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Chrysostomou A, Furlan C, Saccenti E. Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2024:195062. [PMID: 39366464 DOI: 10.1016/j.bbagrm.2024.195062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2024] [Revised: 09/01/2024] [Accepted: 09/24/2024] [Indexed: 10/06/2024]
Abstract
Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencing data and machine learning to classify AML patients and healthy, monocytes, dendritic and progenitor cells population. We found that gene expression profiles of AML patients and healthy controls can be classified at the individual level with high accuracy (>70 %) when using progenitor cells, suggesting the existence of subject-specific single cell transcriptomics profiles. The analysis also revealed molecular determinants of patient heterogeneity (e.g. TPSD1, CT45A1, and GABRA4) which could support new strategies for patient stratification and personalized treatment in leukaemia.
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Affiliation(s)
- Andreas Chrysostomou
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Cristina Furlan
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands.
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2
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Mires S, Sommella E, Merciai F, Salviati E, Caponigro V, Basilicata MG, Marini F, Campiglia P, Baquedano M, Dong T, Skerritt C, Eastwood KA, Caputo M. Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study. Metabolomics 2024; 20:70. [PMID: 38955892 PMCID: PMC11219374 DOI: 10.1007/s11306-024-02129-8] [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: 01/19/2024] [Accepted: 05/08/2024] [Indexed: 07/04/2024]
Abstract
INTRODUCTION Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these. OBJECTIVE To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD. METHODS We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling. RESULTS 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides. CONCLUSIONS Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.
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Affiliation(s)
- Stuart Mires
- Translational Health Sciences, University of Bristol, Bristol, UK.
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.
| | | | | | | | - Vicky Caponigro
- Department of Pharmacy, University of Salerno, Salerno, Italy
| | - Manuela Giovanna Basilicata
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | | | - Mai Baquedano
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Tim Dong
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Clare Skerritt
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Kelly-Ann Eastwood
- Translational Health Sciences, University of Bristol, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Massimo Caputo
- Translational Health Sciences, University of Bristol, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
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3
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Di Cesare F, Calgaro M, Ghini V, Squarzanti DF, De Prisco A, Visciglia A, Zanetta P, Rolla R, Savoia P, Amoruso A, Azzimonti B, Vitulo N, Tenori L, Luchinat C, Pane M. Exploring the Effects of Probiotic Treatment on Urinary and Serum Metabolic Profiles in Healthy Individuals. J Proteome Res 2023; 22:3866-3878. [PMID: 37970754 PMCID: PMC10696601 DOI: 10.1021/acs.jproteome.3c00548] [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: 08/29/2023] [Revised: 10/11/2023] [Accepted: 10/24/2023] [Indexed: 11/17/2023]
Abstract
Probiotics are live microorganisms that confer health benefits when administered in adequate amounts. They are used to promote gut health and alleviate various disorders. Recently, there has been an increasing interest in the potential effects of probiotics on human physiology. In the presented study, the effects of probiotic treatment on the metabolic profiles of human urine and serum using a nuclear magnetic resonance (NMR)-based metabonomic approach were investigated. Twenty-one healthy volunteers were enrolled in the study, and they received two different dosages of probiotics for 8 weeks. During the study, urine and serum samples were collected from volunteers before and during probiotic supplementation. The results showed that probiotics had a significant impact on the urinary and serum metabolic profiles without altering their phenotypes. This study demonstrated the effects of probiotics in terms of variations of metabolite levels resulting also from the different probiotic posology. Overall, the results suggest that probiotic administration may affect both urine and serum metabolomes, although more research is needed to understand the mechanisms and clinical implications of these effects. NMR-based metabonomic analysis of biofluids is a powerful tool for monitoring host-gut microflora dynamic interaction as well as for assessing the individual response to probiotic treatment.
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Affiliation(s)
- Francesca Di Cesare
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, Firenze 50019, Italy
- Department
of Chemistry “Ugo Schiff”, University of Florence, Via della Lastruccia 3, Sesto Fiorentino 50019, Italy
| | - Matteo Calgaro
- Department
of Biotechnology, University of Verona, Strada le Grazie, 15, Verona 37134, Italy
| | - Veronica Ghini
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, Firenze 50019, Italy
- Department
of Chemistry “Ugo Schiff”, University of Florence, Via della Lastruccia 3, Sesto Fiorentino 50019, Italy
| | - Diletta Francesca Squarzanti
- Department
of Health Sciences (DiSS), University of
Piemonte Orientale (UPO), Via Solaroli, 17, Novara 28100, Italy
- Center
for Translational Research on Autoimmune and Allergic Diseases (CAAD),
Department of Health Sciences (DiSS), University
of Piemonte Orientale (UPO), Corso Trieste, 15, Novara 28100, Italy
| | | | | | - Paola Zanetta
- Department
of Health Sciences (DiSS), University of
Piemonte Orientale (UPO), Via Solaroli, 17, Novara 28100, Italy
- Center
for Translational Research on Autoimmune and Allergic Diseases (CAAD),
Department of Health Sciences (DiSS), University
of Piemonte Orientale (UPO), Corso Trieste, 15, Novara 28100, Italy
| | - Roberta Rolla
- Department
of Health Sciences (DiSS), University of
Piemonte Orientale (UPO), Via Solaroli, 17, Novara 28100, Italy
| | - Paola Savoia
- Department
of Health Sciences (DiSS), University of
Piemonte Orientale (UPO), Via Solaroli, 17, Novara 28100, Italy
| | - Angela Amoruso
- Probiotical
Research Srl, Via Enrico
Mattei, 3, Novara 28100, Italy
| | - Barbara Azzimonti
- Department
of Health Sciences (DiSS), University of
Piemonte Orientale (UPO), Via Solaroli, 17, Novara 28100, Italy
- Center
for Translational Research on Autoimmune and Allergic Diseases (CAAD),
Department of Health Sciences (DiSS), University
of Piemonte Orientale (UPO), Corso Trieste, 15, Novara 28100, Italy
| | - Nicola Vitulo
- Department
of Biotechnology, University of Verona, Strada le Grazie, 15, Verona 37134, Italy
| | - Leonardo Tenori
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, Firenze 50019, Italy
- Department
of Chemistry “Ugo Schiff”, University of Florence, Via della Lastruccia 3, Sesto Fiorentino 50019, Italy
- Consorzio
Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Via Luigi Sacconi 6, Sesto Fiorentino, Firenze 50019, Italy
| | - Claudio Luchinat
- Consorzio
Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), Via Luigi Sacconi 6, Sesto Fiorentino, Firenze 50019, Italy
- Giotto
Biotech S.r.l., Via Madonna
del Piano, 6, Sesto Fiorentino, Firenze 50019, Italy
| | - Marco Pane
- Probiotical
Research Srl, Via Enrico
Mattei, 3, Novara 28100, Italy
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Mulder FAA, Tenori L, Licari C, Luchinat C. Practical considerations for rapid and quantitative NMR-based metabolomics. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 352:107462. [PMID: 37141802 DOI: 10.1016/j.jmr.2023.107462] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/23/2023] [Accepted: 04/21/2023] [Indexed: 05/06/2023]
Abstract
NMR is a key technology for metabolomics because of its robustness and reproducibility. Herein we discuss practical considerations that extend the utility of NMR spectroscopy. First, the long T1 spin relaxation times of small molecules limits high-throughput data acquisition because most experimental time is lost while waiting for signal recovery. In principle, the addition of a small amount of commercially-available paramagnetic gadolinium chelate allows cost-effective and efficient high-throughput mixture analysis with correct concentration determination. However, idle time caused by slow temperature regulation during sample exchanges, poses a next constraint. We show how, with proper care, NMR sample scanning times can be reduced additionally by a factor of two. Lastly, we describe how equidistant bucketing is a simple and fast procedure for metabolomic fingerprinting. The combination of these advancements help to make NMR metabolomics more versatile than it is today.
