1
|
Maestrello V, Solovyev P, Stroppa A, Bontempo L, Franceschi P. 1H NMR profiling and chemometric analysis for ripening and production characterization of Grana Padano cheese. Food Chem 2024; 456:139986. [PMID: 38852457 DOI: 10.1016/j.foodchem.2024.139986] [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: 01/31/2024] [Revised: 05/23/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
Grana Padano (GP) cheese is a renowned PDO Italian cheese whose nutritional characteristics and market price are influenced by the ripening stage. In this work, it was demonstrated that the combined use of untargeted 1H NMR profiling and chemometric analysis can be used as a powerful tool to quantitatively characterize GP ripening and production, focusing on both aqueous and lipid fractions. An initial exploratory analysis revealed substantial variations in the aqueous fraction attributable to aging time, year and season of production. Multivariate analysis was adopted to show these differences, mainly attributable to amino acids. In contrast, the lipid fraction analysis highlighted the impact of production season on fatty acid unsaturation, influenced by feed variations. As regards the production process, this study focuses on the variations induced by bactofugation. In this respect, the aqueous fraction was found to be extensively influenced by this centrifugation step, affecting compounds crucial to organoleptic characteristics.
Collapse
Affiliation(s)
- Valentina Maestrello
- Fondazione Edmund Mach (FEM), via E. Mach 1, 38098 San Michele all'Adige, Italy; University of Trento, via Mach 1, 38098 San Michele all'Adige, (TN), Italy.
| | - Pavel Solovyev
- Fondazione Edmund Mach (FEM), via E. Mach 1, 38098 San Michele all'Adige, Italy.
| | - Angelo Stroppa
- Consorzio Tutela Grana Padano, Via XXIV Giugno 8, 25015, San Martino Della Battaglia, Desenzano del Garda, BS, Italy.
| | - Luana Bontempo
- Fondazione Edmund Mach (FEM), via E. Mach 1, 38098 San Michele all'Adige, Italy
| | - Pietro Franceschi
- Fondazione Edmund Mach (FEM), via E. Mach 1, 38098 San Michele all'Adige, Italy
| |
Collapse
|
2
|
Bischof G, Januschewski E, Juadjur A. Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning. Foods 2024; 13:1098. [PMID: 38611402 PMCID: PMC11011716 DOI: 10.3390/foods13071098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024] Open
Abstract
(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.
Collapse
Affiliation(s)
- Greta Bischof
- Chemical Analytics, German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany (A.J.)
| | | | | |
Collapse
|
3
|
Bischof G, Witte F, Januschewski E, Schilling F, Terjung N, Heinz V, Juadjur A, Gibis M. Authentication of aged beef in terms of aging time and aging type by 1H NMR spectroscopy. Food Chem 2024; 435:137531. [PMID: 37774627 DOI: 10.1016/j.foodchem.2023.137531] [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: 05/25/2023] [Revised: 08/31/2023] [Accepted: 09/17/2023] [Indexed: 10/01/2023]
Abstract
Meat authenticity addresses parameters such as species, breed, sex, housing system and postmortem treatment. Seventy-four beef backs from two breeds ('Fleckvieh' and 'Schwarzbunt') and three cattle types (heifer, cow, young bull) were dry-aged and wet-aged up to 28 days and analyzed by 1H NMR spectroscopy. Statistical models based on partial least squares regression and discriminant analysis were performed to classify the beef samples by breed, cattle type, aging time, and aging type based on their 1H NMR spectra. The aging time of beef samples can be predicted with an error ± 2.28 days. The cattle type model has an accuracy of cross-validation of 99.2 %, the breed models of 100 % and the aging type model for 28-days aged samples of 99.6 %. These models allow the authentication of beef samples in terms of breed, cattle type, aging time, and aging type with a single 1H NMR measurement.
Collapse
Affiliation(s)
- Greta Bischof
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany; Department of Food Material Science, Institute of Food Science and Biotechnology, University of Hohenheim, Garbenstr. 25, 70599 Stuttgart, Germany
| | - Franziska Witte
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Edwin Januschewski
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Frank Schilling
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Nino Terjung
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Volker Heinz
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Andreas Juadjur
- German Institute of Food Technologies (DIL e.V.), Prof.-v.-Klitzing-Str. 7, 49610 Quakenbrück, Germany
| | - Monika Gibis
- Department of Food Material Science, Institute of Food Science and Biotechnology, University of Hohenheim, Garbenstr. 25, 70599 Stuttgart, Germany.
| |
Collapse
|
4
|
Roach J, Mital R, Haffner JJ, Colwell N, Coats R, Palacios HM, Liu Z, Godinho JLP, Ness M, Peramuna T, McCall LI. Microbiome metabolite quantification methods enabling insights into human health and disease. Methods 2024; 222:81-99. [PMID: 38185226 DOI: 10.1016/j.ymeth.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/09/2024] Open
Abstract
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.
