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Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 24299

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Alessia Vignoli

E-Mail Website
Guest Editor
1. Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
2. Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
3. Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
Interests: applications of NMR-based metabolomics in biomedical research, in particular in the framework of oncology, cardiovascular and respiratory diseases, coeliac disease, and Alzheimer’s disease; analysis of biospecimens via NMR spectroscopy; development of novel statistical tools for NMR data; network analysis of metabolomics data
Special Issues, Collections and Topics in MDPI journals
Dr. Gaia Meoni

E-Mail Website
Guest Editor
1. Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
2. Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
3. Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
Interests: applications of nuclear magnetic resonance (NMR) for the characterization of food matrices in search of molecular factors capable of demonstrating geographic origin and quality; and for the characterization of biological samples in the framework of ageing; Parkinson’s disease; oncology; viral infections and periodontal disease
Special Issues, Collections and Topics in MDPI journals
Dr. Leonardo Tenori

E-Mail Website
Guest Editor
1. Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
2. Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
3. Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
Interests: applications of NMR-based metabolomics in biomedicine and in food science; NMR fingerprinting and profiling of biological samples; development of new analytical approaches for NMR metabolomics; development of new tools for NMR data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites present in a biological specimen. Metabolites represent, at the same time, the downstream output of the genome and the upstream input from various external factors such as the environment, lifestyle and diet. Therefore, in the last few years, metabolomic phenotyping has provided unique insights into the fundamental and molecular causes of several physiological and pathophysiological conditions. This Special Issue aims to publish high-quality research papers related to metabolomics via nuclear magnetic resonance spectroscopy.

We welcome original articles from various fields of metabolomics applications ranging from biomedicine, pharmacology and data mining to biochemistry and clinical chemistry. Systematic reviews or meta-analyses that critically discuss crucial innovations or applications in the field or seek to investigate areas of controversy within the literature will also be considered.

Dr. Alessia Vignoli
Dr. Gaia Meoni
Dr. Leonardo Tenori
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • nuclear magnetic resonance
  • clinical chemistry
  • pathology
  • nutrition
  • biochemistry
  • data mining
  • metabolite quantification
  • pharmacometabolomics

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Published Papers (7 papers)

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Editorial

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3 pages, 183 KiB  
Editorial
Applications and Challenges for Metabolomics via Nuclear Magnetic Resonance Spectroscopy
by Alessia Vignoli, Gaia Meoni and Leonardo Tenori
Appl. Sci. 2022, 12(9), 4655; https://doi.org/10.3390/app12094655 - 6 May 2022
Viewed by 1492
Abstract
Even though metabolomics is about 20 years old, the interest in this “-omic” science is still growing, and high expectations remain in the scientific community for new practical applications in biomedicine and in the agricultural field [...] Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)