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Affiliation(s)
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy; Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino, Florence, Italy
| | - Cristina Licari
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy; Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino, Florence, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy; Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino, Florence, Italy; GiottoBiotech s.r.l., Sesto Fiorentino, Florence, Italy.
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Cannet C, Bayat A, Frauendienst-Egger G, Freisinger P, Spraul M, Himmelreich N, Kockaya M, Ahring K, Godejohann M, MacDonald A, Trefz F. Phenylketonuria (PKU) Urinary Metabolomic Phenotype Is Defined by Genotype and Metabolite Imbalance: Results in 51 Early Treated Patients Using Ex Vivo 1H-NMR Analysis. Molecules 2023; 28:4916. [PMID: 37446577 DOI: 10.3390/molecules28134916] [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: 04/24/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
Phenylketonuria (PKU) is a rare metabolic disorder caused by mutations in the phenylalanine hydroxylase gene. Depending on the severity of the genetic mutation, medical treatment, and patient dietary management, elevated phenylalanine (Phe) may occur in blood and brain tissues. Research has recently shown that high Phe not only impacts the central nervous system, but also other organ systems (e.g., heart and microbiome). This study used ex vivo proton nuclear magnetic resonance (1H-NMR) analysis of urine samples from PKU patients (mean 14.9 ± 9.2 years, n = 51) to identify the impact of elevated blood Phe and PKU treatment on metabolic profiles. Our results found that 24 out of 98 urinary metabolites showed a significant difference (p < 0.05) for PKU patients compared to age-matched healthy controls (n = 51) based on an analysis of urinary metabolome. These altered urinary metabolites were related to Phe metabolism, dysbiosis, creatine synthesis or intake, the tricarboxylic acid (TCA) cycle, end products of nicotinamide-adenine dinucleotide degradation, and metabolites associated with a low Phe diet. There was an excellent correlation between the metabolome and genotype of PKU patients and healthy controls of 96.7% in a confusion matrix model. Metabolomic investigations may contribute to a better understanding of PKU pathophysiology.
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Affiliation(s)
| | - Allan Bayat
- Kennedy Centre, Center for PKU, 2600 Glostrup, Denmark
| | | | - Peter Freisinger
- Department of Pediatrics, School of Medicine, University of Tübingen, 72074 Tübingen, Germany
| | | | | | - Musa Kockaya
- Private Pediatric Practice, 68307 Mannheim, Germany
| | | | | | - Anita MacDonald
- Dietetic Department, Birmingham Children's Hospital, Birmingham B4 6NH, UK
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6
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Mires S, Reddy S, Skerritt C, Caputo M, Eastwood K. Maternal metabolomic profiling and congenital heart disease risk in offspring: A systematic review of observational studies. Prenat Diagn 2023; 43:647-660. [PMID: 36617630 PMCID: PMC10946495 DOI: 10.1002/pd.6301] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/19/2022] [Accepted: 01/02/2023] [Indexed: 01/10/2023]
Abstract
Aetiological understanding and screening methods for congenital heart disease (CHD) are limited. Maternal metabolomic assessment offers the potential to identify risk factors and biomarkers. We performed a systematic review (PROSPERO CRD42022308452) investigating the association between fetal/childhood CHD and endogenous maternal metabolites. Ovid-MEDLINE, Ovid-EMBASE and Cochrane Library were searched between inception and 06/09/2022. Case control studies included analysing maternal blood or urine metabolites in pregnancy or postpartum where there was foetal/childhood CHD. Risk of bias assessment utilised the Scottish Intercollegiate Guidelines Network methodology checklist and narrative synthesis was performed. A total of 134 records were screened with eight eligible studies (n = 3242 pregnancies, n = 842 CHD-affected offspring). Five studies performed metabolomic analysis in pregnancy. Metabolites distinguishing case and control groups spanned lipid, glucose and amino-acid pathways, with the development of sensitive risk prediction models. No single metabolite consistently distinguished cases and controls across studies. Three studies performed targeted analysis postnatally with altered lipid and amino acid metabolites and raised homocysteine and markers of oxidative stress identified in cases. Included studies reported small sample sizes, analysing different biosamples at variable time points using differing techniques. At present, there is not enough evidence to confidently associate maternal metabolomic profiles with offspring CHD risk. However, several identified pathways warrant further investigation.
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Affiliation(s)
- Stuart Mires
- University of BristolBristolUK
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
| | - Snigdha Reddy
- Birmingham Women's and Children's NHS Foundation TrustBirminghamUK
| | - Clare Skerritt
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
| | - Massimo Caputo
- University of BristolBristolUK
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
| | - Kelly‐Ann Eastwood
- University of BristolBristolUK
- University Hospitals Bristol and Weston NHS Foundation TrustBristolUK
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7
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Derveaux E, Geubbelmans M, Criel M, Demedts I, Himpe U, Tournoy K, Vercauter P, Johansson E, Valkenborg D, Vanhove K, Mesotten L, Adriaensens P, Thomeer M. NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15072127. [PMID: 37046788 PMCID: PMC10093525 DOI: 10.3390/cancers15072127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
Background: Lung cancer can be detected by measuring the patient’s plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient’s metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor’s presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.
