); (D.L.); Tel.: +86-771-5358955 (W.W.); +86-592-2186078 (D.L.)
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17
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Cloteau C, Dervilly G, Kaabia Z, Bagilet F, Delcourt V, Loup B, Guitton Y, Royer AL, Monteau F, Garcia P, Ma P, Le Bizec B, Bailly-Chouriberry L. FROM A NON-TARGETED METABOLOMICS APPROACH TO A TARGETED BIOMARKERS STRATEGY TO HIGHLIGHT TESTOSTERONE ABUSE IN EQUINE. ILLUSTRATION OF A METHODOLOGICAL TRANSFER BETWEEN PLATFORMS AND LABORATORIES. Drug Test Anal 2022; 14:864-878. [PMID: 35001538 DOI: 10.1002/dta.3221] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 12/17/2021] [Accepted: 12/27/2021] [Indexed: 11/09/2022]
Abstract
In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids (AAS) abuses in animal competitions, a non-targeted LC-HRMS based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on 4 potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput LC-SRM. The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.
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Affiliation(s)
- C Cloteau
- ONIRIS, INRAE, LABERCA, Nantes, France.,Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
| | | | - Z Kaabia
- Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
| | - F Bagilet
- Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
| | - V Delcourt
- Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
| | - B Loup
- Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
| | - Y Guitton
- ONIRIS, INRAE, LABERCA, Nantes, France
| | - A L Royer
- ONIRIS, INRAE, LABERCA, Nantes, France
| | - F Monteau
- ONIRIS, INRAE, LABERCA, Nantes, France
| | - P Garcia
- Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
| | - Popot Ma
- Laboratoire des Courses Hippiques (GIE-LCH), Verrières le Buisson, France
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18
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Design and Methods of the Validating Injury to the Renal Transplant Using Urinary Signatures (VIRTUUS) Study in Children. Transplant Direct 2021; 7:e791. [PMID: 34805493 PMCID: PMC8601357 DOI: 10.1097/txd.0000000000001244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 11/23/2022] Open
Abstract
Lack of noninvasive diagnostic and prognostic biomarkers to reliably detect early allograft injury poses a major hindrance to long-term allograft survival in pediatric kidney transplant recipients.
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19
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Drago D, Andolfo A, Mosca E, Orro A, Nocera L, Cucchiara V, Bellone M, Montorsi F, Briganti A. A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer. Cancer Biol Med 2021; 18:j.issn.2095-3941.2020.0617. [PMID: 34037347 PMCID: PMC8185872 DOI: 10.20892/j.issn.2095-3941.2020.0617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/25/2020] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Significant efforts are currently being made to identify novel biomarkers for the diagnosis and risk stratification of prostate cancer (PCa). Metabolomics can be a very useful approach in biomarker discovery because metabolites are an important read-out of the disease when characterized in biological samples. We aimed to determine a metabolomic signature which can accurately distinguish men with clinically significant PCa from those affected by benign prostatic hyperplasia (BPH). METHODS We first performed untargeted metabolomics using ultrahigh-performance liquid chromatography tandem mass spectrometry on expressed prostatic secretion urine (EPS-urine) from 25 patients affected by BPH and 25 men with clinically significant PCa (defined as Gleason score ≥ 3 + 4). Diagnosis was histologically confirmed after surgical treatment. The EPS-urine metabolomic approach was then applied to a larger, prospective cohort of 92 consecutive patients undergoing multiparametric magnetic resonance imaging for clinical suspicion of PCa prior to biopsy. RESULTS We established a novel metabolomic signature capable of accurately distinguishing PCa from benign tissue. A metabolomic signature was associated with clinically significant PCa in all subgroups of the Prostate Imaging Reporting and Data System (PI-RADS) classification (100% and 89.13% of accuracy when the PI-RADS was in range of 1-2 and 4-5, respectively, and 87.50% in the more critical cases when the PI-RADS was 3). CONCLUSIONS A combination of metabolites and clinical variables can effectively help in identifying PCa patients that might be overlooked by current imaging technologies. Metabolites from EPS-urine should help in defining the diagnostic pathway of PCa, thus improving PCa detection and decreasing the number of unnecessary prostate biopsies.
