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Sun J, Xia Y. Pretreating and normalizing metabolomics data for statistical analysis. Genes Dis 2024; 11:100979. [PMID: 38299197 PMCID: PMC10827599 DOI: 10.1016/j.gendis.2023.04.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 04/09/2023] [Indexed: 02/02/2024] Open
Abstract
Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.
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Affiliation(s)
- Jun Sun
- Division of Gastroenterology and Hepatology, Department of Medicine, Department of Microbiology/Immunology, UIC Cancer Center, University of Illinois Chicago, Jesse Brown VA Medical Center Chicago (537), Chicago, IL 60612, USA
| | - Yinglin Xia
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA
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2
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Zniber M, Lamminen T, Taimen P, Boström PJ, Huynh TP. 1H-NMR-based urine metabolomics of prostate cancer and benign prostatic hyperplasia. Heliyon 2024; 10:e28949. [PMID: 38617934 PMCID: PMC11015411 DOI: 10.1016/j.heliyon.2024.e28949] [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: 12/30/2023] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/16/2024] Open
Abstract
Background Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are prevalent conditions affecting a significant portion of the male population, particularly with advancing age. Traditional diagnostic methods, such as digital rectal examination (DRE) and prostate-specific antigen (PSA) tests, have limitations in specificity and sensitivity, leading to potential overdiagnosis and unnecessary biopsies. Significance This study explores the effectiveness of 1H NMR urine metabolomics in distinguishing PCa from BPH and in differentiating various PCa grades, presenting a non-invasive diagnostic alternative with the potential to enhance early detection and patient-specific treatment strategies. Results The study demonstrated the capability of 1H NMR urine metabolomics in detecting distinct metabolic profiles between PCa and BPH, as well as among different Gleason grade groups. Notably, this method surpassed the PSA test in distinguishing PCa from BPH. Untargeted metabolomics analysis also revealed several metabolites with varying relative concentrations between PCa and BPH cases, suggesting potential biomarkers for these conditions.
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Affiliation(s)
- Mohammed Zniber
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, Turku, Finland
| | - Tarja Lamminen
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine and FICAN West Cancer Centre, University of Turku and Department of Pathology, Turku University Hospital, Turku, Finland
| | - Peter J. Boström
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Tan-Phat Huynh
- Laboratory of Molecular Science and Engineering, Åbo Akademi University, Turku, Finland
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3
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Mamede L, Fall F, Schoumacher M, Ledoux A, Bugli C, De Tullio P, Quetin-Leclercq J, Govaerts B, Frédérich M. Comparison of extraction methods in vitro Plasmodium falciparum: A 1H NMR and LC-MS joined approach. Biochem Biophys Res Commun 2024; 703:149684. [PMID: 38367514 DOI: 10.1016/j.bbrc.2024.149684] [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: 12/22/2023] [Accepted: 02/12/2024] [Indexed: 02/19/2024]
Abstract
Malaria is a parasitic disease that remains a global concern and the subject of many studies. Metabolomics has emerged as an approach to better comprehend complex pathogens and discover possible drug targets, thus giving new insights that can aid in the development of antimalarial therapies. However, there is no standardized method to extract metabolites from in vitro Plasmodium falciparum intraerythrocytic parasites, the stage that causes malaria. Additionally, most methods are developed with either LC-MS or NMR analysis in mind, and have rarely been evaluated with both tools. In this work, three extraction methods frequently found in the literature were reproduced and samples were analyzed through both LC-MS and 1H NMR, and evaluated in order to reveal which is the most repeatable and consistent through an array of different tools, including chemometrics, peak detection and annotation. The most reliable method in this study proved to be a double extraction with methanol and methanol/water (80:20, v/v). Metabolomic studies in the field should move towards standardization of methodologies and the use of both LC-MS and 1H NMR in order to make data more comparable between studies and facilitate the achievement of biologically interpretable information.