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Affiliation(s)
- Elien Derveaux
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium
- Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Agoralaan 1—Building D, B-3590 Diepenbeek, Belgium
| | - Melvin Geubbelmans
- Data Science Institute, Hasselt University, Agoralaan 1, B-3590 Diepenbeek, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan 1, B-3590 Diepenbeek, Belgium
| | - Maarten Criel
- Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Schiepse Bos 6, B-3600 Genk, Belgium
| | - Ingel Demedts
- Department of Respiratory Medicine, AZ Delta, Deltalaan 1, B-8800 Roeselare, Belgium
| | - Ulrike Himpe
- Department of Respiratory Medicine, AZ Delta, Deltalaan 1, B-8800 Roeselare, Belgium
| | - Kurt Tournoy
- Department of Respiratory Medicine, Onze-Lieve-Vrouw Ziekenhuis, Moorselbaan 164, B-9300 Aalst, Belgium
- Faculty of Medicine and Health Sciences, Ghent University, De Pintelaan 85, B-9000 Ghent, Belgium
| | - Piet Vercauter
- Department of Respiratory Medicine, Onze-Lieve-Vrouw Ziekenhuis, Moorselbaan 164, B-9300 Aalst, Belgium
| | - Erik Johansson
- Sartorius Stedim Data Analytics AB, Östra Strandgatan 24, 903 33 Umeå, Sweden
| | - Dirk Valkenborg
- Data Science Institute, Hasselt University, Agoralaan 1, B-3590 Diepenbeek, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan 1, B-3590 Diepenbeek, Belgium
| | - Karolien Vanhove
- Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Agoralaan 1—Building D, B-3590 Diepenbeek, Belgium
- Department of Respiratory Medicine, AZ Vesalius, Hazelereik 51, B-3700 Tongeren, Belgium
| | - Liesbet Mesotten
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium
- Department of Nuclear Medicine, Ziekenhuis Oost-Limburg, Schiepse Bos 6, B-3600 Genk, Belgium
| | - Peter Adriaensens
- Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Agoralaan 1—Building D, B-3590 Diepenbeek, Belgium
| | - Michiel Thomeer
- Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, B-3500 Hasselt, Belgium
- Department of Respiratory Medicine, Ziekenhuis Oost-Limburg, Schiepse Bos 6, B-3600 Genk, Belgium
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Grant CW, Wilton AR, Kaddurah-Daouk R, Skime M, Biernacka J, Mayes T, Carmody T, Wang L, Lazaridis K, Weinshilboum R, Bobo WV, Trivedi MH, Croarkin PE, Athreya AP. Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder. Front Pharmacol 2022; 13:984383. [PMID: 36263124 PMCID: PMC9573988 DOI: 10.3389/fphar.2022.984383] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.
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Affiliation(s)
- Caroline W. Grant
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Angelina R. Wilton
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Department of Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC, United States
| | - Michelle Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Joanna Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Taryn Mayes
- Peter O’Donnell Jr. Brain Institute and the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Thomas Carmody
- Department Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Konstantinos Lazaridis
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - William V. Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, United States
| | - Madhukar H. Trivedi
- Peter O’Donnell Jr. Brain Institute and the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
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9
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Cui D, Kong L, Wang Y, Zhu Y, Zhang C. In situ identification of environmental microorganisms with Raman spectroscopy. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2022; 11:100187. [PMID: 36158754 PMCID: PMC9488013 DOI: 10.1016/j.ese.2022.100187] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/13/2022] [Accepted: 05/15/2022] [Indexed: 05/28/2023]
Abstract
Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.
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Affiliation(s)
- Dongyu Cui
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Lingchao Kong
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science & Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yi Wang
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yuanqing Zhu
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai Earthquake Agency, Shanghai, 200062, China
| | - Chuanlun Zhang
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, University of Southern University of Science and Technology, Shenzhen, 518055, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai Earthquake Agency, Shanghai, 200062, China
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10
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Chamberlain M, O'Flaherty S, Cobián N, Barrangou R. Metabolomic Analysis of Lactobacillus acidophilus, L. gasseri, L. crispatus, and Lacticaseibacillus rhamnosus Strains in the Presence of Pomegranate Extract. Front Microbiol 2022; 13:863228. [PMID: 35663851 PMCID: PMC9160967 DOI: 10.3389/fmicb.2022.863228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 04/06/2022] [Indexed: 11/20/2022] Open
Abstract
Lactobacillus species are prominent inhabitants of the human gastrointestinal tract that contribute to maintaining a balanced microbial environment that positively influences host health. These bacterial populations can be altered through use of probiotic supplements or via dietary changes which in turn affect the host health. Utilizing polyphenolic compounds to selectively stimulate the growth of commensal bacteria can have a positive effect on the host through the production of numerous metabolites that are biologically active. Four Lactobacillus strains were grown in the presence of pomegranate (POM) extract. Two strains, namely, L. acidophilus NCFM and L. rhamnosus GG, are commonly used probiotics, while the other two strains, namely, L. crispatus NCK1351 and L. gasseri NCK1342, exhibit probiotic potential. To compare and contrast the impact of POM on the strains' metabolic capacity, we investigated the growth of the strains with and without the presence of POM and identified their carbohydrate utilization and enzyme activity profiles. To further investigate the differences between strains, an untargeted metabolomic approach was utilized to quantitatively and qualitatively define the metabolite profiles of these strains. Several metabolites were produced significantly and/or exclusively in some of the strains, including mevalonate, glutamine, 5-aminoimidazole-4-carboxamide, phenyllactate, and fumarate. The production of numerous discrete compounds illustrates the unique characteristics of and diversity between strains. Unraveling these differences is essential to understand the probiotic function and help inform strain selection for commercial product formulation.
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Affiliation(s)
- MaryClaire Chamberlain
- Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Raleigh, NC, United States
| | - Sarah O'Flaherty
- Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Raleigh, NC, United States
| | - Natalia Cobián
- Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Raleigh, NC, United States
| | - Rodolphe Barrangou
- Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, Raleigh, NC, United States
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11
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Hussain H, Vutipongsatorn K, Jiménez B, Antcliffe DB. Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response. Metabolites 2022; 12:metabo12050376. [PMID: 35629881 PMCID: PMC9145582 DOI: 10.3390/metabo12050376] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/01/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.
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Affiliation(s)
- Humma Hussain
- Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (H.H.); (K.V.)
| | - Kritchai Vutipongsatorn
- Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (H.H.); (K.V.)
| | - Beatriz Jiménez
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK;
- National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK
| | - David B. Antcliffe
- Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK; (H.H.); (K.V.)
