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Cuevas-Delgado P, Warmuzińska N, Łuczykowski K, Bojko B, Barbas C. Exploring sample treatment strategies for untargeted metabolomics: A comparative study of solid phase microextraction (SPME) and homogenization with solid-liquid extraction (SLE) in renal tissue. Anal Chim Acta 2024; 1312:342758. [PMID: 38834268 DOI: 10.1016/j.aca.2024.342758] [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: 01/08/2024] [Revised: 05/06/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The selection of the sample treatment strategy is a crucial step in the metabolomics workflow. Solid phase microextraction (SPME) is a sample processing methodology with great potential for use in untargeted metabolomics of tissue samples. However, its utilization is not as widespread as other standard protocols involving steps of tissue collection, metabolism quenching, homogenization, and extraction of metabolites by solvents. Since SPME allows us to perform all these steps in one action in tissue samples, in addition to other advantages, it is necessary to know whether this methodology produces similar or comparable metabolome and lipidome coverage and performance to classical methods. RESULTS SPME and homogenization with solid-liquid extraction (Homo-SLE) sample treatment methods were applied to healthy murine kidney tissue, followed by comprehensive metabolomics and lipidomics analyses. In addition, it has been tested whether freezing and storage of the tissue causes alterations in the renal metabolome and lipidome, so the analyses were performed on fresh and frozen tissue samples Lipidomics analysis revealed the exclusive presence of different structural membrane and intracellular lipids in the Homo-SLE group. Conversely, all annotated metabolites were detected in both groups. Notably, the freezing of the sample mainly causes a decrease in the levels of most lipid species and an increase in metabolites such as amino acids, purines, and pyrimidines. These alterations are principally detected in a statistically significant way by SPME methodology. Finally, the samples of both methodologies show a positive correlation in all the analyses. SIGNIFICANCE These results demonstrate that in SPME processing, as long as the fundamentals of non-exhaustive extraction in a pre-equilibrium kinetic regime, extraction in a tissue localized area, the chemistry of the fiber coating and non-homogenization of the tissue are taken into account, is an excellent method to use in kidney tissue metabolomics; since this methodology presents an easy-to-use, efficient, and less invasive approach that simplifies the different sample processing steps.
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Affiliation(s)
- Paula Cuevas-Delgado
- Centre for Metabolomics and Bioanalysis (CEMBIO), School of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
| | - Natalia Warmuzińska
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Kamil Łuczykowski
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Barbara Bojko
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), School of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain.
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Arshad U, Zimpel R, Husnain A, Poindexter MB, Santos JEP. Effect of rumen-protected choline on fat digestibility and lymph metabolome in dairy cows. J Anim Physiol Anim Nutr (Berl) 2024; 108:950-964. [PMID: 38379267 DOI: 10.1111/jpn.13943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 12/08/2023] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Objectives were to determine the effects of supplementing rumen-protected choline (RPC) from an established source with low (L, 28.8%) or a prototype with less lipid coating protection and high (H, 60.0%) concentrations of choline chloride on digestibility of fat and supra-mammary lymph metabolome in feed-restricted cows. Pregnant, nonlactating Holstein cows (n = 33; 11/treatment) at mean (±standard deviation) 231 ± 4.7 days of gestation were blocked by body condition (4.23 ± 0.47) and assigned to receive 0 (CON) or 25.8 g/d of choline ion from L (L25.8) or H (H25.8). Cows were adapted to the diet and then fed-restricted to 42% of the net energy of lactation required for maintenance and pregnancy for 9 days. Intake of metabolizable methionine was maintained at 19 g/d. On Day 9, cows were fed 450 g of saturated fatty acids (SFA), and feces and blood were sampled continuously for 24 h. Supra-mammary lymph was sampled 6 h after feeding SFA and metabolome was characterized. Feeding RPC increased digestibility of fat (CON = 80.4 vs. RPC = 86.0 ± 1.9%) and reduced the concentration of haptoglobin in serum (CON = 174 vs. RPC = 77 ± 14 µg/ml) independent of source of RPC fed. Feeding RPC increased the concentrations of triacylglycerol in serum (CON = 15.1 vs. RPC = 17.8 ± 1.9 mg/dl) in feed-restricted cows after feeding SFA, and the increment tended to be greater for cows fed H25.8 than L25.8. Supplementing RPC tended to increase the concentrations of triacylglycerol (CON = 11.4 vs. RPC = 15.8 ± 3.4 mg/dl) in supra-mammary lymph. Feeding RPC increased the concentration of choline and affected the concentrations of analytes involved in metabolic pathways associated with amino acid metabolism and biosynthesis of phospholipids in lymph compared with CON. Feeding RPC, independent of source used, increased fat digestibility with some changes in lymph metabolome in cows under negative nutrient balance.