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Affiliation(s)
- Lúcia Mamede
- Laboratory of Pharmacognosy, Center of Interdisciplinary Research on Medicines (CIRM), University of Liège, Belgium
| | - Fanta Fall
- Pharmacognosy Research Group, Louvain Drug Research Institute (LDRI), UCLouvain, Brussels, Belgium
| | - Matthieu Schoumacher
- Laboratory of Pharmaceutical Chemistry, Center of Interdisciplinary Research on Medicines (CIRM), University of Liège, Belgium
| | - Allison Ledoux
- Laboratory of Pharmacognosy, Center of Interdisciplinary Research on Medicines (CIRM), University of Liège, Belgium
| | - Céline Bugli
- Statistical Methodology and Computing Service (SMCS/LIDAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Pascal De Tullio
- Laboratory of Pharmaceutical Chemistry, Center of Interdisciplinary Research on Medicines (CIRM), University of Liège, Belgium
| | - Joëlle Quetin-Leclercq
- Pharmacognosy Research Group, Louvain Drug Research Institute (LDRI), UCLouvain, Brussels, Belgium
| | - Bernadette Govaerts
- Statistical Methodology and Computing Service (SMCS/LIDAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Michel Frédérich
- Laboratory of Pharmacognosy, Center of Interdisciplinary Research on Medicines (CIRM), University of Liège, Belgium.
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4
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da Rocha Junior ER, Porto VA, Crispim AC, Ursulino JS, de Jesus LWO, de Souza Bento E, Santos JCC, de Aquino TM. Assessment of thimerosal effects in sublethal concentrations on zebrafish (Danio rerio) embryos exploring NMR-based metabolomics profile. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2024; 106:104361. [PMID: 38211665 DOI: 10.1016/j.etap.2024.104361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 01/13/2024]
Abstract
Thimerosal, a preservative commonly used in the pharmaceutical and cosmetic industry, has raised concerns regarding its potentially toxic effects as an organic mercury compound. Within this context, using an NMR-based metabolomics profile and chemometric analysis, zebrafish embryos were used as an in vivo model to study the effects of thimerosal in metabolic profiles after exposure to sublethal concentrations of the mercury compound. The thimerosal concentrations of 40 and 80 nM were employed, corresponding to 40% and 80% of the LC50, respectively, for zebrafish embryos. The most significant alterations in the metabolic profile included changes in carbohydrates, amino acids, nucleotides, trimethylamine-N-oxide, ethanolamine, betaine, and ethanol. Furthermore, thimerosal exposure affects various metabolic pathways, impairing the nervous system, disrupting protein metabolism, and potentially causing oxidative damage. Therefore, adopting a metabolomics approach in this investigation provided insights into the potentially implicated metabolic pathways contributing to the deleterious effects of thimerosal in biological systems.
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Affiliation(s)
- Edmilson Rodrigues da Rocha Junior
- Nucleus of Analysis and Research in Nuclear Magnetic Resonance - NAPRMN, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | - Viviane Amaral Porto
- Nucleus of Analysis and Research in Nuclear Magnetic Resonance - NAPRMN, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | - Alessandre Carmo Crispim
- Nucleus of Analysis and Research in Nuclear Magnetic Resonance - NAPRMN, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | - Jeferson Santana Ursulino
- Nucleus of Analysis and Research in Nuclear Magnetic Resonance - NAPRMN, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | | | - Edson de Souza Bento
- Nucleus of Analysis and Research in Nuclear Magnetic Resonance - NAPRMN, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil
| | | | - Thiago Mendonça de Aquino
- Nucleus of Analysis and Research in Nuclear Magnetic Resonance - NAPRMN, Institute of Chemistry and Biotechnology, Federal University of Alagoas, Maceió, AL, Brazil.