Collapse
Affiliation(s)
- Jarrod Roach
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Rohit Mital
- Department of Biology, University of Oklahoma
| | - Jacob J Haffner
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Nathan Colwell
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Randy Coats
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Horvey M Palacios
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma
| | | | - Monica Ness
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Thilini Peramuna
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma; Department of Chemistry and Biochemistry, San Diego State University.
| |
Collapse
|
5
|
Vieira de Sousa T, Guedes de Pinho P, Pinto J. Metabolomic Signatures of Treatment Response in Bladder Cancer. Int J Mol Sci 2023; 24:17543. [PMID: 38139377 PMCID: PMC10743932 DOI: 10.3390/ijms242417543] [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: 11/24/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Bladder cancer (BC) stands as one of the most prevalent urological malignancies, with over 500 thousand newly diagnosed cases annually. Treatment decisions in BC depend on factors like the risk of recurrence, the type of tumor, and the stage of the disease. While standard therapeutic approaches encompass transurethral resection of the bladder tumor, radical cystectomy, and chemo- or immunotherapy, these methods exhibit limited efficacy in mitigating the aggressive and recurrent nature of bladder tumors. To overcome this challenge, it is crucial to develop innovative methods for monitoring and predicting treatment responses among patients with BC. Metabolomics is gaining recognition as a promising approach for discovering biomarkers. It has the potential to reveal metabolic disruptions that precisely reflect how BC patients respond to particular treatments, providing a revolutionary method to improve accuracy in monitoring and predicting outcomes. In this article, we present a comprehensive review of studies employing metabolomics approaches to investigate the metabolic responses associated with different treatment modalities for BC. The review encompasses an exploration of various models, samples, and analytical techniques applied in this context. Special emphasis is placed on the reported changes in metabolite levels derived from these studies, highlighting their potential as biomarkers for personalized medicine in BC.
Collapse
Affiliation(s)
- Tiago Vieira de Sousa
- Associate Laboratory i4HB–Institute for Health and Bioeconomy, University of Porto, 4050-313 Porto, Portugal;
- UCIBIO–Applied Molecular Biosciences Unit, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Paula Guedes de Pinho
- Associate Laboratory i4HB–Institute for Health and Bioeconomy, University of Porto, 4050-313 Porto, Portugal;
- UCIBIO–Applied Molecular Biosciences Unit, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Joana Pinto
- Associate Laboratory i4HB–Institute for Health and Bioeconomy, University of Porto, 4050-313 Porto, Portugal;
- UCIBIO–Applied Molecular Biosciences Unit, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, Rua Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| |
Collapse
|
6
|
Dudka I, Lundquist K, Wikström P, Bergh A, Gröbner G. Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes. J Transl Med 2023; 21:860. [PMID: 38012666 PMCID: PMC10683247 DOI: 10.1186/s12967-023-04747-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 11/21/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis. METHODS A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity). RESULTS Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B. CONCLUSIONS The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.
Collapse
Affiliation(s)
- Ilona Dudka
- Department of Chemistry, Umeå University, Umeå, Sweden
| | | | - Pernilla Wikström
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden.