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Affiliation(s)
- Usman Arshad
- Department of Animal Sciences, DH Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, USA
| | - Roney Zimpel
- Department of Animal Sciences, DH Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, USA
| | - Ali Husnain
- Department of Animal Sciences, DH Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, USA
| | - Michael B Poindexter
- Department of Animal Sciences, DH Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, USA
| | - José E P Santos
- Department of Animal Sciences, DH Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville, USA
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Frias MA, Pagano S, Bararpour N, Sidibé J, Kamau F, Fétaud-Lapierre V, Hudson P, Thomas A, Lecour S, Strijdom H, Vuilleumier N. People living with HIV display increased anti-apolipoprotein A1 auto-antibodies, inflammation, and kynurenine metabolites: a case-control study. Front Cardiovasc Med 2024; 11:1343361. [PMID: 38414919 PMCID: PMC10896987 DOI: 10.3389/fcvm.2024.1343361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/25/2024] [Indexed: 02/29/2024] Open
Abstract
Objective This study aimed to study the relationship between auto-antibodies against apolipoprotein A1 (anti-apoA1 IgG), human immunodeficiency virus (HIV) infection, anti-retroviral therapy (ART), and the tryptophan pathways in HIV-related cardiovascular disease. Design This case-control study conducted in South Africa consisted of control volunteers (n = 50), people living with HIV (PLWH) on ART (n = 50), and untreated PLWH (n = 44). Cardiovascular risk scores were determined, vascular measures were performed, and an extensive biochemical characterisation (routine, metabolomic, and inflammatory systemic profiles) was performed. Methods Anti-apoA1 IgG levels were assessed by an in-house ELISA. Inflammatory biomarkers were measured with the Meso Scale Discovery® platform, and kynurenine pathway metabolites were assessed using targeted metabolomic profiling conducted by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS). Results Cardiovascular risk scores and vascular measures exhibited similarities across the three groups, while important differences were observed in systemic inflammatory and tryptophan pathways. Anti-apoA1 IgG seropositivity rates were 15%, 40%, and 70% in control volunteers, PLWH ART-treated, and PLWH ART-naïve, respectively. Circulating anti-apoA1 IgG levels were significantly negatively associated with CD4+ cell counts and positively associated with viremia and pro-inflammatory biomarkers (IFNγ, TNFα, MIPα, ICAM-1, VCAM-1). While circulating anti-apoA1 IgG levels were associated with increased levels of kynurenine in both control volunteers and PLWH, the kynurenine/tryptophan ratio was significantly increased in PLWH ART-treated. Conclusion HIV infection increases the humoral response against apoA1, which is associated with established HIV severity criteria and kynurenine pathway activation.