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5
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Huau G, Liaubet L, Gourdine JL, Riquet J, Renaudeau D. Multi-tissue metabolic and transcriptomic responses to a short-term heat stress in swine. BMC Genomics 2024; 25:99. [PMID: 38262957 PMCID: PMC10804606 DOI: 10.1186/s12864-024-09999-1] [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: 11/13/2023] [Accepted: 01/09/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Heat stress (HS) is an increasing threat for pig production with a wide range of impacts. When submitted to high temperatures, pigs will use a variety of strategies to alleviate the effect of HS. While systemic adaptations are well known, tissue-specific changes remain poorly understood. In this study, thirty-two pigs were submitted to a 5-day HS at 32 °C. RESULTS Transcriptomic and metabolomic analyses were performed on several tissues. The results revealed differentially expressed genes and metabolites in different tissues. Specifically, 481, 1774, 71, 1572, 17, 164, and 169 genes were differentially expressed in muscle, adipose tissue, liver, blood, thyroid, pituitary, and adrenal glands, respectively. Regulatory glands (pituitary, thyroid, and adrenal) had a lower number of regulated genes, perhaps indicating an earlier sensitivity to HS. In addition, 7, 8, 2, and 8 metabolites were differentially produced in muscle, liver, plasma, and urine, respectively. The study also focused on the oxidative stress pathway in muscle and liver by performing a correlation analysis between genes and metabolites. CONCLUSIONS This study has identified various adaptation mechanisms in swine that enable them to cope with heat stress (HS). These mechanisms include a global decrease in energetic metabolism, as well as changes in metabolic precursors that are linked with protein and lipid catabolism and anabolism. Notably, the adaptation mechanisms differ significantly between regulatory (pituitary, thyroid and adrenal glands) and effector tissues (muscle, adipose tissue, liver and blood). Our findings provide new insights into the comprehension of HS adaptation mechanisms in swine.
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Affiliation(s)
- Guilhem Huau
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, 31326, Castanet Tolosan, France
- PEGASE, INRAE, Institut Agro, 35590, Saint-Gilles, France
| | - Laurence Liaubet
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, 31326, Castanet Tolosan, France
| | | | - Juliette Riquet
- GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, 31326, Castanet Tolosan, France
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Liaubet L, Guilmineau C, Lefort G, Billon Y, Reigner S, Bailly J, Marty-Gasset N, Gress L, Servien R, Bonnet A, Gilbert H, Vialaneix N, Quesnel H. Plasma 1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake. Sci Rep 2023; 13:7127. [PMID: 37130953 PMCID: PMC10154392 DOI: 10.1038/s41598-023-34279-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 04/27/2023] [Indexed: 05/04/2023] Open
Abstract
Together with environmental factors, physiological maturity at birth is a major determinant for neonatal survival and postnatal development in mammalian species. Maturity at birth is the outcome of complex mechanisms of intra-uterine development and maturation during the end of gestation. In pig production, piglet preweaning mortality averages 20% of the litter and thus, maturity is a major welfare and economic concern. Here, we used both targeted and untargeted metabolomic approaches to provide a deeper understanding of the maturity in a model of lines of pigs divergently selected on residual feed intake (RFI), previously shown to have contrasted signs of maturity at birth. Analyses were conducted on plasma metabolome of piglets at birth and integrated with other phenotypic characteristics associated to maturity. We confirmed proline and myo-inositol, previously described for their association with delayed growth, as potential markers of maturity. Urea cycle and energy metabolism were found more regulated in piglets from high and low RFI lines, respectively, suggesting a better thermoregulation ability for the low RFI (with higher feed efficiency) piglets.
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Affiliation(s)
- Laurence Liaubet
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France.
| | | | - Gaëlle Lefort
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
- Université de Toulouse, INRAE, UR MIAT, 31326, Castanet-Tolosan, France
- CNRS, IFCE, INRAE, Université de Tours, PRC, 37380, Nouzilly, France
| | - Yvon Billon
- INRAE, GENESI, 17700, Saint Pierre d'Amilly, France
| | | | - Jean Bailly
- INRAE, GENESI, 17700, Saint Pierre d'Amilly, France
| | | | - Laure Gress
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Rémi Servien
- INRAE, Univ. Montpellier, LBE, 102 Avenue des étangs, 11100, Narbonne, France
| | - Agnès Bonnet
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Hélène Gilbert
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | | | - Hélène Quesnel
- PEGASE, INRAE, Institut Agro, 35590, Saint-Gilles, France
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Liu XF, Lu JJ, Li Y, Yang XY, Qiang JW. Ferrous sulfate reverses cerebral metabolic abnormality induced by minimal hepatic encephalopathy. Metab Brain Dis 2023; 38:1613-1620. [PMID: 36917427 DOI: 10.1007/s11011-023-01198-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 03/03/2023] [Indexed: 03/16/2023]
Abstract
Orally administered ferrous iron was previously reported to significantly improve the cognition and locomotion of patients with minimal hepatic encephalopathy (MHE). However, the metabolic mechanisms of the therapeutic effect of ferrous iron are unknown. In this study, MHE was induced in rats by partial portal vein ligation (PPVL), and was treated with ferrous sulfate. The Morris water maze was used to evaluate the cognitive condition of the rats. The metabolites observed by NMR and validated by liquid chromatography-mass spectrometry were defined as the key affected metabolites. The enzyme activities and trace element contents in the rat brains were also investigated. The Mn content was found to be increased but the ferrous iron content decreased in the cortex and striatum in MHE. Decreased oxoglutarate dehydrogenase activity and increased glutamine synthetase (GS) and pyruvate carboxylase (PC) activity were observed in the cortex of MHE rats. Decreased pyruvate dehydrogenase activity and increased GS and PC activity were observed in the striatum of MHE rats. The levels of BCAAs and taurine were significantly decreased, and the contents of GABA, lactate, arginine, aspartate, carnosine, citrulline, cysteine, glutamate, glutamine, glycine, methionine, ornithine, proline, threonine and tyrosine were significantly increased. These metabolic abnormalities described above were restored after treatment with ferrous sulfate. Pathway enrichment analysis suggested that urea cycle, aspartate metabolism, arginine and proline metabolism, glycine and serine metabolism, and glutamate metabolism were the major metabolic abnormalities in MHE rats, but these processes could be restored and cognitive impairment could be improved by ferrous sulfate administration.