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Affiliation(s)
- Denise Drago
- ProMeFa, Proteomics and Metabolomics Facility, Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Annapaola Andolfo
- ProMeFa, Proteomics and Metabolomics Facility, Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Ettore Mosca
- Institute of Biomedical Technologies, National Research Council (CNR), Milan 20090, Italy
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council (CNR), Milan 20090, Italy
| | - Luigi Nocera
- Department of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Vito Cucchiara
- Department of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Matteo Bellone
- Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Francesco Montorsi
- Department of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
| | - Alberto Briganti
- Department of Urology and Division of Experimental Oncology, Urological Research Institute (URI), IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
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20
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Hyötyläinen T. Analytical challenges in human exposome analysis with focus on environmental analysis combined with metabolomics. J Sep Sci 2021; 44:1769-1787. [PMID: 33650238 DOI: 10.1002/jssc.202001263] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/23/2021] [Accepted: 02/23/2021] [Indexed: 12/19/2022]
Abstract
Environmental factors, such as chemical exposures, are likely to play a crucial role in the development of several human chronic diseases. However, how the specific exposures contribute to the onset and progress of various diseases is still poorly understood. In part, this is because comprehensive characterization of the chemical exposome is a highly challenging task, both due to its complex dynamic nature as well as due to the analytical challenges. Herein, the analytical challenges in the field of exposome research are reviewed, with specific emphasis on the sampling, sample preparation, and analysis, as well as challenges in the compound identification. The primary focus is on the human chemical exposome, that is, exposures to mixtures of environmental chemicals and its impact on human metabolome. In order to highlight the recent progress in the exposome research in relation to human health and disease, selected examples of human exposome studies are presented.
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Affiliation(s)
- Tuulia Hyötyläinen
- MTM Research Centre, School of Science and Technology, Örebro University, Örebro, Sweden
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21
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Esterhuizen K, Lindeque JZ, Mason S, van der Westhuizen FH, Rodenburg RJ, de Laat P, Smeitink JAM, Janssen MCH, Louw R. One mutation, three phenotypes: novel metabolic insights on MELAS, MIDD and myopathy caused by the m.3243A > G mutation. Metabolomics 2021; 17:10. [PMID: 33438095 DOI: 10.1007/s11306-020-01769-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/31/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION The m.3243A > G mitochondrial DNA mutation is one of the most common mitochondrial disease-causing mutations, with a carrier rate as high as 1:400. This point mutation affects the MT-TL1 gene, ultimately affecting the oxidative phosphorylation system and the cell's energy production. Strikingly, the m.3243A > G mutation is associated with different phenotypes, including mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS), maternally inherited diabetes and deafness (MIDD) and myopathy. OBJECTIVES We investigated urine metabolomes of MELAS, MIDD and myopathy patients in order to identify affected metabolic pathways and possible treatment options. METHODS A multiplatform metabolomics approach was used to comprehensively analyze the metabolome and compare metabolic profiles of different phenotypes caused by the m.3243A > G mutation. Our analytical array consisted of NMR spectroscopy, LC-MS/MS and GC-TOF-MS. RESULTS The investigation revealed phenotypic specific metabolic perturbations, as well as metabolic similarities between the different phenotypes. We show that glucose metabolism is highly disturbed in the MIDD phenotype, but not in MELAS or myopathy, remodeled fatty acid oxidation is characteristic of the MELAS patients, while one-carbon metabolism is strongly modified in both MELAS and MIDD, but not in the myopathy group. Lastly we identified increased creatine in the urine of the myopathy patients, but not in MELAS or MIDD. CONCLUSION We conclude by giving novel insight on the phenotypes of the m.3243A > G mutation from a metabolomics point of view. Directives are also given for future investigations that could lead to better treatment options for patients suffering from this debilitating disease.
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Affiliation(s)
- Karien Esterhuizen
- Mitochondria Research Laboratory, Human Metabolomics, North-West University, Potchefstroom, South Africa
| | - J Zander Lindeque
- Mitochondria Research Laboratory, Human Metabolomics, North-West University, Potchefstroom, South Africa
| | - Shayne Mason
- Mitochondria Research Laboratory, Human Metabolomics, North-West University, Potchefstroom, South Africa
| | | | - Richard J Rodenburg
- Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Paul de Laat
- Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Jan A M Smeitink
- Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Mirian C H Janssen
- Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
- Department of Internal Medicine, Radboud Center for Mitochondrial Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Roan Louw
- Mitochondria Research Laboratory, Human Metabolomics, North-West University, Potchefstroom, South Africa.