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Affiliation(s)
- Miguel A. Frias
- Division of Laboratory Medicine, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
- Department of Medical Specialties, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Sabrina Pagano
- Division of Laboratory Medicine, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
- Department of Medical Specialties, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nasim Bararpour
- Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- Department of Genetics, Stanford University, Stanford, CA, United States
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, United States
| | - Jonathan Sidibé
- Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Festus Kamau
- Centre for Cardiometabolic Research in Africa, Division of Medical Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Vanessa Fétaud-Lapierre
- Division of Laboratory Medicine, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
- Department of Medical Specialties, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Peter Hudson
- Cape Heart Institute, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Aurélien Thomas
- Faculty Unit of Toxicology, CURML, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
- Unit of Forensic Toxicology and Chemistry, CURML, Lausanne and Geneva University Hospitals, Lausanne, Geneva, Switzerland
| | - Sandrine Lecour
- Cape Heart Institute, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Hans Strijdom
- Centre for Cardiometabolic Research in Africa, Division of Medical Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Nicolas Vuilleumier
- Division of Laboratory Medicine, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland
- Department of Medical Specialties, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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Klont F, Nijdam FB, Bakker SJL, Keski-Rahkonen P, Hopfgartner G, Investigators T. High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification. Analyst 2024; 149:1061-1067. [PMID: 38251754 PMCID: PMC10866140 DOI: 10.1039/d3an01874a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 12/08/2023] [Indexed: 01/23/2024]
Abstract
Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.
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Affiliation(s)
- Frank Klont
- Unit of PharmacoTherapy, -Epidemiology & -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Quai Ernest Ansermet 24, 1211 Geneva, Switzerland
| | - Fleur B Nijdam
- Unit of PharmacoTherapy, -Epidemiology & -Economics, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
| | - Stephan J L Bakker
- Department of Internal Medicine, Division of Nephrology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Avenue Tony Garnier 25, 69007 Lyon, France
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Quai Ernest Ansermet 24, 1211 Geneva, Switzerland
| | - TransplantLines Investigators
- Group of Authors on Behalf of the Transplant Lines Biobank and Cohort Study, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands
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Wanichthanarak K, In-on A, Fan S, Fiehn O, Wangwiwatsin A, Khoomrung S. Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0. Gigascience 2024; 13:giae005. [PMID: 38488666 PMCID: PMC10941642 DOI: 10.1093/gigascience/giae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/22/2023] [Accepted: 02/02/2024] [Indexed: 03/18/2024] Open
Abstract
In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.
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Affiliation(s)
- Kwanjeera Wanichthanarak
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Ammarin In-on
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sili Fan
- Department of Biostatistics, University of California Davis, Davis, CA 95616, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis Genome Center, Davis, CA 95616, USA
| | - Arporn Wangwiwatsin
- Department of Systems Biosciences and Computational Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Sakda Khoomrung
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10700, Thailand
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Cuevas-Delgado P, Miguel V, Rupérez FJ, Lamas S, Barbas C. Impact of renal tubular Cpt1a overexpression on the kidney metabolome in the folic acid-induced fibrosis mouse model. Front Mol Biosci 2023; 10:1161036. [PMID: 37377862 PMCID: PMC10291237 DOI: 10.3389/fmolb.2023.1161036] [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: 02/07/2023] [Accepted: 04/24/2023] [Indexed: 06/29/2023] Open
Abstract
Background: Chronic kidney disease (CKD) is characterized by the progressive and irreversible deterioration of kidney function and structure with the appearance of renal fibrosis. A significant decrease in mitochondrial metabolism, specifically a reduction in fatty acid oxidation (FAO) in tubular cells, is observed in tubulointerstitial fibrosis, whereas FAO enhancement provides protection. Untargeted metabolomics offers the potential to provide a comprehensive analysis of the renal metabolome in the context of kidney injury. Methodology: Renal tissue from a carnitine palmitoyl transferase 1a (Cpt1a) overexpressing mouse model, which displays enhanced FAO in the renal tubule, subjected to folic acid nephropathy (FAN) was studied through a multiplatform untargeted metabolomics approach based on LC-MS, CE-MS and GC-MS analysis to achieve the highest coverage of the metabolome and lipidome affected by fibrosis. The expression of genes related to the biochemical routes showing significant changes was also evaluated. Results: By combining different tools for signal processing, statistical analysis and feature annotation, we were able to identify variations in 194 metabolites and lipids involved in many metabolic routes: TCA cycle, polyamines, one-carbon metabolism, amino acid metabolism, purine metabolism, FAO, glycerolipids and glycerophospholipids synthesis and degradation, glycosphingolipids interconversion, and sterol metabolism. We found several metabolites strongly altered by FAN, with no reversion induced by Cpt1a overexpression (v.g. citric acid), whereas other metabolites were influenced by CPT1A-induced FAO (v.g. glycine-betaine). Conclusion: It was implemented a successful multiplatform metabolomics approach for renal tissue analysis. Profound metabolic changes accompany CKD-associated fibrosis, some associated with tubular FAO failure. These results highlight the importance of addressing the crosstalk between metabolism and fibrosis when undertaking studies attempting to elucidate the mechanism of CKD progression.