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Affiliation(s)
- Xue-Fei Liu
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Jing-Jing Lu
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Ying Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
| | - Xiu-Ying Yang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Jin-Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
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Wang W, Ma LH, Maletic-Savatic M, Liu Z. NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.01.530642. [PMID: 36909516 PMCID: PMC10002723 DOI: 10.1101/2023.03.01.530642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.
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Affiliation(s)
- Wanli Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Graduate Program of Quantitative & Computational Biosciences, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Li-Hua Ma
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Mirjana Maletic-Savatic
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, 77030, USA
- Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, 77030, USA
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9
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Enespa, Chandra P. Tool and techniques study to plant microbiome current understanding and future needs: an overview. Commun Integr Biol 2022; 15:209-225. [PMID: 35967908 PMCID: PMC9367660 DOI: 10.1080/19420889.2022.2082736] [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] [Indexed: 12/02/2022] Open
Abstract
Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.
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Affiliation(s)
- Enespa
- Department of Plant Pathology, School of Agriculture, SMPDC, University of Lucknow, Lucknow, India
| | - Prem Chandra
- Department of Environmental Microbiology, Babasaheb Bhimrao Ambedkar (A Central) University, Lucknow, India
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Prostko P, Pikkemaat J, Selter P, Lukaschek M, Wechselberger R, Khamiakova T, Valkenborg D. R Shiny App for the Automated Deconvolution of NMR Spectra to Quantify the Solid-State Forms of Pharmaceutical Mixtures. Metabolites 2022; 12:metabo12121248. [PMID: 36557287 PMCID: PMC9786300 DOI: 10.3390/metabo12121248] [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: 10/01/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Bioavailability and chemical stability are important characteristics of drug products that are strongly affected by the solid-state structure of the active pharmaceutical ingredient (API). In pharmaceutical development and quality control activities, solid-state NMR (ssNMR) has proved to be an excellent tool for the detection and accurate quantification of undesired solid-state forms. To obtain correct quantitative outcomes, the resulting spectrum of an analytical sample should be deconvoluted into the individual spectra of the pure components. However, the ssNMR deconvolution is particularly challenging due to the following: the relatively large line widths that may lead to severe peak overlap, multiple spinning sidebands as a result of applying Magic Angle Spinning (MAS), and highly irregular peak shapes commonly observed in mixture spectra. To address these challenges, we created a tailored and automated deconvolution approach of ssNMR mixture spectra that involves a linear combination modelling (LCM) of previously acquired reference spectra of pure solid-state components. For optimal model performance, the template and mixture spectra should be acquired under the same conditions and experimental settings. In addition to the parameters controlling the contributions of the components in the mixture, the proposed model includes terms for spectral processing such as phase correction and horizontal shifting that are all jointly estimated via a non-linear, constrained optimisation algorithm. Finally, our novel procedure has been implemented in a fully functional and user-friendly R Shiny webtool (hence no local R installation required) that offers interactive data visualisations, manual adjustments to the automated deconvolution results, and the traceability and reproducibility of analyses.