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | | |
Collapse
|
7
|
Rai MK, Yadav S, Jain A, Singh K, Kumar A, Raj R, Dubey D, Singh H, Guleria A, Chaturvedi S, Khan AR, Nath A, Misra DP, Agarwal V, Kumar D. Clinical metabolomics by NMR revealed serum metabolic signatures for differentiating sarcoidosis from tuberculosis. Metabolomics 2023; 19:92. [PMID: 37940751 DOI: 10.1007/s11306-023-02052-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 09/20/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Pulmonary sarcoidosis (SAR) and tuberculosis (TB) are two granulomatous lung-diseases and often pose a diagnostic challenge to a treating physicians. OBJECTIVE The present study aims to explore the diagnostic potential of NMR based serum metabolomics approach to differentiate SAR from TB. MATERIALS AND METHOD The blood samples were obtained from three study groups: SAR (N = 35), TB (N = 28) and healthy normal subjects (NC, N = 56) and their serum metabolic profiles were measured using 1D 1H CPMG (Carr-Purcell-Meiboom-Gill) NMR spectra recorded at 800 MHz NMR spectrometer. The quantitative metabolic profiles were compared employing a combination of univariate and multivariate statistical analysis methods and evaluated for their diagnostic potential using receiver operating characteristic (ROC) curve analysis. RESULTS Compared to SAR, the sera of TB patients were characterized by (a) elevated levels of lactate, acetate, 3-hydroxybutyrate (3HB), glutamate and succinate (b) decreased levels of glucose, citrate, pyruvate, glutamine, and several lipid and membrane metabolites (such as very-low/low density lipoproteins (VLDL/LDL), polyunsaturated fatty acids, etc.). CONCLUSION The metabolic disturbances not only found to be well in concordance with various previous reports, these further demonstrated very high sensitivity and specificity to distinguish SAR from TB patients suggesting serum metabolomics analysis can serve as surrogate method in the diagnosis and clinical management of SAR.
Collapse
Affiliation(s)
- Mohit Kumar Rai
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
| | - Sachin Yadav
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
- Department of Chemistry, Integral University, Lucknow, UP, 226026, India
| | - Avinash Jain
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India.
- Department of Clinical Immunology and Rheumatology, SMS Medical College, Jaipur, India.
| | - Kritika Singh
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
| | - Amit Kumar
- Centre of Biomedical Research (CBMR), Lucknow, UP, 226014, India
| | - Ritu Raj
- Centre of Biomedical Research (CBMR), Lucknow, UP, 226014, India
| | - Durgesh Dubey
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
- Centre of Biomedical Research (CBMR), Lucknow, UP, 226014, India
| | - Harshit Singh
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
- Immuno Biology Lab, Translational Health Science and Technology Institute, Faridabad, HR, 121001, India
| | - Anupam Guleria
- Centre of Biomedical Research (CBMR), Lucknow, UP, 226014, India
| | - Saurabh Chaturvedi
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Sector III, Pushp Vihar, M.B. Road, New Delhi, 110017, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Lucknow, UP, 226026, India
| | - Alok Nath
- Department of Pulmonary Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
| | - Durga Prasanna Misra
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India
| | - Vikas Agarwal
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, 226014, India.
| | - Dinesh Kumar
- Centre of Biomedical Research (CBMR), Lucknow, UP, 226014, India.
| |
Collapse
|
8
|
Wenck S, Mix T, Fischer M, Hackl T, Seifert S. Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables. Metabolites 2023; 13:1075. [PMID: 37887402 PMCID: PMC10608983 DOI: 10.3390/metabo13101075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.
Collapse
Affiliation(s)
- Soeren Wenck
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
| | - Thorsten Mix
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany;
| | - Markus Fischer
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
| | - Thomas Hackl
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
- Institute of Organic Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany;
| | - Stephan Seifert
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany (M.F.); (T.H.)
| |
Collapse
|
9
|
Kim J, Woo HK, Vimalajeewa D, Vidakovic B. Analysis and classification of 1H-NMR spectra by multifractal analysis. PLoS One 2023; 18:e0286205. [PMID: 37289702 PMCID: PMC10249796 DOI: 10.1371/journal.pone.0286205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/11/2023] [Indexed: 06/10/2023] Open
Abstract
The objective of this research focuses on the development of a statistical methodology able to answer the question of whether variation in the intake of sulfur amino acids (SAA) affects the metabolic process. Traditional approaches, which evaluate specific biomarkers after a series of preprocessing procedures, have been criticized as not being fully informative, as well as inappropriate for translation of methodology. Rather than focusing on particular biomarkers, our proposed methodology involves the multifractal analysis that measures the inhomogeneity of regularity of the proton nuclear magnetic resonance (1H-NMR) spectrum by wavelet-based multifractal spectrum. With two different statistical models (Model-I and Model-II), three different geometric features of the multifractal spectrum of each 1H-NMR spectrum (spectral mode, left slope, and broadness) are employed to evaluate the effect of SAA and discriminate 1H-NMR spectra associated with different treatments. The investigated effects of SAA include group effect (high and low doses of SAA), depletion/repletion effect, and time over data effect. The 1H-NMR spectra analysis outcomes show that group effect is significant for both models. The hourly variation in time and depletion/repletion effects does not show noticeable differences for the three features in Model-I. However, these two effects are significant for the spectral mode feature in Model-II. The 1H-NMR spectra of the SAA low groups exhibit highly regular patterns with more variability than that of the SAA high groups for both models. Moreover, the discriminatory analysis conducted using the support vector machine and the principal components analysis shows that the 1H-NMR spectra of SAA high and low groups can be easily discriminatory for both models, while the spectra of depletion and repletion within these groups are discriminatory for Model-I and Model-II. Therefore, the study outcomes imply that the amount of SAA is important and that SAA intake affects mostly the hourly variation of the metabolic process and the difference between depletion and repletion each day. In conclusion, the proposed multifractal analysis of 1H-NMR spectra provides a novel tool to investigate metabolic processes.