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Affiliation(s)
- Paula Cuevas-Delgado
- Centre for Metabolomics and Bioanalysis (CEMBIO), School of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
| | - Verónica Miguel
- Program of Physiological and Pathological Processes, Centro de Biología Molecular “Severo Ochoa” (CBMSO, CSIC-UAM), Madrid, Spain
| | - Francisco J. Rupérez
- Centre for Metabolomics and Bioanalysis (CEMBIO), School of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
| | - Santiago Lamas
- Program of Physiological and Pathological Processes, Centro de Biología Molecular “Severo Ochoa” (CBMSO, CSIC-UAM), Madrid, Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), School of Pharmacy, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain
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Riquelme G, Bortolotto EE, Dombald M, Monge ME. Model-driven data curation pipeline for LC-MS-based untargeted metabolomics. Metabolomics 2023; 19:15. [PMID: 36856823 DOI: 10.1007/s11306-023-01976-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 03/02/2023]
Abstract
INTRODUCTION There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing. OBJECTIVES The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review. METHODS Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package. RESULTS The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations. CONCLUSION The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.
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Affiliation(s)
- Gabriel Riquelme
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina
- Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Ciudad de Buenos Aires, Argentina
| | - Emmanuel Ezequiel Bortolotto
- Laboratorio Central, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, C1199, Ciudad de Buenos Aires, Argentina
| | - Matías Dombald
- Laboratorio Central, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, C1199, Ciudad de Buenos Aires, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina.
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Ten-Doménech I, Cascant-Vilaplana MM, Navarro-Esteve V, Felderer B, Moreno-Giménez A, Rienda I, Gormaz M, Moreno-Torres M, Pérez-Guaita D, Quintás G, Kuligowski J. Metabolomic Diversity of Human Milk Cells over the Course of Lactation-A Preliminary Study. Nutrients 2023; 15:nu15051100. [PMID: 36904100 PMCID: PMC10005050 DOI: 10.3390/nu15051100] [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: 01/09/2023] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Human milk (HM) is a complex biofluid containing a wide cell variety including epithelial cells and leukocytes. However, the cellular compositions and their phenotypic properties over the course of lactation are poorly understood. The aim of this preliminary study was to characterize the cellular metabolome of HM over the course of lactation. Cells were isolated via centrifugation and the cellular fraction was characterized via cytomorphology and immunocytochemical staining. Cell metabolites were extracted and analyzed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-QqTOF-MS) in the positive and negative electrospray ionization modes. Immunocytochemical analysis revealed a high variability of the number of detected cells with relative median abundances of 98% of glandular epithelial cells, 1% of leukocytes, and 1% of keratinocytes. Significant correlations between the milk postnatal age with percentage of epithelial cells and leukocytes, and with total cell count were observed. Results from the Hierarchical Cluster Analysis of immunocytochemical profiles were very similar to those observed in the analysis of the metabolomic profiles. In addition, metabolic pathway analysis showed alterations in seven metabolic pathways correlating with postnatal age. This work paves the way for future investigations on changes in the metabolomic fraction of the cellular compartment of HM.