Research

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16 pages, 3765 KiB  
Article
Untargeted 1H-NMR Urine Metabolomic Analysis of Preterm Infants with Neonatal Sepsis
by Panagiota D. Georgiopoulou, Styliani A. Chasapi, Irene Christopoulou, Anastasia Varvarigou and Georgios A. Spyroulias
Appl. Sci. 2022, 12(4), 1932; https://doi.org/10.3390/app12041932 - 12 Feb 2022
Cited by 7 | Viewed by 2732
Abstract
One of the most critical medical conditions occurring after preterm birth is neonatal sepsis, a systemic infection with high rates of morbidity and mortality, chiefly amongst neonates hospitalized in Neonatal Intensive Care Units (NICU). Neonatal sepsis is categorized as early-onset sepsis (EOS) and [...] Read more.
One of the most critical medical conditions occurring after preterm birth is neonatal sepsis, a systemic infection with high rates of morbidity and mortality, chiefly amongst neonates hospitalized in Neonatal Intensive Care Units (NICU). Neonatal sepsis is categorized as early-onset sepsis (EOS) and late-onset sepsis (LOS) regarding the time of the disease onset. The accurate early diagnosis or prognosis have hurdles to overcome, since there are not specific clinical signs or laboratory tests. Herein, a need for biomarkers presents, with the goals of aiding accurate medical treatment, reducing the clinical severity of symptoms and the hospitalization time. Through nuclear magnetic resonance (NMR) based metabolomics, we aim to investigate the urine metabolomic profile of septic neonates and reveal those metabolites which could be indicative for an initial discrimination between the diseased and the healthy ones. Multivariate and univariate statistical analysis between NMR spectroscopic data of urine samples from neonates that developed EOS, LOS, and a healthy control group revealed a discriminate metabolic profile of septic newborns. Gluconate, myo-inositol, betaine, taurine, lactose, glucose, creatinine and hippurate were the metabolites highlighted as significant in most comparisons. Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)
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17 pages, 3152 KiB  
Article
Phenotyping Green and Roasted Beans of Nicaraguan Coffea Arabica Varieties Processed with Different Post-Harvest Practices
by Gaia Meoni, Claudio Luchinat, Enrico Gotti, Alejandro Cadena and Leonardo Tenori
Appl. Sci. 2021, 11(24), 11779; https://doi.org/10.3390/app112411779 - 11 Dec 2021
Cited by 4 | Viewed by 2219
Abstract
Metabolomic tecniques have already been used to characterize two of the most common coffee species, C. arabica and C. canephora, but no studies have focused on the characterization of green and roasted coffee varieties of a certain species. We aim to provide, [...] Read more.
Metabolomic tecniques have already been used to characterize two of the most common coffee species, C. arabica and C. canephora, but no studies have focused on the characterization of green and roasted coffee varieties of a certain species. We aim to provide, using NMR-based metabolomics, detailed and comprehensive information regarding the compositional differences of seven coffee varieties (C. arabica) of green and roasted coffee bean batches from Nicaragua. We also evaluated how different varieties react to the same post-harvest procedures such as fermentation time, type of drying and roasting. The characterization of the metabolomic profile of seven different Arabica varieties (Bourbon-typica), allowed us also to assess the possible use of an NMR spectra of bean aqueous extracts to recognize the farm of origin, even considering different farms from the same geographical area (Nueva Segovia). Here, we also evaluated the effect of post-harvest procedures such as fermentation time and type of drying on green and roasted coffee, suggesting that post-harvest procedures can be responsible for different flavours. This study provides proof of concept for the ability of NMR to phenotype coffee, helping to authenticate and optimise the best way of processing coffee. Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)
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Graphical abstract