- Human Metabolomics, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom, South Africa.
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22
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Mervant L, Tremblay-Franco M, Jamin EL, Kesse-Guyot E, Galan P, Martin JF, Guéraud F, Debrauwer L. Osmolality-based normalization enhances statistical discrimination of untargeted metabolomic urine analysis: results from a comparative study. Metabolomics 2021; 17:2. [PMID: 33389209 DOI: 10.1007/s11306-020-01758-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 12/09/2020] [Indexed: 02/06/2023]
Abstract
INTRODUCTION Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods. OBJECTIVES To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study. METHODS We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models. RESULTS Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination. CONCLUSIONS This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples. TRIAL REGISTRATION NUMBER NCT03335644.
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Affiliation(s)
- Loïc Mervant
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France
- Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Marie Tremblay-Franco
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France.
- Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
| | - Emilien L Jamin
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France
- Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Emmanuelle Kesse-Guyot
- Sorbonne Paris Nord University, Inserm, INRAE, Cnam, Nutritional Epidemiology, Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), 93017, Bobigny, France
| | - Pilar Galan
- Sorbonne Paris Nord University, Inserm, INRAE, Cnam, Nutritional Epidemiology, Research Team (EREN), Epidemiology and Statistics Research Center - University of Paris (CRESS), 93017, Bobigny, France
| | - Jean-François Martin
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France
- Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Françoise Guéraud
- Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Laurent Debrauwer
- Metatoul-AXIOM Platform, MetaboHUB, Toxalim, INRAE, Toulouse, France
- Toxalim, Toulouse University, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
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23
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Sun J, Charron CS, Liu Z, Novotny JA, Harrington PDB, Ross SA, Seifried HE, Chen P. Study on Human Urinary Metabolic Profiles after Consumption of Kale and Daikon Radish using a High-resolution Mass Spectrometry-Based Non-targeted and Targeted Metabolomic Approach. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:14307-14318. [PMID: 33205650 DOI: 10.1021/acs.jafc.0c05184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In the present study, urine samples were collected from healthy human volunteers to determine the metabolic fates of phenolic compounds and glucosinolates after a single meal of kale and daikon radish. The major glucosinolates and phenolic compounds in kale and daikon radish were measured. The urinary metabolome after feeding at different time periods was investigated. A targeted metabolite analysis method was developed based on the known metabolic pathways for glucosinolates and phenolic compounds. Using a targeted approach, a total of 18 metabolites were found in urine: 4 from phenolic compounds and 14 from glucosinolates. Among these metabolites, 4-methylsulfinyl-3-butenyl isothiocyanate, 4-methylsulfinyl-3-butenyl isothiocyanate-cysteine, and 4-methylsulfinyl-3-butenylglucosinolate-N-acetyl cysteine were reported for the first time in human urine. The combination of non-targeted and targeted metabolomic approaches can gain a full metabolite profile for human dietary intervention studies.
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Affiliation(s)
- Jianghao Sun
- U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center, Methods and Application of Food Composition Laboratory, Beltsville, Maryland 20705, United States
- Department of Chemistry and Biochemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701, United States
| | - Craig S Charron
- U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center, Food Components and Health Laboratory, Beltsville, Maryland 20705, United States
| | - Zhihao Liu
- U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center, Methods and Application of Food Composition Laboratory, Beltsville, Maryland 20705, United States
| | - Janet A Novotny
- U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center, Food Components and Health Laboratory, Beltsville, Maryland 20705, United States
| | - Peter de B Harrington
- Department of Chemistry and Biochemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701, United States
| | - Sharon A Ross
- National Institutes of Health, National Cancer Institute, Division of Cancer Protection Prevention, Rockville, Maryland 20852, United States
| | - Harold E Seifried
- National Institutes of Health, National Cancer Institute, Division of Cancer Protection Prevention, Rockville, Maryland 20852, United States
| | - Pei Chen
- U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human Nutrition Research Center, Methods and Application of Food Composition Laboratory, Beltsville, Maryland 20705, United States
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24
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Nam SL, de la Mata AP, Dias RP, Harynuk JJ. Towards Standardization of Data Normalization Strategies to Improve Urinary Metabolomics Studies by GC×GC-TOFMS. Metabolites 2020; 10:metabo10090376. [PMID: 32961779 PMCID: PMC7570207 DOI: 10.3390/metabo10090376] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022] Open
Abstract
Urine is a popular biofluid for metabolomics studies due to its simple, non-invasive collection and its availability in large quantities, permitting frequent sampling, replicate analyses, and sample banking. The biggest disadvantage with using urine is that it exhibits significant variability in concentration and composition within an individual over relatively short periods of time (arising from various external factors and internal processes regulating the body’s water and solute content). In treating the data from urinary metabolomics studies, one must account for the natural variability of urine concentrations to avoid erroneous data interpretation. Amongst various proposed approaches to account for broadly varying urine sample concentrations, normalization to creatinine has been widely accepted and is most commonly used. MS total useful signal (MSTUS) is another normalization method that has been recently reported for mass spectrometry (MS)-based metabolomics studies. Herein, we explored total useful peak area (TUPA), a modification of MSTUS that is applicable to GC×GC-TOFMS (and data from other separations platforms), for sample normalization in urinary metabolomics studies. Performance of TUPA was compared to the two most common normalization approaches, creatinine adjustment and Total Peak Area (TPA) normalization. Each normalized dataset was evaluated using Principal Component Analysis (PCA). The results showed that TUPA outperformed alternative normalization methods to overcome urine concentration variability. Results also conclusively demonstrate the risks in normalizing data to creatinine.