Collapse
Affiliation(s)
- Jongphil Kim
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States of America
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, United States of America
| | - Hin Kyeol Woo
- Snapchat Inc., New York, NY, United States of America
| | - Dixon Vimalajeewa
- Department of Statistics, Texas A&M University, College Station, TX, United States of America
| | - Brani Vidakovic
- Department of Statistics, Texas A&M University, College Station, TX, United States of America
| |
Collapse
|
10
|
Nicoleti JL, Braga ES, Stanisic D, Jadranin M, Façanha DAE, Barral TD, Hanna SA, Azevedo V, Meyer R, Tasic L, Portela RW. A serum NMR metabolomic analysis of the Corynebacterium pseudotuberculosis infection in goats. Appl Microbiol Biotechnol 2023:10.1007/s00253-023-12595-0. [PMID: 37219572 DOI: 10.1007/s00253-023-12595-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 05/24/2023]
Abstract
Caseous lymphadenitis (CLA), an infectious disease caused by Corynebacterium pseudotuberculosis in small ruminants, is highly prevalent worldwide. Economic losses have already been associated with the disease, and little is known about the host-pathogen relationship associated with the disease. The present study aimed to perform a metabolomic study of the C. pseudotuberculosis infection in goats. Serum samples were collected from a herd of 173 goats. The animals were classified as controls (not infected), asymptomatic (seropositives but without detectable CLA clinical signs), and symptomatic (seropositive animals presenting CLA lesions), according to microbiological isolation and immunodiagnosis. The serum samples were analyzed using nuclear magnetic resonance (1H-NMR), nuclear Overhauser effect spectroscopy (NOESY), and Carr-Purcell-Meiboom-Gill (CPMG) sequences. The NMR data were analyzed using chemometrics, and principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were performed to discover specific biomarkers responsible for discrimination between the groups. A high dissemination of the infection by C. pseudotuberculosis was observed, being 74.57% asymptomatic and 11.56% symptomatic. In the evaluation of 62 serum samples by NMR, the techniques were satisfactory in the discrimination of the groups, being also complementary and mutually confirming, demonstrating possible biomarkers for the infection by the bacterium. Twenty metabolites of interest were identified by NOESY and 29 by CPMG, such as tryptophan, polyunsaturated fatty acids, formic acid, NAD+, and 3-hydroxybutyrate, opening promising possibilities for the use of these results in new therapeutic, immunodiagnosis, and immunoprophylactic tools, as well as for studies of the immune response against C. pseudotuberculosis. KEY POINTS: • Sixty-two samples from healthy, CLA asymptomatic, and symptomatic goats were screened • Twenty metabolites of interest were identified by NOESY and 29 by CPMG • 1H-NMR NOESY and CPMG were complementary and mutually confirming.
Collapse
Affiliation(s)
- Jorge Luis Nicoleti
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Erik Sobrinho Braga
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
| | - Danijela Stanisic
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
| | - Milka Jadranin
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
- Institute of Chemistry, Technology and Metallurgy, University of Belgrade, 11000, Belgrade, Serbia
| | - Débora Andréa Evangelista Façanha
- Institute of Rural Development, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Redenção, Ceará State, 62790-000, Brazil
| | - Thiago Doria Barral
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Samira Abdallah Hanna
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Vasco Azevedo
- Laboratório de Genética Celular e Molecular, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais State, 31270-901, Brazil
| | - Roberto Meyer
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil
| | - Ljubica Tasic
- Laboratório de Química Biológica, Instituto de Química, Universidade Estadual de Campinas, Campinas, São Paulo State, 13083-970, Brazil
| | - Ricardo Wagner Portela
- Laboratório de Imunologia E Biologia Molecular, Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia State, 40231-300, Brazil.