12 pages, 1525 KiB  
Article
Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse
by Alessia Vignoli, Elena Mori, Samantha Di Donato, Luca Malorni, Chiara Biagioni, Matteo Benelli, Vanessa Calamai, Stefano Cantafio, Annamaria Parnofiello, Maddalena Baraghini, Alessia Garzi, Francesca Del Monte, Dario Romagnoli, Ilenia Migliaccio, Claudio Luchinat, Leonardo Tenori and Laura Biganzoli
Appl. Sci. 2021, 11(23), 11120; https://doi.org/10.3390/app112311120 - 23 Nov 2021
Cited by 4 | Viewed by 2473
Abstract
Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative serum metabolomic fingerprint of [...] Read more.
Background: Colorectal cancer (CRC) is the fourth most commonly diagnosed and third most deadly cancer worldwide. Surgery is the main treatment option for early disease; however, a relevant proportion of CRC patients relapse. Here, variations among preoperative and postoperative serum metabolomic fingerprint of CRC patients were studied, and possible associations between metabolic variations and cancer relapse were explored. Methods: A total of 41 patients with stage I-III CRC, planned for radical resection, were enrolled. Serum samples, collected preoperatively (t0) and 4–6 weeks after surgery before the start of any treatment (t1), were analyzed via NMR spectroscopy. NMR data were analyzed using multivariate and univariate statistical approaches. Results: Serum metabolomic fingerprints show differential clustering between t0 and t1 (82–85% accuracy). Pyruvate, HDL-related parameters, acetone, and 3-hydroxybutyrate appear to be the major players in this discrimination. Eight out of the 41 CRC patients enrolled developed cancer relapse. Postoperative, relapsed patients show an increase of pyruvate and HDL-related parameters, and a decrease of Apo-A1 Apo-B100 ratio and VLDL-related parameters. Conclusions: Surgery significantly alters the metabolomic fingerprint of CRC patients. Some metabolic changes seem to be associated with the development of cancer relapse. These data, if validated in a larger cohort, open new possibilities for risk stratification in patients with early-stage CRC. Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)
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18 pages, 2988 KiB  
Article
Effects of Workers Exposure to Nanoparticles Studied by NMR Metabolomics
by Štěpán Horník, Lenka Michálková, Jan Sýkora, Vladimír Ždímal, Štěpánka Vlčková, Štěpánka Dvořáčková and Daniela Pelclová
Appl. Sci. 2021, 11(14), 6601; https://doi.org/10.3390/app11146601 - 18 Jul 2021
Cited by 2 | Viewed by 2486
Abstract
In this study, the effects of occupational exposure to nanoparticles (NPs) were studied by NMR metabolomics. Exhaled breath condensate (EBC) and blood plasma samples were obtained from a research nanoparticles-processing unit at a national research university. The samples were taken from three groups [...] Read more.
In this study, the effects of occupational exposure to nanoparticles (NPs) were studied by NMR metabolomics. Exhaled breath condensate (EBC) and blood plasma samples were obtained from a research nanoparticles-processing unit at a national research university. The samples were taken from three groups of subjects: samples from workers exposed to nanoparticles collected before and after shift, and from controls not exposed to NPs. Altogether, 60 1H NMR spectra of exhaled breath condensate (EBC) samples and 60 1H NMR spectra of blood plasma samples were analysed, 20 in each group. The metabolites identified together with binning data were subjected to multivariate statistical analysis, which provided clear discrimination of the groups studied. Statistically significant metabolites responsible for group separation served as a foundation for analysis of impaired metabolic pathways. It was found that the acute effect of NPs exposure is mainly reflected in the pathways related to the production of antioxidants and other protective species, while the chronic effect is manifested mainly in the alteration of glutamine and glutamate metabolism, and the purine metabolism pathway. Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)
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15 pages, 1611 KiB  
Article
The Ability to Normalise Energy Metabolism in Advanced COVID-19 Disease Seems to Be One of the Key Factors Determining the Disease Progression—A Metabolomic NMR Study on Blood Plasma
by Eva Baranovicova, Anna Bobcakova, Robert Vysehradsky, Zuzana Dankova, Erika Halasova, Vladimir Nosal and Jan Lehotsky
Appl. Sci. 2021, 11(9), 4231; https://doi.org/10.3390/app11094231 - 7 May 2021
Cited by 19 | Viewed by 3129
Abstract
Background: COVID-19 represents a severe inflammatory condition. Our work was designed to monitor the longitudinal dynamics of the metabolomic response of blood plasma and to reveal presumable discrimination in patients with positive and negative outcomes of COVID-19 respiratory symptoms. Methods: Blood plasma from [...] Read more.
Background: COVID-19 represents a severe inflammatory condition. Our work was designed to monitor the longitudinal dynamics of the metabolomic response of blood plasma and to reveal presumable discrimination in patients with positive and negative outcomes of COVID-19 respiratory symptoms. Methods: Blood plasma from patients, divided into subgroups with positive (survivors) and negative (worsening condition, non-survivors) outcomes, on Days 1, 3, and 7 after admission to hospital, was measured by NMR spectroscopy. Results: We observed changes in energy metabolism in both groups of COVID-19 patients; initial hyperglycaemia, indicating lowered glucose utilisation, was balanced with increased production of 3-hydroxybutyrate as an alternative energy source and accompanied by accelerated protein catabolism manifested by an increase in BCAA levels. These changes were normalised in patients with positive outcome by the seventh day, but still persisted one week after hospitalisation in patients with negative outcome. The initially decreased glutamine plasma level normalised faster in patients with positive outcome. Patients with negative outcome showed a more pronounced Phe/Tyr ratio, which is related to exacerbated and generalised inflammatory processes. Almost ideal discrimination from controls was proved. Conclusions: Distinct metabolomic responses to severe inflammation initiated by SARS-CoV-2 infection may serve towards complementary personalised pharmacological and nutritional support to improve patient outcomes. Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)
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Review

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39 pages, 21321 KiB  
Review
NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
by Carmelo Corsaro, Sebastiano Vasi, Fortunato Neri, Angela Maria Mezzasalma, Giulia Neri and Enza Fazio
Appl. Sci. 2022, 12(6), 2824; https://doi.org/10.3390/app12062824 - 9 Mar 2022
Cited by 17 | Viewed by 7869
Abstract
NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The [...] Read more.
NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy. Full article
(This article belongs to the Special Issue Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry)
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