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Affiliation(s)
- Isabel Ten-Doménech
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
| | - Mari Merce Cascant-Vilaplana
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
| | - Víctor Navarro-Esteve
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain
| | - Birgit Felderer
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
- Master Program Biotechnical Processes, Austrian Biotech University of Applied Sciences, Konrad Lorenz-Strasse 10, 3430 Tulln, Austria
| | - Alba Moreno-Giménez
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
| | - Iván Rienda
- Servicio de Anatomía Patológica, University & Polytechnic Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
| | - María Gormaz
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
- Division of Neonatology, University & Polytechnic Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
| | - Marta Moreno-Torres
- Unidad de Hepatología Experimental y Trasplante Hepático, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
- Department of Biochemistry and Molecular Biology, University of Valencia, C/Blasco Ibáñez 15, 46010 Valencia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28220 Madrid, Spain
| | - David Pérez-Guaita
- Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain
| | - Guillermo Quintás
- Health and Biomedicine, Leitat Technological Center, Carrer de la Innovació, 2, 08225 Terrassa, Spain
| | - Julia Kuligowski
- Neonatal Research Group, Health Research Institute Hospital La Fe, Avda Fernando Abril Martorell 106, 46026 Valencia, Spain
- Correspondence: ; Tel.: +34-96-1246661
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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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10
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Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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11
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Liu Z, Zhang M, Chen P, Harnly JM, Sun J. Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:11138-11153. [PMID: 35998657 DOI: 10.1021/acs.jafc.2c01878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.
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Affiliation(s)
- Zhihao Liu
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
- Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Mengliang Zhang
- Department of Chemistry, Middle Tennessee State University, Murfreesboro, Tennessee 37132, United States
| | - Pei Chen
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
| | - James M Harnly
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
| | - Jianghao Sun
- United States Department of Agriculture, Methods and Application of Food Composition Laboratory, Beltsville Human Nutrition Research Center, Agricultural Research Service, Beltsville, Maryland 20705, United States
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12
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Traquete F, Luz J, Cordeiro C, Sousa Silva M, Ferreira AEN. Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics. Front Mol Biosci 2022; 9:917911. [PMID: 35936789 PMCID: PMC9353772 DOI: 10.3389/fmolb.2022.917911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/03/2022] [Indexed: 11/16/2022] Open
Abstract
Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.
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13
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Lai X, Guo K, Huang W, Su Y, Chen S, Li Q, Liang K, Gao W, Wang X, Chen Y, Wang H, Lin W, Wei X, Ni W, Lin Y, Jiang D, Cheng YH, Che CM, Ng KM. Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:499-507. [PMID: 34981796 DOI: 10.1039/d1ay01940f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
An increasing amount of evidence has proven that serum metabolites can instantly reflect disease states. Therefore, sensitive and reproducible detection of serum metabolites in a high-throughput manner is urgently needed for clinical diagnosis. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a high-throughput platform for metabolite detection, but it is hindered by significant signal fluctuations because of the "sweet spot" effect of organic matrices. Here, by screening two transformation methods and four normalization techniques to reduce the significant signal fluctuations of the DHB matrix, an integrated MALDI-MS data processing approach combined with machine learning methods was established to reveal metabolic biomarkers of lung cancer. In our study, 13 distinctive features with statistically significant differences (p < 0.001) between 34 lung cancer patients and 26 healthy controls were selected as significant potential biomarkers of lung cancer. 6 out of the 13 distinctive features were identified as intact metabolites. Our results demonstrate the potential for clinical application of MALDI-MS in serum metabolomics for biomarker screening in lung cancer.