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Affiliation(s)
| | | | | | - James J Harynuk
- Correspondence: ; Tel.: +1-780-492-8303; Fax: +1-780-492-8231
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25
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Drabińska N, Młynarz P, de Lacy Costello B, Jones P, Mielko K, Mielnik J, Persad R, Ratcliffe NM. An Optimization of Liquid-Liquid Extraction of Urinary Volatile and Semi-Volatile Compounds and Its Application for Gas Chromatography-Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy. Molecules 2020; 25:E3651. [PMID: 32796601 PMCID: PMC7463579 DOI: 10.3390/molecules25163651] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 02/07/2023] Open
Abstract
Urinary volatile compounds (VCs) have been recently assessed for disease diagnoses. They belong to very diverse chemical classes, and they are characterized by different volatilities, polarities and concentrations, complicating their analysis via a single analytical procedure. There remains a need for better, lower-cost methods for VC biomarker discovery. Thus, there is a strong need for alternative methods, enabling the detection of a broader range of VCs. Therefore, the main aim of this study was to optimize a simple and reliable liquid-liquid extraction (LLE) procedure for the analysis of VCs in urine using gas chromatography-mass spectrometry (GC-MS), in order to obtain the maximum number of responses. Extraction parameters such as pH, type of solvent and ionic strength were optimized. Moreover, the same extracts were analyzed using Proton Nuclear Magnetic Resonance Spectroscopy (1H-NMR), to evaluate the applicability of a single urine extraction for multiplatform purposes. After the evaluation of experimental conditions, an LLE protocol using 2 mL of urine in the presence of 2 mL of 1 M sulfuric acid and sodium sulphate extracted with dichloromethane was found to be optimal. The optimized method was validated with the external standards and was found to be precise and linear, and allowed for detection of >400 peaks in a single run present in at least 50% of six samples-considerably more than the number of peaks detected by solid-phase microextracton fiber pre-concentration-GC-MS (328 ± 6 vs. 234 ± 4). 1H-NMR spectroscopy of the polar and non-polar extracts extended the range to >40 more (mainly low volatility compounds) metabolites (non-destructively), the majority of which were different from GC-MS. The more peaks detectable, the greater the opportunity of assessing a fingerprint of several compounds to aid biomarker discovery. In summary, we have successfully demonstrated the potential of LLE as a cheap and simple alternative for the analysis of VCs in urine, and for the first time the applicability of a single urine solvent extraction procedure for detecting a wide range of analytes using both GC-MS and 1H-NMR analysis to enhance putative biomarker detection. The proposed method will simplify the transport between laboratories and storage of samples, as compared to intact urine samples.