- Correspondence:
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12
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Ghini V, Maggi L, Mazzoni A, Spinicci M, Zammarchi L, Bartoloni A, Annunziato F, Turano P. Serum NMR Profiling Reveals Differential Alterations in the Lipoproteome Induced by Pfizer-BioNTech Vaccine in COVID-19 Recovered Subjects and Naïve Subjects. Front Mol Biosci 2022; 9:839809. [PMID: 35480886 PMCID: PMC9037139 DOI: 10.3389/fmolb.2022.839809] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/07/2022] [Indexed: 12/16/2022] Open
Abstract
1H NMR spectra of sera have been used to define the changes induced by vaccination with Pfizer-BioNTech vaccine (2 shots, 21 days apart) in 10 COVID-19-recovered subjects and 10 COVID-19-naïve subjects at different time points, starting from before vaccination, then weekly until 7 days after second injection, and finally 1 month after the second dose. The data show that vaccination does not induce any significant variation in the metabolome, whereas it causes changes at the level of lipoproteins. The effects are different in the COVID-19-recovered subjects with respect to the naïve subjects, suggesting that a previous infection reduces the vaccine modulation of the lipoproteome composition.
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Affiliation(s)
- Veronica Ghini
- Department of Chemistry, University of Florence, Florence, Italy
- Magnetic Resonance Center (CERM), University of Florence, Florence, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Florence, Italy
| | - Laura Maggi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Alessio Mazzoni
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Michele Spinicci
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Infectious and Tropical Disease Unit, Careggi University Hospital, Florence, Italy
| | - Lorenzo Zammarchi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Infectious and Tropical Disease Unit, Careggi University Hospital, Florence, Italy
| | - Alessandro Bartoloni
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Infectious and Tropical Disease Unit, Careggi University Hospital, Florence, Italy
| | - Francesco Annunziato
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- Flow Cytometry Diagnostic Center and Immunotherapy, Careggi University Hospital, Florence, Italy
| | - Paola Turano
- Department of Chemistry, University of Florence, Florence, Italy
- Magnetic Resonance Center (CERM), University of Florence, Florence, Italy
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Florence, Italy
- *Correspondence: Paola Turano,
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13
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Campbell CD, Barnett C, Sulaiman I. A clinicians’ review of the respiratory microbiome. Breathe (Sheff) 2022; 18:210161. [PMID: 36338247 PMCID: PMC9584600 DOI: 10.1183/20734735.0161-2021] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/02/2022] [Indexed: 11/25/2022] Open
Abstract
The respiratory microbiome and its impact in health and disease is now well characterised. With the development of next-generation sequencing and the use of other techniques such as metabolomics, the functional impact of microorganisms in different host environments can be elucidated. It is now clear that the respiratory microbiome plays an important role in respiratory disease. In some diseases, such as bronchiectasis, examination of the microbiome can even be used to identify patients at higher risk of poor outcomes. Furthermore, the microbiome can aid in phenotyping. Finally, development of multi-omic analysis has revealed interactions between the host and microbiome in some conditions. This review, although not exhaustive, aims to outline how the microbiome is investigated, the healthy respiratory microbiome and its role in respiratory disease. The respiratory microbiome encompasses bacterial, fungal and viral communities. In health, it is a dynamic structure and dysbiotic in disease. Dysbiosis can be related to disease severity and may be utilised to predict patients at clinical risk.https://bit.ly/3pNSgnA
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14
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Meoni G, Tenori L, Schade S, Licari C, Pirazzini C, Bacalini MG, Garagnani P, Turano P, Trenkwalder C, Franceschi C, Mollenhauer B, Luchinat C. Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients. NPJ Parkinsons Dis 2022; 8:14. [PMID: 35136088 PMCID: PMC8826921 DOI: 10.1038/s41531-021-00274-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 12/16/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.
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Affiliation(s)
- Gaia Meoni
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Sesto Fiorentino, Florence, Italy
| | - Sebastian Schade
- Department of Clinical Neurophysiology, University Medical Center Goettingen, Goettingen, Germany
| | - Cristina Licari
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy
| | - Chiara Pirazzini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | | | - Paolo Garagnani
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Sesto Fiorentino, Florence, Italy
| | | | - Claudia Trenkwalder
- University Medical Center Goettingen, Department of Neurology and Paracelsus-Elena-Klinik, Kassel, Germany
| | - Claudio Franceschi
- Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy. .,Laboratory of Systems Medicine of Healthy Aging and Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia.
| | - Brit Mollenhauer
- University Medical Center Goettingen, Department of Neurology and Paracelsus-Elena-Klinik, Kassel, Germany.
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Florence, Italy. .,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (C.I.R.M.M.P.), Sesto Fiorentino, Florence, Italy.
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15
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Saffari A, Cannet C, Blaschek A, Hahn A, Hoffmann GF, Johannsen J, Kirsten R, Kockaya M, Kölker S, Müller-Felber W, Roos A, Schäfer H, Schara U, Spraul M, Trefz FK, Vill K, Wick W, Weiler M, Okun JG, Ziegler A. 1H-NMR-based metabolic profiling identifies non-invasive diagnostic and predictive urinary fingerprints in 5q spinal muscular atrophy. Orphanet J Rare Dis 2021; 16:441. [PMID: 34670613 PMCID: PMC8527822 DOI: 10.1186/s13023-021-02075-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 10/10/2021] [Indexed: 11/13/2022] Open
Abstract
Background 5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of 1H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA. Results Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool. Conclusions This study provides preliminary evidence for the use of 1H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy. Supplementary Information The online version contains supplementary material available at 10.1186/s13023-021-02075-x.
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Affiliation(s)
- Afshin Saffari
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany
| | | | - Astrid Blaschek
- Division of Pediatric Neurology and Developmental Medicine and LMU Center for Children With Medical Complexity, LMU Hospital, Dr. von Hauner Children's Hospital, Munich, Germany
| | - Andreas Hahn
- Department of Child Neurology, University Hospital Gießen, Gießen, Germany
| | - Georg F Hoffmann
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany
| | - Jessika Johannsen
- Department of Pediatrics, Neuropediatrics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Romy Kirsten
- NCT Liquidbank, National Center for Tumor Diseases, Heidelberg, Germany
| | | | - Stefan Kölker
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany
| | - Wolfgang Müller-Felber
- Division of Pediatric Neurology and Developmental Medicine and LMU Center for Children With Medical Complexity, LMU Hospital, Dr. von Hauner Children's Hospital, Munich, Germany
| | - Andreas Roos
- Department of Neuropediatrics, Developmental Neurology and Social Pediatrics, Centre for Neuromuscular Disorders in Children, Children's University Clinic Essen, University of Duisburg-Essen, Essen, Germany
| | | | - Ulrike Schara
- Department of Neuropediatrics, Developmental Neurology and Social Pediatrics, Centre for Neuromuscular Disorders in Children, Children's University Clinic Essen, University of Duisburg-Essen, Essen, Germany
| | | | - Friedrich K Trefz
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany
| | - Katharina Vill
- Division of Pediatric Neurology and Developmental Medicine and LMU Center for Children With Medical Complexity, LMU Hospital, Dr. von Hauner Children's Hospital, Munich, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Markus Weiler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen G Okun
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany
| | - Andreas Ziegler
- Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine, Heidelberg University Hospital, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany.