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Affiliation(s)
- Xiaopin Lai
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Kunbin Guo
- Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Wei Huang
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Yang Su
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Siyu Chen
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Qiongdan Li
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Kaiqing Liang
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Wenhua Gao
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
| | - Xin Wang
- Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Yuping Chen
- Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Hongbiao Wang
- Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Wen Lin
- Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Xiaolong Wei
- Cancer Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Wenxiu Ni
- Department of Medical Chemistry, Shantou University Medical College, Shantou, Guangdong, 515041, P. R. China
| | - Yan Lin
- The Second Affiliated Hospital of Shantou University Medical College, Guangdong, 515041, P. R. China
| | - Dazhi Jiang
- Department of Computer Science, College of Engineering, Shantou University, Guangdong, 515063, P. R. China
| | - Yu-Hong Cheng
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong S. A. R., P. R. China
| | - Chi-Ming Che
- Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong S. A. R., P. R. China
| | - Kwan-Ming Ng
- Department of Chemistry, Key Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province, Shantou University, Guangdong, 515063, P. R. China
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14
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Ivanova L, Rangel-Huerta OD, Tartor H, Gjessing MC, Dahle MK, Uhlig S. Fish Skin and Gill Mucus: A Source of Metabolites for Non-Invasive Health Monitoring and Research. Metabolites 2021; 12:28. [PMID: 35050150 PMCID: PMC8781917 DOI: 10.3390/metabo12010028] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/16/2021] [Accepted: 12/25/2021] [Indexed: 11/28/2022] Open
Abstract
Mucous membranes such as the gill and skin mucosa in fish protect them against a multitude of environmental factors. At the same time, changes in the molecular composition of mucus may provide valuable information about the interaction of the fish with their environment, as well as their health and welfare. In this study, the metabolite profiles of the plasma, skin and gill mucus of freshwater Atlantic salmon (Salmo salar) were compared using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Several normalization procedures aimed to reduce unwanted variation in the untargeted data were tested. In addition, the basal metabolism of skin and gills, and the impact of the anesthetic benzocaine for euthanisation were studied. For targeted metabolomics, the commercial AbsoluteIDQ p400 HR kit was used to evaluate the potential differences in metabolic composition in epidermal mucus as compared to the plasma. The targeted metabolomics data showed a high level of correlation between different types of biological fluids from the same individual, indicating that mucus metabolite composition could be used for fish health monitoring and research.
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Affiliation(s)
- Lada Ivanova
- Norwegian Veterinary Institute, P.O. Box 64, N-1431 Ås, Norway; (O.D.R.-H.); (H.T.); (M.C.G.); (M.K.D.); (S.U.)
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15
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Araújo AM, Carvalho F, Guedes de Pinho P, Carvalho M. Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions. Metabolites 2021; 11:692. [PMID: 34677407 PMCID: PMC8539642 DOI: 10.3390/metabo11100692] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/17/2022] Open
Abstract
Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.
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Affiliation(s)
- Ana Margarida Araújo
- Associate Laboratory i4HB, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (F.C.); (P.G.d.P.)
- UCIBIO—Applied Molecular Biosciences Unit, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira nº228, 4050-313 Porto, Portugal
| | - Félix Carvalho
- Associate Laboratory i4HB, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (F.C.); (P.G.d.P.)
- UCIBIO—Applied Molecular Biosciences Unit, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira nº228, 4050-313 Porto, Portugal
| | - Paula Guedes de Pinho
- Associate Laboratory i4HB, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (F.C.); (P.G.d.P.)
- UCIBIO—Applied Molecular Biosciences Unit, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira nº228, 4050-313 Porto, Portugal
| | - Márcia Carvalho
- Associate Laboratory i4HB, Institute for Health and Bioeconomy, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; (F.C.); (P.G.d.P.)