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Affiliation(s)
- Piotr Prostko
- Data Science Institute, UHasselt—Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan 1, BE 3590 Diepenbeek, Belgium
| | - Jeroen Pikkemaat
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Philipp Selter
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Michail Lukaschek
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Rainer Wechselberger
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Tatsiana Khamiakova
- Janssen Pharmaceutica, Manufacturing and Applied Statistics, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Dirk Valkenborg
- Data Science Institute, UHasselt—Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan 1, BE 3590 Diepenbeek, Belgium
- Correspondence:
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11
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Early detection of ureteropelvic junction obstruction in neonates with prenatal diagnosis of renal pelvis dilatation using 1H NMR urinary metabolomics. Sci Rep 2022; 12:13406. [PMID: 35927301 PMCID: PMC9352869 DOI: 10.1038/s41598-022-17664-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 07/28/2022] [Indexed: 11/25/2022] Open
Abstract
Renal pelvis dilatation (RPD) is diagnosed in utero on prenatal ultrasonography (US) and can resolve spontaneously. However, isolated RPD can also reflect ureteropelvic junction obstruction (UPJO), which requires surgical treatment to prevent progressive renal deterioration. The diagnosis of UPJO can only be confirmed after birth with repeat US and renal isotope studies. 1H Nuclear Magnetic Resonance spectroscopy (NMR) was performed on urine of newborns with prenatally diagnosed unilateral RPD and healthy controls to identify specific urinary biomarkers for UPJO. The original combination of EigenMS normalization and sparse partial-least-squares discriminant analysis improved selectivity and sensitivity. In total, 140 urine samples from newborns were processed and 100 metabolites were identified. Correlation network identified discriminant metabolites in lower concentrations in UPJO patients. Two main metabolic pathways appeared to be impaired in patients with UPJO i.e. amino acid and betaine metabolism. In this prospective study, metabolic profiling of urine samples by NMR clearly distinguishes patients who required surgery for UPJO from patients with transient dilatations and controls. This study will pave the way for the use of metabolomics for the diagnosis of prenatal hydronephrosis in clinical routine.
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12
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Valorization of Traditional Italian Walnut (Juglans regia L.) Production: Genetic, Nutritional and Sensory Characterization of Locally Grown Varieties in the Trentino Region. PLANTS 2022; 11:plants11151986. [PMID: 35956464 PMCID: PMC9370163 DOI: 10.3390/plants11151986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/22/2022] [Accepted: 07/24/2022] [Indexed: 12/12/2022]
Abstract
Juglans regia (L.) is cultivated worldwide for its nutrient-rich nuts. In Italy, despite the growing demand, walnut cultivation has gone through a strong decline in recent decades, which led to Italy being among the top five net importing countries. To promote the development of local high-quality Italian walnut production, we devised a multidisciplinary project to highlight the distinctive traits of three varieties grown in the mountainous region Trentino (northeast of Italy): the heirloom ‘Bleggiana’, a second local accession called local Franquette and the French cultivar ‘Lara’, recently introduced in the local production to increase yield. The genetic characterization confirmed the uniqueness of ‘Bleggiana’ and revealed local Franquette as a newly described autochthonous variety, thus named ‘Blegette’. The metabolic profiles highlighted a valuable nutritional composition of the local varieties, richer in polyphenols and with a lower ω-6/ω-3 ratio than the commercial ‘Lara’. ‘Blegette’ obtained the highest preference scores from consumers for both the visual aspect and tasting; however, the volatile organic compound profiles did not discriminate among the characterized cultivars. The described local varieties represent an interesting reservoir of walnut genetic diversity and quality properties, which deserve future investigation on agronomically useful traits (e.g., local adaptation and water usage) for a high-quality and sustainable production.