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16
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Bermingham KM, Brennan L, Segurado R, Barron RE, Gibney ER, Ryan MF, Gibney MJ, O'Sullivan AM. Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study. J Proteome Res 2021; 20:3992-4000. [PMID: 34304563 PMCID: PMC8397426 DOI: 10.1021/acs.jproteome.1c00319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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Genes, sex, age,
diet, lifestyle, gut microbiome, and multiple
other factors affect human metabolomic profiles. Understanding metabolomic
variation is critical in human nutrition research as metabolites that
are sensitive to change versus those that are more stable might be
more informative for a particular study design. This study aims to
identify stable metabolomic regions and determine the genetic and
environmental contributions to stability. Using a classic twin design, 1H nuclear magnetic resonance (NMR) urinary metabolomic profiles
were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate
mixed models identified stable urinary metabolites with intraclass
correlation coefficients ≥0.51. Longitudinal twin modeling
measured the contribution of genetic and environmental influences
to variation in the stable urinary NMR metabolome, comprising stable
metabolites. The conservation of an individual’s stable urinary
NMR metabolome over time was assessed by calculating conservation
indices. In this study, 20% of the urinary NMR metabolome is stable
over 2 months (intraclass correlation (ICC) 0.51–0.65). Common
genetic and shared environmental factors contributed to variance in
the stable urinary NMR metabolome over time. Using the stable metabolome,
91% of individuals had good metabolomic conservation indices ≥0.70.
To conclude, this research identifies 20% of the urinary NMR metabolome
as stable, improves our knowledge of the sources of metabolomic variation
over time, and demonstrates the conservation of an individual’s
urinary NMR metabolome.
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Affiliation(s)
- Kate M Bermingham
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Lorraine Brennan
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Ricardo Segurado
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Rebecca E Barron
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Eileen R Gibney
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Miriam F Ryan
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Michael J Gibney
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
| | - Aifric M O'Sullivan
- UCD Institute of Food and health, School of Agriculture and Food Science, University College Dublin, Belfield Dublin 4, Ireland
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17
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Mal TK, Tian Y, Patterson AD. Sample Preparation and Data Analysis for NMR-Based Metabolomics. Methods Mol Biol 2021; 2194:301-313. [PMID: 32926373 DOI: 10.1007/978-1-0716-0849-4_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
NMR spectroscopy has become one of the preferred analytical techniques for metabolomics studies due to its inherent nondestructive nature, ability to identify and quantify metabolites simultaneously in a complex mixture, minimal sample preparation requirement, and high degree of experimental reproducibility. NMR-based metabolomics studies involve the measurement and multivariate statistical analysis of metabolites present in biological samples such as biofluids, stool/feces, intestinal content, tissue, and cell extracts by high-resolution NMR spectroscopy-the goal then is to identify and quantify metabolites and evaluate changes of metabolite concentrations in response to some perturbation. Here we describe methodologies for NMR sample preparation of biofluids (serum, saliva, and urine) and stool/feces, intestinal content, and tissues for NMR experiments including extraction of polar metabolites and application of NMR in metabolomics studies. One dimensional (1D) 1H NMR experiments with different variations such as pre-saturation, relaxation-edited, and diffusion-edited are routinely acquired for profiling and metabolite identification and quantification. 2D homonuclear 1H-1H TOCSY and COSY, 2D J-resolved, and heteronuclear 1H-13C HSQC and HMBC are also performed to assist with metabolite identification and quantification. The NMR data are then subjected to targeted and/or untargeted multivariate statistical analysis for biomarker discovery, clinical diagnosis, toxicological studies, molecular phenotyping, and functional genomics.
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Affiliation(s)
- Tapas K Mal
- Department of Chemistry, Pennsylvania State University, University Park, PA, USA.
| | - Yuan Tian
- Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
| | - Andrew D Patterson
- Department of Veterinary and Biomedical Sciences, Pennsylvania State University, University Park, PA, USA
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18
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Jahagirdar S, Saccenti E. Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine. J Proteome Res 2020; 20:932-949. [PMID: 33267585 PMCID: PMC7786380 DOI: 10.1021/acs.jproteome.0c00696] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Networks
and network analyses are fundamental tools of systems
biology. Networks are built by inferring pair-wise relationships among
biological entities from a large number of samples such that subject-specific
information is lost. The possibility of constructing these sample
(individual)-specific networks from single molecular profiles might
offer new insights in systems and personalized medicine and as a consequence
is attracting more and more research interest. In this study, we evaluated
and compared LIONESS (Linear Interpolation to Obtain Network Estimates
for Single Samples) and ssPCC (single sample network based on Pearson
correlation) in the metabolomics context of metabolite–metabolite
association networks. We illustrated and explored the characteristics
of these two methods on (i) simulated data, (ii) data generated from
a dynamic metabolic model to simulate real-life observed metabolite
concentration profiles, and (iii) 22 metabolomic data sets and (iv)
we applied single sample network inference to a study case pertaining
to the investigation of necrotizing soft tissue infections to show
how these methods can be applied in metabolomics. We also proposed
some adaptations of the methods that can be used for data exploration.
Overall, despite some limitations, we found single sample networks
to be a promising tool for the analysis of metabolomics data.
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Affiliation(s)
- Sanjeevan Jahagirdar
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
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19
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Ghini V, Tenori L, Pane M, Amoruso A, Marroncini G, Squarzanti DF, Azzimonti B, Rolla R, Savoia P, Tarocchi M, Galli A, Luchinat C. Effects of Probiotics Administration on Human Metabolic Phenotype. Metabolites 2020; 10:metabo10100396. [PMID: 33036487 PMCID: PMC7601401 DOI: 10.3390/metabo10100396] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/24/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023] Open
Abstract
The establishment of the beneficial interactions between the host and its microbiota is essential for the correct functioning of the organism, since microflora alterations can lead to many diseases. Probiotics improve balanced microbial communities, exerting substantial health-promoting effects. Here we monitored the molecular outcomes, obtained by gut microflora modulation through probiotic treatment, on human urine and serum metabolic profiles, with a metabolomic approach. Twenty-two subjects were enrolled in the study and administered with two different probiotic types, both singularly and in combination, for 8 weeks. Urine and serum samples were collected before and during the supplementation and were analyzed by nuclear magnetic resonance (NMR) spectroscopy and statistical analyses. After eight weeks of treatment, probiotics deeply influence the urinary metabolic profiles of the volunteers, without significantly altering their single phenotypes. Anyway, bacteria supplementation tends to reduce the differences in metabolic phenotypes among individuals. Overall, the effects are recipient-dependent, and in some individuals, robust effects are already well visible after four weeks. Modifications in metabolite levels, attributable to each type of probiotic administration, were also monitored. Metabolomic analysis of biofluids turns out to be a powerful technique to monitor the dynamic interactions between the microflora and the host, and the individual response to probiotic assumption.