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Affiliation(s)
- Natalia Drabińska
- Institute of Animal Reproduction and Food Research of Polish Academy of Sciences, 10 Tuwima Str., 10-748 Olsztyn, Poland
- Institute of Biosensor Technology, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK;
| | - Piotr Młynarz
- Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wroclaw University of Science and Technology, 27 Wybrzeże Stanisława Wyspianskiego, 50-370 Wroclaw, Poland; (P.M.); (K.M.); (J.M.)
| | - Ben de Lacy Costello
- Institute of Biosensor Technology, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK;
| | | | - Karolina Mielko
- Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wroclaw University of Science and Technology, 27 Wybrzeże Stanisława Wyspianskiego, 50-370 Wroclaw, Poland; (P.M.); (K.M.); (J.M.)
| | - Justyna Mielnik
- Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wroclaw University of Science and Technology, 27 Wybrzeże Stanisława Wyspianskiego, 50-370 Wroclaw, Poland; (P.M.); (K.M.); (J.M.)
| | - Raj Persad
- Bristol Urological Institute, Southmead Hospital, Bristol BS10 5BN, UK;
| | - Norman Mark Ratcliffe
- Institute of Biosensor Technology, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK;
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26
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Liebal UW, Phan ANT, Sudhakar M, Raman K, Blank LM. Machine Learning Applications for Mass Spectrometry-Based Metabolomics. Metabolites 2020; 10:E243. [PMID: 32545768 PMCID: PMC7345470 DOI: 10.3390/metabo10060243] [Citation(s) in RCA: 143] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/11/2020] [Indexed: 12/20/2022] Open
Abstract
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.
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Affiliation(s)
- Ulf W. Liebal
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| | - An N. T. Phan
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| | - Malvika Sudhakar
- Department of Biotechnology, Bhupat and Juoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India; (M.S.); (K.R.)
- Initiative for Biological Systems Engineering, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Juoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India; (M.S.); (K.R.)
- Initiative for Biological Systems Engineering, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Lars M. Blank
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
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27
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Giallourou N, Fardus-Reid F, Panic G, Veselkov K, McCormick BJJ, Olortegui MP, Ahmed T, Mduma E, Yori PP, Mahfuz M, Svensen E, Ahmed MMM, Colston JM, Kosek MN, Swann JR. Metabolic maturation in the first 2 years of life in resource-constrained settings and its association with postnatal growths. SCIENCE ADVANCES 2020; 6:eaay5969. [PMID: 32284996 PMCID: PMC7141821 DOI: 10.1126/sciadv.aay5969] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 01/14/2020] [Indexed: 05/10/2023]
Abstract
Malnutrition continues to affect the growth and development of millions of children worldwide, and chronic undernutrition has proven to be largely refractory to interventions. Improved understanding of metabolic development in infancy and how it differs in growth-constrained children may provide insights to inform more timely, targeted, and effective interventions. Here, the metabolome of healthy infants was compared to that of growth-constrained infants from three continents over the first 2 years of life to identify metabolic signatures of aging. Predictive models demonstrated that growth-constrained children lag in their metabolic maturity relative to their healthier peers and that metabolic maturity can predict growth 6 months into the future. Our results provide a metabolic framework from which future nutritional programs may be more precisely constructed and evaluated.
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Affiliation(s)
- N. Giallourou
- Division of Integrative Systems Medicine and Digestive Diseases, Imperial College London, London, UK
| | - F. Fardus-Reid
- Division of Integrative Systems Medicine and Digestive Diseases, Imperial College London, London, UK
| | - G. Panic
- Division of Integrative Systems Medicine and Digestive Diseases, Imperial College London, London, UK
| | - K. Veselkov
- Division of Integrative Systems Medicine and Digestive Diseases, Imperial College London, London, UK
| | | | - M. P. Olortegui
- Asociación Benéfica PRISMA, Unidad de Investigación Biomedica, Iquitos, Peru
| | - T. Ahmed
- International Center for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - E. Mduma
- Haydom Global Health Institute, Haydom, Tanzania
| | - P. P. Yori
- Asociación Benéfica PRISMA, Unidad de Investigación Biomedica, Iquitos, Peru
- Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA
| | - M. Mahfuz
- International Center for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - E. Svensen
- Haydom Global Health Institute, Haydom, Tanzania
- Haukeland University Hospital, Bergen, Norway
| | - M. M. M. Ahmed
- International Center for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - J. M. Colston
- Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - M. N. Kosek
- Asociación Benéfica PRISMA, Unidad de Investigación Biomedica, Iquitos, Peru
- Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, USA
- Corresponding author.
| | - J. R. Swann
- Division of Integrative Systems Medicine and Digestive Diseases, Imperial College London, London, UK
- School of Human Development and Health, Faculty of Medicine, University of Southampton, UK
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