| |
Collapse
|
11
|
van der Kemp WJM, Grinde MT, Malvik JO, van Laarhoven HWM, Prompers JJ, Klomp DWJ, Burgering B, Bathen TF, Moestue SA. Metabolic profiling of colorectal cancer organoids: A comparison between high-resolution magic angle spinning magnetic resonance spectroscopy and solution nuclear magnetic resonance spectroscopy of polar extracts. NMR IN BIOMEDICINE 2023; 36:e4882. [PMID: 36451530 DOI: 10.1002/nbm.4882] [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: 12/21/2021] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
Patient-derived cancer cells cultured in vitro are a cornerstone of cancer metabolism research. More recently, the introduction of organoids has provided the research community with a more versatile model system. Physiological structure and organization of the cell source tissue are maintained in organoids, representing a closer link to in vivo tumor models. High-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) is a commonly applied analytical approach for metabolic profiling of intact tissue, but its use has not been reported for organoids. The aim of the current work was to compare the performance of HR MAS MRS and extraction-based nuclear magnetic resonance (NMR) in metabolic profiling of wild-type and tumor progression organoids (TPOs) from human colon cancer, and further to investigate how the sequentially increased genetic alterations of the TPOs affect the metabolic profile. Sixteen metabolites were reliably identified and quantified both in spectra based on NMR of extracts and HR MAS MRS of intact organoids. The metabolite concentrations from the two approaches were highly correlated (r = 0.94), and both approaches were able to capture the systematic changes in metabolic features introduced by the genetic alterations characteristic of colorectal cancer progression (e.g., increased levels of lactate and decreased levels of myo-inositol and phosphocholine with an increasing number of mutations). The current work highlights that HR MAS MRS is a well-suited method for metabolic profiling of intact organoids, with the additional benefit that the nondestructive nature of HR MAS enables subsequent recovery of the organoids for further analyses based on nucleic acids or proteins.
Collapse
Affiliation(s)
- Wybe J M van der Kemp
- Division of Imaging and Oncology, University Medical Centre (UMC) Utrecht, Utrecht, The Netherlands
| | - Maria T Grinde
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jon O Malvik
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeanine J Prompers
- Division of Imaging and Oncology, University Medical Centre (UMC) Utrecht, Utrecht, The Netherlands
| | - Dennis W J Klomp
- Division of Imaging and Oncology, University Medical Centre (UMC) Utrecht, Utrecht, The Netherlands
| | - Boudewijn Burgering
- Center for Molecular Medicine, Molecular Cancer Research, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Siver Andreas Moestue
- Department of Clinical and Molecular Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Pharmacy, Nord University, Bodø, Norway
| |
Collapse
|
12
|
Dasgupta S, Ghosh N, Bhattacharyya P, Roy Chowdhury S, Chaudhury K. Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview. Crit Rev Clin Lab Sci 2023; 60:153-170. [PMID: 36420874 DOI: 10.1080/10408363.2022.2140329] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.
Collapse
Affiliation(s)
- Sanjukta Dasgupta
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Nilanjana Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| |
Collapse
|
13
|
Uchimiya M, Olofsson M, Powers MA, Hopkinson BM, Moran MA, Edison AS. 13C NMR metabolomics: J-resolved STOCSY meets INADEQUATE. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 347:107365. [PMID: 36634594 DOI: 10.1016/j.jmr.2022.107365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/20/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Robust annotation of metabolites is a challenging task in metabolomics. Among available applications, 13C NMR experiment INADEQUATE determines direct 13C-13C connectivity unambiguously, offering indispensable information on molecular structure. Despite its great utility, it is not always practical to collect INADEQUATE data on every sample in a large metabolomics study because of its relatively long experiment time. Here, we propose an alternative approach that maintains the quality of information but saves experiment time. In this approach, individual samples in a study are first screened by 13C homonuclear J-resolved experiment (JRES). Next, JRES data are processed by statistical total correlation spectroscopy (STOCSY) to extract peaks that behave similarly among samples. Finally, INADEQUATE is collected on one internal pooled sample to select STOCSY peaks that originate from the same compound. We tested this concept using the 13C-labeled endometabolome of a model marine diatom strain incubated under various settings, intending to cover a range of metabolites produced under different external conditions. This scheme was able to extract known diatom metabolites proline, 2,3-dihydroxypropane-1-sulfonate (DHPS), β-1,3-glucan, choline, and glutamate. This pipeline also detected unknown compounds with structural information, which is valuable in metabolomics where a priori knowledge of metabolites is not always available. The ability of this scheme was seen even in sugar regions, which are usually challenging in 1H NMR due to severe peak overlap. JRES and INADEQUATE were highly complementary; INADEQUATE provided directly-bonded 13C networks, whereas JRES linked INADEQUATE networks within the same compound but broken by nitrogen or sulfur atoms, highlighting the advantage of this integrated approach.