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Affiliation(s)
- Veronica Ghini
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy;
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy;
- Department of Chemistry, University of Florence, 50019 Sesto Fiorentino, Italy
| | - Marco Pane
- Probiotical S.p.A., 28100 Novara, Italy; (M.P.); (A.A.)
| | | | - Giada Marroncini
- Department of Experimental and Clinical Biochemical Sciences “Mario Serio”, University of Florence, 50100 Firenze, Italy; (G.M.); (M.T.); (A.G.)
| | - Diletta Francesca Squarzanti
- Department of Health Sciences (DiSS), University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy; (D.F.S.); (B.A.); (R.R.); (P.S.)
- Center for Translational Research on Autoimmune and Allergic Diseases (CAAD), DiSS, UPO, Corso Trieste 15/A, 28100 Novara, Italy
| | - Barbara Azzimonti
- Department of Health Sciences (DiSS), University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy; (D.F.S.); (B.A.); (R.R.); (P.S.)
- Center for Translational Research on Autoimmune and Allergic Diseases (CAAD), DiSS, UPO, Corso Trieste 15/A, 28100 Novara, Italy
| | - Roberta Rolla
- Department of Health Sciences (DiSS), University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy; (D.F.S.); (B.A.); (R.R.); (P.S.)
- Clinical Chemistry Unit, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100 Novara, Italy
| | - Paola Savoia
- Department of Health Sciences (DiSS), University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy; (D.F.S.); (B.A.); (R.R.); (P.S.)
- SCDU Dermatology, AOU Maggiore della Carità, 28100 Novara, Italy
| | - Mirko Tarocchi
- Department of Experimental and Clinical Biochemical Sciences “Mario Serio”, University of Florence, 50100 Firenze, Italy; (G.M.); (M.T.); (A.G.)
| | - Andrea Galli
- Department of Experimental and Clinical Biochemical Sciences “Mario Serio”, University of Florence, 50100 Firenze, Italy; (G.M.); (M.T.); (A.G.)
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), 50019 Sesto Fiorentino, Italy;
- Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy;
- Department of Chemistry, University of Florence, 50019 Sesto Fiorentino, Italy
- Correspondence: ; Tel.: +39-055-457-4296
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On the Use of Correlation and MI as a Measure of Metabolite-Metabolite Association for Network Differential Connectivity Analysis. Metabolites 2020; 10:metabo10040171. [PMID: 32344593 PMCID: PMC7241243 DOI: 10.3390/metabo10040171] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/15/2020] [Accepted: 04/22/2020] [Indexed: 02/06/2023] Open
Abstract
Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite-metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson's or Spearman's correlation when the application is to quantify and detect differentially connected metabolites.
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Pei J, Li F, Xie Y, Liu J, Yu T, Feng X. Microbial and metabolomic analysis of gingival crevicular fluid in general chronic periodontitis patients: lessons for a predictive, preventive, and personalized medical approach. EPMA J 2020; 11:197-215. [PMID: 32547651 PMCID: PMC7272536 DOI: 10.1007/s13167-020-00202-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 03/10/2020] [Indexed: 12/14/2022]
Abstract
Objectives General chronic periodontitis (GCP) is a bacterial inflammatory disease with complex pathology. Despite extensive studies published on the variation in the oral microbiota and metabolic profiles of GCP patients, information is lacking regarding the correlation between host-bacterial interactions and biochemical metabolism. This study aimed to analyze the oral microbiome, the oral metabolome, and the link between them and to identify potential molecules as useful biomarkers for predictive, preventive, and personalized medicine (PPPM) in GCP. Methods In this study, gingival crevicular fluid (GCF) samples were collected from patients with GCP (n = 30) and healthy controls (n = 28). The abundance of oral microbiota constituents was obtained by Illumina sequencing, and the relative level of metabolites was measured by gas chromatography-mass spectrometry. Full-mouth probing depth, clinical attachment loss, and bleeding on probing were recorded as indices of periodontal disease. Results The relative abundances of 7 phyla and 82 genera differed significantly between the GCP and healthy groups. Seventeen differential metabolites involved in different metabolism pathways were selected based on variable influence on projection values (VIP > 1) and P values (P < 0.05). Through Spearman's correlation analysis, microorganisms, metabolites in GCF, and clinical data together showed a clear trend, and clinical data regarding periodontitis can be reflected in the shift of the oral microbial community and the change in metabolites in GCF. A combination of citramalic acid and N-carbamylglutamate yielded satisfactory accuracy (AUC = 0.876) for the predictive diagnosis of GCP. Conclusions Dysbiosis in the polymicrobial community structure and changes in metabolism could be mechanisms underlying periodontitis. The differential microorganisms and metabolites in GCF between periodontitis patients and healthy individuals are possibly biomarkers, pointing to a potential strategy for the prediction, diagnosis, prognosis, and management of personalized periodontal therapy.
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Affiliation(s)
- Jun Pei
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200000 China.,National Clinical Research Center for Oral Diseases, Shanghai, 200000 China.,Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, 200000 China
| | - Fei Li
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200000 China.,National Clinical Research Center for Oral Diseases, Shanghai, 200000 China.,Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, 200000 China
| | - Youhua Xie
- Key Lab of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University, Shanghai, 200000 China
| | - Jing Liu
- Key Lab of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University, Shanghai, 200000 China
| | - Tian Yu
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200000 China.,National Clinical Research Center for Oral Diseases, Shanghai, 200000 China.,Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, 200000 China
| | - Xiping Feng
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200000 China.,National Clinical Research Center for Oral Diseases, Shanghai, 200000 China.,Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, 200000 China
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22
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Willis JR, Gabaldón T. The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems. Microorganisms 2020; 8:microorganisms8020308. [PMID: 32102216 PMCID: PMC7074908 DOI: 10.3390/microorganisms8020308] [Citation(s) in RCA: 216] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 02/14/2020] [Accepted: 02/16/2020] [Indexed: 02/07/2023] Open
Abstract
Abstract: The human oral cavity is home to an abundant and diverse microbial community (i.e., the oral microbiome), whose composition and roles in health and disease have been the focus of intense research in recent years. Thanks to developments in sequencing-based approaches, such as 16S ribosomal RNA metabarcoding, whole metagenome shotgun sequencing, or meta-transcriptomics, we now can efficiently explore the diversity and roles of oral microbes, even if unculturable. Recent sequencing-based studies have charted oral ecosystems and how they change due to lifestyle or disease conditions. As studies progress, there is increasing evidence of an important role of the oral microbiome in diverse health conditions, which are not limited to diseases of the oral cavity. This, in turn, opens new avenues for microbiome-based diagnostics and therapeutics that benefit from the easy accessibility of the oral cavity for microbiome monitoring and manipulation. Yet, many challenges remain ahead. In this review, we survey the main sequencing-based methodologies that are currently used to explore the oral microbiome and highlight major findings enabled by these approaches. Finally, we discuss future prospects in the field.