- UCIBIO—Applied Molecular Biosciences Unit, REQUIMTE, Laboratory of Toxicology, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira nº228, 4050-313 Porto, Portugal
- FP-I3ID, FP-ENAS, University Fernando Pessoa, Praça 9 de Abril, 349, 4249-004 Porto, Portugal
- Faculty of Health Sciences, University Fernando Pessoa, Rua Carlos da Maia, 296, 4200-150 Porto, Portugal
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16
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Perspectives and challenges in extracellular vesicles untargeted metabolomics analysis. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116382] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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17
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Yang H, Zhang P, Xie M, Luo J, Zhang J, Zhang G, Wang Y, Lin H, Ji Z. Parallel Metabolomic Profiling of Cerebrospinal Fluid, Plasma, and Spinal Cord to Identify Biomarkers for Spinal Cord Injury. J Mol Neurosci 2021; 72:126-135. [PMID: 34498202 PMCID: PMC8755701 DOI: 10.1007/s12031-021-01903-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 08/18/2021] [Indexed: 11/27/2022]
Abstract
Loss of physical and emotional health due to spinal cord injury (SCI) has been rapidly increasing worldwide. Effective evaluation of the severity of SCI is crucial to its prognosis. Herein, we constructed rat models of SCI with four different degrees of injury (sham group, light injury group, moderate injury group, and heavy injury group), using the surgical approach. Cerebrospinal fluid (CSF), plasma, and spinal cord were sampled at the sub-acute spinal cord (72 h post-injury) from each rat. The LC-MS-based metabolic profiling of these samples was performed according to a universal metabolome standard (UMS). The results demonstrated that 130, 104, and 128 metabolites were significantly altered within the CSF, plasma, and spinal cord samples, respectively. Among them, there were four differential metabolites, including uric acid, phosphorycholine, pyridoxine, and guanidoacetic acid, which were commonly identified within the CSF, plasma, and spinal cord samples. Further pathway analysis of these differential metabolites demonstrated a disturbance in the metabolism of glyoxylate and dicarboxylate and glycine, serine, and threonine which were associated with pathophysiologic consequence of spinal cord injury. In particular, phosphorycholine, pyridoxine, and guanidoacetic acid demonstrated a relationship with SCI severity. Thus, they could be utilized as potential metabolite biomarkers for SCI severity assessment.
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Affiliation(s)
- Hua Yang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China
| | - Pengwei Zhang
- MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Institute of Life and Health Engineering, Jinan University, Guangzhou, 510632, China
| | - Min Xie
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China
| | - Jianxian Luo
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China
| | - Jing Zhang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China
| | - Guowei Zhang
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China
| | - Yang Wang
- MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Institute of Life and Health Engineering, Jinan University, Guangzhou, 510632, China.
| | - Hongsheng Lin
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China.
| | - Zhisheng Ji
- Department of Orthopedics, The First Affiliated Hospital of Jinan University, No.601 West Huangpu Avenue, Tianhe, Guangzhou, 510630, China.
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18
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Fernández-Garcia M, Sanchez-Flores A, Gonzalez LM, Barbas C, Rey-Stolle MF, Sevilla E, García A, Montero E. Integration of Functional Genomic, Transcriptomic, and Metabolomic Data to Identify Key Features in Genomic Expression, Metabolites, and Metabolic Pathways of Babesia divergens. Methods Mol Biol 2021; 2369:217-249. [PMID: 34313992 DOI: 10.1007/978-1-0716-1681-9_13] [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] [Indexed: 02/15/2023]
Abstract
Upon invasion of red blood cells (RBCs), the Apicomplexa parasite Babesia divergens remains within the RBC for several hours and reproduces asexually, resulting in infective free merozoites that egress and destroy the host cell. Free merozoites rapidly seek and invade new uninfected RBCs. This repetitive cycle allows B. divergens to build a complex population of intraerythrocytic and extracellular stages in the bloodstream of humans and cattle, thus causing babesiosis. To compare biological aspects between B. divergens stages, including the different nature of their metabolism, could be key to our understanding of pathogenesis. Thus, we are currently assessing differences in the B. divergens metabolism of intra- and extracellular (free merozoites) life stages by the use of an integrative approach combining functional genomic, transcriptomic, differential expression, and metabolomic data acquired from sequencing and various analytical platforms. To our knowledge, this is the first effort to describe, in detail, the experimental procedures and integration of different omics to explore the regulation of the metabolism, invasion and proliferation mechanisms of B. divergens. This integrative approach can be used as a reference to study other Apicomplexa parasites.