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Lindner S, Burger R, Rutledge DN, Do XT, Rumpf J, Diehl BWK, Schulze M, Monakhova YB. Is the Calibration Transfer of Multivariate Calibration Models between High- and Low-Field NMR Instruments Possible? A Case Study of Lignin Molecular Weight. Anal Chem 2022; 94:3997-4004. [PMID: 35201769 DOI: 10.1021/acs.analchem.1c05125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Although several successful applications of benchtop nuclear magnetic resonance (NMR) spectroscopy in quantitative mixture analysis exist, the possibility of calibration transfer remains mostly unexplored, especially between high- and low-field NMR. This study investigates for the first time the calibration transfer of partial least squares regressions [weight average molecular weight (Mw) of lignin] between high-field (600 MHz) NMR and benchtop NMR devices (43 and 60 MHz). For the transfer, piecewise direct standardization, calibration transfer based on canonical correlation analysis, and transfer via the extreme learning machine auto-encoder method are employed. Despite the immense resolution difference between high-field and low-field NMR instruments, the results demonstrate that the calibration transfer from high- to low-field is feasible in the case of a physical property, namely, the molecular weight, achieving validation errors close to the original calibration (down to only 1.2 times higher root mean square errors). These results introduce new perspectives for applications of benchtop NMR, in which existing calibrations from expensive high-field instruments can be transferred to cheaper benchtop instruments to economize.
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Affiliation(s)
- Simon Lindner
- Department of Chemistry and Biotechnology, FH Aachen University of Applied Sciences, Jülich 52428, Germany.,Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - René Burger
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Douglas N Rutledge
- Université Paris-Saclay, INRAE, AgroParisTech, UMR SayFood, Paris 75005, France.,National Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga 2650, Australia
| | - Xuan Tung Do
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Jessica Rumpf
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Bernd W K Diehl
- Spectral Service AG, Emil-Hoffmann-Straße 33, Köln 50996, Germany
| | - Margit Schulze
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, Rheinbach 53359, Germany
| | - Yulia B Monakhova
- Department of Chemistry and Biotechnology, FH Aachen University of Applied Sciences, Jülich 52428, Germany.,Spectral Service AG, Emil-Hoffmann-Straße 33, Köln 50996, Germany.,Institute of Chemistry, Saratov State University, Saratov 410012, Russia
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14
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Ye D, Li X, Shen J, Xia X. Microbial metabolomics: From novel technologies to diversified applications. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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15
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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16
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Solovyev PA, Fauhl-Hassek C, Riedl J, Esslinger S, Bontempo L, Camin F. NMR spectroscopy in wine authentication: An official control perspective. Compr Rev Food Sci Food Saf 2021; 20:2040-2062. [PMID: 33506593 DOI: 10.1111/1541-4337.12700] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/30/2020] [Accepted: 12/23/2020] [Indexed: 12/14/2022]
Abstract
Wine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, 2 H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, 1 H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and nontargeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of 1 H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open-source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.
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Affiliation(s)
- Pavel A Solovyev
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, San Michele all'Adige, 38010, Italy
| | - Carsten Fauhl-Hassek
- German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Unit Product Identity, Supply Chains and Traceability, Max-Dohrn Strasse, 8-10, Berlin, 10589, Germany
| | - Janet Riedl
- German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Unit Product Identity, Supply Chains and Traceability, Max-Dohrn Strasse, 8-10, Berlin, 10589, Germany
| | - Susanne Esslinger
- German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Unit Product Identity, Supply Chains and Traceability, Max-Dohrn Strasse, 8-10, Berlin, 10589, Germany
| | - Luana Bontempo
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, San Michele all'Adige, 38010, Italy
| | - Federica Camin
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), via E. Mach 1, San Michele all'Adige, 38010, Italy.,Center Agriculture Food Environment (C3A), University of Trento, via Mach 1, San Michele all'Adige, Tennessee, 38010, Italy
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17
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Edison AS, Colonna M, Gouveia GJ, Holderman NR, Judge MT, Shen X, Zhang S. NMR: Unique Strengths That Enhance Modern Metabolomics Research. Anal Chem 2020; 93:478-499. [DOI: 10.1021/acs.analchem.0c04414] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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18
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Mikkelsen M, Tapper S, Near J, Mostofsky SH, Puts NAJ, Edden RAE. Correcting frequency and phase offsets in MRS data using robust spectral registration. NMR IN BIOMEDICINE 2020; 33:e4368. [PMID: 32656879 PMCID: PMC9652614 DOI: 10.1002/nbm.4368] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 06/10/2020] [Accepted: 06/16/2020] [Indexed: 05/16/2023]
Abstract
An algorithm for retrospective correction of frequency and phase offsets in MRS data is presented. The algorithm, termed robust spectral registration (rSR), contains a set of subroutines designed to robustly align individual transients in a given dataset even in cases of significant frequency and phase offsets or unstable lipid contamination and residual water signals. Data acquired by complex multiplexed editing approaches with distinct subspectral profiles are also accurately aligned. Automated removal of unstable lipid contamination and residual water signals is applied first, when needed. Frequency and phase offsets are corrected in the time domain by aligning each transient to a weighted average reference in a statistically optimal order using nonlinear least-squares optimization. The alignment of subspectra in edited datasets is performed using an approach that specifically targets subtraction artifacts in the frequency domain. Weighted averaging is then used for signal averaging to down-weight poorer-quality transients. Algorithm performance was assessed on one simulated and 67 in vivo pediatric GABA-/GSH-edited HERMES datasets and compared with the performance of a multistep correction method previously developed for aligning HERMES data. The performance of the novel approach was quantitatively assessed by comparing the estimated frequency/phase offsets against the known values for the simulated dataset or by examining the presence of subtraction artifacts in the in vivo data. Spectral quality was improved following robust alignment, especially in cases of significant spectral distortion. rSR reduced more subtraction artifacts than the multistep method in 64% of the GABA difference spectra and 75% of the GSH difference spectra. rSR overcomes the major challenges of frequency and phase correction.