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Affiliation(s)
- Jesse R. Willis
- Barcelona Supercomputing Centre (BCS-CNS), Jordi Girona, 29., 08034 Barcelona, Spain
- Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology (BIST), 08034 Barcelona, Spain
| | - Toni Gabaldón
- Barcelona Supercomputing Centre (BCS-CNS), Jordi Girona, 29., 08034 Barcelona, Spain
- Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology (BIST), 08034 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010 Barcelona, Spain
- Correspondence:
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23
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Systems and Precision Medicine in Necrotizing Soft Tissue Infections. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1294:187-207. [PMID: 33079370 DOI: 10.1007/978-3-030-57616-5_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.
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24
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DHA-Induced Perturbation of Human Serum Metabolome. Role of the Food Matrix and Co-Administration of Oat β-glucan and Anthocyanins. Nutrients 2019; 12:nu12010086. [PMID: 31892215 PMCID: PMC7019822 DOI: 10.3390/nu12010086] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/18/2019] [Accepted: 12/23/2019] [Indexed: 12/16/2022] Open
Abstract
Docosahexaenoic acid (DHA) has been reported to have a positive impact on many diet-related disease risks, including metabolic syndrome. Although many DHA-enriched foods have been marketed, the impact of different food matrices on the effect of DHA is unknown. As well, the possibility to enhance DHA effectiveness through the co-administration of other bioactives has seldom been considered. We evaluated DHA effects on the serum metabolome administered to volunteers at risk of metabolic syndrome as an ingredient of three different foods. Foods were enriched with DHA alone or in combination with oat beta-glucan or anthocyanins and were administered to volunteers for 4 weeks. Serum samples collected at the beginning and end of the trial were analysed by NMR-based metabolomics. Multivariate and univariate statistical analyses were used to characterize modifications in the serum metabolome and to evaluate bioactive-bioactive and bioactive-food matrix interactions. DHA administration induces metabolome perturbation that is influenced by the food matrix and the co-presence of other bioactives. In particular, when co-administered with oat beta-glucan, DHA induces a strong rearrangement in the lipoprotein profile of the subjects. The observed modifications are consistent with clinical results and indicate that metabolomics represents a possible strategy to choose the most appropriate food matrices for bioactive enrichment.
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25
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Hughes RL, Kable ME, Marco M, Keim NL. The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models. Part II: Results. Adv Nutr 2019; 10:979-998. [PMID: 31225587 PMCID: PMC6855959 DOI: 10.1093/advances/nmz049] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/28/2019] [Accepted: 04/12/2019] [Indexed: 12/17/2022] Open
Abstract
The gut microbiota is increasingly implicated in the health and metabolism of its human host. The host's diet is a major component influencing the composition and function of the gut microbiota, and mounting evidence suggests that the composition and function of the gut microbiota influence the host's metabolic response to diet. This effect of the gut microbiota on personalized dietary response is a growing focus of precision nutrition research and may inform the effort to tailor dietary advice to the individual. Because the gut microbiota has been shown to be malleable to some extent, it may also allow for therapeutic alterations of the gut microbiota in order to alter response to certain dietary components. This article is the second in a 2-part review of the current research in the field of precision nutrition incorporating the gut microbiota into studies investigating interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as analyze the results subsequently obtained. Part II reviews the findings of these studies and discusses the gaps in our current knowledge and directions for future research. The studies reviewed provide the current understanding in this field of research and a foundation from which we may build, utilizing and expanding upon the methods and results they present to inform future studies.
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Affiliation(s)
- Riley L Hughes
- Departments of Nutrition, Food Science & Technology, University of California, Davis, CA
| | - Mary E Kable
- Departments of Nutrition, Food Science & Technology, University of California, Davis, CA,Departments of Immunity and Disease Prevention, Obesity and Metabolism, Western Human Nutrition Research Center, Agricultural Research Service, USDA, Davis, CA
| | - Maria Marco
- Food Science & Technology, University of California, Davis, CA
| | - Nancy L Keim
- Departments of Nutrition, Food Science & Technology, University of California, Davis, CA,Obesity and Metabolism, Western Human Nutrition Research Center, Agricultural Research Service, USDA, Davis, CA,Address correspondence to NLK (e-mail: )
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27
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Winkelmann O, Küchler T. Reliable Classification of Olive Oil Origin Based on Minor Component Profile Using
1
H‐NMR and Multivariate Analysis. EUR J LIPID SCI TECH 2019. [DOI: 10.1002/ejlt.201900027] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Ole Winkelmann
- Eurofins Analytik GmbH Neuländer Kamp 1 21079 Hamburg Germany
| | - Torben Küchler
- Eurofins Analytik GmbH Neuländer Kamp 1 21079 Hamburg Germany
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28
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Emwas AH, Roy R, McKay RT, Tenori L, Saccenti E, Gowda GAN, Raftery D, Alahmari F, Jaremko L, Jaremko M, Wishart DS. NMR Spectroscopy for Metabolomics Research. Metabolites 2019; 9:E123. [PMID: 31252628 PMCID: PMC6680826 DOI: 10.3390/metabo9070123] [Citation(s) in RCA: 519] [Impact Index Per Article: 103.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/14/2019] [Accepted: 06/18/2019] [Indexed: 12/14/2022] Open
Abstract
Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.