Collapse
Affiliation(s)
- Mario Uchimiya
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Malin Olofsson
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Sweden; Department of Marine Sciences, University of Georgia, Athens, GA 30602, USA
| | - McKenzie A Powers
- Department of Marine Sciences, University of Georgia, Athens, GA 30602, USA
| | - Brian M Hopkinson
- Department of Marine Sciences, University of Georgia, Athens, GA 30602, USA
| | - Mary Ann Moran
- Department of Marine Sciences, University of Georgia, Athens, GA 30602, USA
| | - Arthur S Edison
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA; Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.
| |
Collapse
|
14
|
de Medeiros LS, de Araújo Júnior MB, Peres EG, da Silva JCI, Bassicheto MC, Di Gioia G, Veiga TAM, Koolen HHF. Discovering New Natural Products Using Metabolomics-Based Approaches. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1439:185-224. [PMID: 37843810 DOI: 10.1007/978-3-031-41741-2_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.
Collapse
Affiliation(s)
- Lívia Soman de Medeiros
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil.
| | - Moysés B de Araújo Júnior
- Grupo de Pesquisa em Metabolômica e Espectrometria de Massas, Universidade do Estado do Amazonas, Manaus, Brazil
| | - Eldrinei G Peres
- Grupo de Pesquisa em Metabolômica e Espectrometria de Massas, Universidade do Estado do Amazonas, Manaus, Brazil
| | | | - Milena Costa Bassicheto
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | - Giordanno Di Gioia
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | - Thiago André Moura Veiga
- Grupo de Pesquisas LaBiORG - Laboratório de Química Bio-orgânica Otto Richard Gottlieb, Universidade Federal de São Paulo, Diadema, Brazil
| | | |
Collapse
|
15
|
Metabolomic Profiling of the Responses of Planktonic and Biofilm Vibrio cholerae to Silver Nanoparticles. Antibiotics (Basel) 2022; 11:antibiotics11111534. [DOI: 10.3390/antibiotics11111534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Vibrio cholerae causes cholera and can switch between planktonic and biofilm lifeforms, where biofilm formation enhances transmission, virulence, and antibiotic resistance. Due to antibiotic microbial resistance, new antimicrobials including silver nanoparticles (AgNPs) are being studied. Nevertheless, little is known about the metabolic changes exerted by AgNPs on both microbial lifeforms. Our objective was to evaluate the changes in the metabolomic profile of V. cholerae planktonic and biofilm cells in response to sublethal concentrations of AgNPs using MS2 untargeted metabolomics and chemoinformatics. A total of 690 metabolites were quantified among all groups. More metabolites were significantly modulated in planktonic cells (n = 71) compared to biofilm (n = 37) by the treatment. The chemical class profiles were distinct for both planktonic and biofilm, suggesting a phenotype-dependent metabolic response to the nanoparticles. Chemical enrichment analysis showed altered abundances of oxidized fatty acids (FA), saturated FA, phosphatidic acids, and saturated stearic acid in planktonic cells treated with AgNPs, which hints at a turnover of the membrane. In contrast, no chemical classes were enriched in the biofilm. In conclusion, this study suggests that the response of V. cholerae to silver nanoparticles is phenotype-dependent and that planktonic cells experience a lipid remodeling process, possibly related to an adaptive mechanism involving the cell membrane.
Collapse
|
16
|
Moco S. Studying Metabolism by NMR-Based Metabolomics. Front Mol Biosci 2022; 9:882487. [PMID: 35573745 PMCID: PMC9094115 DOI: 10.3389/fmolb.2022.882487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/24/2022] [Indexed: 12/12/2022] Open
Abstract
During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.
Collapse
|