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Affiliation(s)
- Mark Mikkelsen
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Sofie Tapper
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Jamie Near
- Douglas Mental Health University Institute and Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nicolaas A. J. Puts
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
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19
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Hajjar G, Merchak N, Daniel C, Rizk T, Akoka S, Bejjani J. Improved lipid mixtures profiling by 1H NMR using reference lineshape adjustment and deconvolution techniques. Talanta 2020; 208:120475. [DOI: 10.1016/j.talanta.2019.120475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/19/2019] [Accepted: 10/13/2019] [Indexed: 10/25/2022]
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20
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Dynamic observation and analysis of metabolic response to moxibustion stimulation on ethanol-induced gastric mucosal lesions (GML) rats. Chin Med 2019; 14:44. [PMID: 31636695 PMCID: PMC6794790 DOI: 10.1186/s13020-019-0266-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Accepted: 10/04/2019] [Indexed: 12/13/2022] Open
Abstract
Background Gastric mucosal lesion (GML) is the initiating pathological process in many refractory gastric diseases. And moxibustion is an increasingly popular alternative therapy that prevents and treats diseases. However, there are few published reports about developing pathology of GML and therapeutic mechanism of moxibustion treatment on GML. In this study, we investigated pathology of GML and therapeutic mechanism of moxibustion treatment on GML. Methods The male Sprague-Dawley (SD) rats were induced by intragastric administration of 75% ethanol after fasting for 24 h and treated by moxibustion at Zusanli (ST36) and Liangmen (ST21) for 1 day, 4 days or 7 days. Then we applied 1H NMR-based metabolomics to dynamic analysis of metabolic profiles in biological samples (stomach, cerebral cortex and medulla). And the conventional histopathological examinations as well as metabolic pathways assays were also performed. Results Moxibustion intervention showed a beneficial effect on GML by modulating comprehensive metabolic alterations caused by GML, including energy metabolism, membrane metabolism, cellular active and neurotransmitters function. Conclusions Moxibustion can effectively treat gastric mucosal damage and effectively regulate the concentration of some related differential metabolites to maintain the stability of the metabolic pathway.
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21
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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22
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Cobas C. Applications of the Whittaker smoother in NMR spectroscopy. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2018; 56:1140-1148. [PMID: 29719068 DOI: 10.1002/mrc.4747] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 04/09/2018] [Accepted: 04/16/2018] [Indexed: 05/26/2023]
Abstract
The Whittaker smoother, a special case of penalized least square, is a multipurpose algorithm that has proven to be very useful in many scientific fields, including image processing, chromatography, and optical spectroscopy. It shares many similarities with the Savitzky-Golay algorithm, but it is significantly faster and easier to automate. Its use in nuclear magnetic resonance, however, is not widespread although several applications have recently been published. In this review, the mathematical background of the method and its main applications in nuclear magnetic resonance spectroscopy will be discussed.
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Affiliation(s)
- Carlos Cobas
- Mestrelab Research S.L., Santiago de Compostela, A Coruña, 15706, Spain
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