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Affiliation(s)
- Miguel Fernández-Garcia
- CEMBIO (Center for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain
| | - Alejandro Sanchez-Flores
- Unidad Universitaria de Secuenciación Masiva y Bioinformática, Instituto de Biotecnología, Cuernavaca, Mexico
| | - Luis Miguel Gonzalez
- Laboratorio de Referencia e Investigación en Parasitología, Centro Nacional de Microbiología, ISCIII Majadahonda, Madrid, Spain
| | - Coral Barbas
- CEMBIO (Center for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain
| | - Mª Fernanda Rey-Stolle
- CEMBIO (Center for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain
| | - Elena Sevilla
- Laboratorio de Referencia e Investigación en Parasitología, Centro Nacional de Microbiología, ISCIII Majadahonda, Madrid, Spain
| | - Antonia García
- CEMBIO (Center for Metabolomics and Bioanalysis), Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain.
| | - Estrella Montero
- Laboratorio de Referencia e Investigación en Parasitología, Centro Nacional de Microbiología, ISCIII Majadahonda, Madrid, Spain.
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19
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Lee DD, Park SJ, Zborek KL, Schwarz MA. A shift from glycolytic and fatty acid derivatives toward one-carbon metabolites in the developing lung during transitions of the early postnatal period. Am J Physiol Lung Cell Mol Physiol 2021; 320:L640-L659. [PMID: 33502935 DOI: 10.1152/ajplung.00417.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
During postnatal lung development, metabolic changes that coincide with stages of alveolar formation are poorly understood. Responding to developmental and environmental factors, metabolic changes can be rapidly and adaptively altered. The objective of the present study was to determine biological and technical determinants of metabolic changes during postnatal lung development. Over 118 metabolic features were identified by liquid chromatography with tandem mass spectrometry (LC-MS/MS, Sciex QTRAP 5500 Triple Quadrupole). Biological determinants of metabolic changes were the transition from the postnatal saccular to alveolar stages and exposure to 85% hyperoxia, an environmental insult. Technical determinants of metabolic identification were brevity and temperature of harvesting, both of which improved metabolic preservation within samples. Multivariate statistical analyses revealed the transition between stages of lung development as the period of major metabolic alteration. Of three distinctive groups that clustered by age, the saccular stage was identified by its enrichment of both glycolytic and fatty acid derivatives. The critical transition between stages of development were denoted by changes in amino acid derivatives. Of the amino acid derivatives that significantly changed, a majority were linked to metabolites of the one-carbon metabolic pathway. The enrichment of one-carbon metabolites was independent of age and environmental insult. Temperature was also found to significantly influence the metabolic levels within the postmortem sampled lung, which underscored the importance of methodology. Collectively, these data support not only distinctive stages of metabolic change but also highlight amino acid metabolism, in particular one-carbon metabolites as metabolic signatures of the early postnatal lung.
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Affiliation(s)
- Daniel D Lee
- Department of Pediatrics, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana.,Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, Indiana.,Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana
| | - Sang Jun Park
- Department of Preprofessional Studies, University of Notre Dame, South Bend, Indiana
| | - Kirsten L Zborek
- Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana
| | - Margaret A Schwarz
- Department of Pediatrics, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana.,Department of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, Indiana.,Department of Cellular and Integrative Physiology, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana
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20
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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21
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Roca M, Alcoriza MI, Garcia-Cañaveras JC, Lahoz A. Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial. Anal Chim Acta 2020; 1147:38-55. [PMID: 33485584 DOI: 10.1016/j.aca.2020.12.025] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022]
Abstract
Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.
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Affiliation(s)
- Marta Roca
- Analytical Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Maria Isabel Alcoriza
- Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Juan Carlos Garcia-Cañaveras
- Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain
| | - Agustín Lahoz
- Analytical Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain; Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain.
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A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows. Metabolites 2020; 10:metabo10100416. [PMID: 33081373 PMCID: PMC7602939 DOI: 10.3390/metabo10100416] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/12/2022] Open
Abstract
Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users.
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Creydt M, Fischer M. Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity. Molecules 2020; 25:E3972. [PMID: 32878155 PMCID: PMC7504784 DOI: 10.3390/molecules25173972] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 08/27/2020] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.
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Affiliation(s)
- Marina Creydt
- Hamburg School of Food Science-Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany;
- Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Markus Fischer
- Hamburg School of Food Science-Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany;
- Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
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