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Affiliation(s)
- Abdul-Hamid Emwas
- Core Labs, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Raja Roy
- Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Uttar Pradesh 226014, India
| | - Ryan T McKay
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2W2, Canada
| | - Leonardo Tenori
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
- Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue, Seattle, WA 98109, USA
| | - Fatimah Alahmari
- Department of NanoMedicine Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia
| | - Lukasz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Mariusz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
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Ghini V, Quaglio D, Luchinat C, Turano P. NMR for sample quality assessment in metabolomics. N Biotechnol 2019; 52:25-34. [PMID: 31022482 DOI: 10.1016/j.nbt.2019.04.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 04/15/2019] [Accepted: 04/21/2019] [Indexed: 12/11/2022]
Abstract
The EU Framework 7 project SPIDIA was the occasion for development of NMR approaches to evaluate the impact of different pre-analytical treatments on the quality of biological samples dedicated to metabolomics. Systematic simulation of different pre-analytical procedures was performed on urine and blood serum and plasma. Here we review the key aspects of these studies that have led to the development of CEN technical specifications, to be translated into ISO/IS in the course of the EU Horizon 2020 project SPIDIA4P. Inspired by the SPIDIA results, follow-up research was performed, extending the analysis to different sample types and to the different effects of long-term storage. The latter activity was in conjunction with the local European da Vinci Biobank. These results (which partially contributed to the ANNEX of CEN/TS 16945"MOLECULAR IN VITRO DIAGNOSTIC EXAMINATIONS - SPECIFICATIONS FOR PRE-EXAMINATION PROCESSES FOR METABOLOMICS IN URINE, VENOUS BLOOD SERUM AND PLASMA") are presented in detail.
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Affiliation(s)
- Veronica Ghini
- Center of Magnetic Resonance (CERM), University of Florence, Sesto Fiorentino FI, Italy
| | - Deborah Quaglio
- Department of Chemistry and Technology of Drugs, Sapienza University of Rome, Rome, Italy
| | - Claudio Luchinat
- Center of Magnetic Resonance (CERM), University of Florence, Sesto Fiorentino FI, Italy; Department of Chemistry, University of Florence, Sesto Fiorentino FI, Italy
| | - Paola Turano
- Center of Magnetic Resonance (CERM), University of Florence, Sesto Fiorentino FI, Italy; Department of Chemistry, University of Florence, Sesto Fiorentino FI, Italy.
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Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry. Anal Bioanal Chem 2019; 411:4883-4898. [PMID: 30989265 PMCID: PMC6611759 DOI: 10.1007/s00216-019-01764-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 02/25/2019] [Accepted: 03/06/2019] [Indexed: 01/27/2023]
Abstract
Despite the attractiveness of breath analysis as a non-invasive means to retrieve relevant metabolic information, its introduction into routine clinical practice remains a challenge. Among all the different analytical techniques available to interrogate exhaled breath, secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) offers a number of advantages (e.g., real-time, yet wide, metabolome coverage) that makes it ideal for untargeted and targeted studies. However, so far, SESI-HRMS has relied mostly on lab-built prototypes, making it difficult to standardize breath sampling and subsequent analysis, hence preventing further developments such as multi-center clinical studies. To address this issue, we present here a number of new developments. In particular, we have characterized a new SESI interface featuring real-time readout of critical exhalation parameters such as CO2, exhalation flow rate, and exhaled volume. Four healthy subjects provided breath specimens over a period of 1 month to characterize the stability of the SESI-HRMS system. A first assessment of the repeatability of the system using a gas standard revealed a coefficient of variation (CV) of 2.9%. Three classes of aldehydes, namely 4-hydroxy-2-alkenals, 2-alkenals and 4-hydroxy-2,6-alkedienals―hypothesized to be markers of oxidative stress―were chosen as representative metabolites of interest to evaluate the repeatability and reproducibility of this breath analysis analytical platform. Median and interquartile ranges (IQRs) of CVs for CO2, exhalation flow rate, and exhaled volume were 3.2% (1.5%), 3.1% (1.9%), and 5.0% (4.6%), respectively. Despite the high repeatability observed for these parameters, we observed a systematic decay in the signal during repeated measurements for the shorter fatty aldehydes, which eventually reached a steady state after three/four repeated exhalations. In contrast, longer fatty aldehydes showed a steady behavior, independent of the number of repeated exhalation maneuvers. We hypothesize that this highly molecule-specific and individual-independent behavior may be explained by the fact that shorter aldehydes (with higher estimated blood-to-air partition coefficients; approaching 100) mainly get exchanged in the airways of the respiratory system, whereas the longer aldehydes (with smaller estimated blood-to-air partition coefficients; approaching 10) are thought to exchange mostly in the alveoli. Exclusion of the first three exhalations from the analysis led to a median CV (IQR) of 6.7 % (5.5 %) for the said classes of aldehydes. We found that such intra-subject variability is in general much lower than inter-subject variability (median relative differences between subjects 48.2%), suggesting that the system is suitable to capture such differences. No batch effect due to sampling date was observed, overall suggesting that the intra-subject variability measured for these series of aldehydes was biological rather than technical. High correlations found among the series of aldehydes support this notion. Finally, recommendations for breath sampling and analysis for SESI-HRMS users are provided with the aim of harmonizing procedures and improving future inter-laboratory comparisons. Graphical abstract ![]()
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Meoni G, Lorini S, Monti M, Madia F, Corti G, Luchinat C, Zignego AL, Tenori L, Gragnani L. The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy. Sci Rep 2019; 9:4128. [PMID: 30858406 PMCID: PMC6412048 DOI: 10.1038/s41598-019-40028-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 01/23/2019] [Indexed: 02/06/2023] Open
Abstract
Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using 1H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that 1H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection.
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Affiliation(s)
- Gaia Meoni
- University of Florence, Magnetic Resonance Center (CERM), Sesto Fiorentino, 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino, 50019, Italy
| | - Serena Lorini
- Careggi University Hospital, Department of Experimental and Clinical Medicine, Interdepartmental Center for Systemic Manifestations of Hepatitis Viruses (MaSVE), Florence, 50134, Italy
| | - Monica Monti
- Careggi University Hospital, Department of Experimental and Clinical Medicine, Interdepartmental Center for Systemic Manifestations of Hepatitis Viruses (MaSVE), Florence, 50134, Italy
| | - Francesco Madia
- Careggi University Hospital, Department of Experimental and Clinical Medicine, Interdepartmental Center for Systemic Manifestations of Hepatitis Viruses (MaSVE), Florence, 50134, Italy
| | - Giampaolo Corti
- Careggi University Hospital, Infectious and Tropical Diseases Unit, Florence, 50134, Italy
| | - Claudio Luchinat
- University of Florence, Magnetic Resonance Center (CERM), Sesto Fiorentino, 50019, Italy.,Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Sesto Fiorentino, 50019, Italy.,University of Florence, Department of Chemistry "Ugo Schiff", Sesto Fiorentino, 50019, Italy
| | - Anna Linda Zignego
- Careggi University Hospital, Department of Experimental and Clinical Medicine, Interdepartmental Center for Systemic Manifestations of Hepatitis Viruses (MaSVE), Florence, 50134, Italy
| | - Leonardo Tenori
- University of Florence, Magnetic Resonance Center (CERM), Sesto Fiorentino, 50019, Italy. .,University of Florence, Department of Experimental and Clinical Medicine, Florence, 50134, Italy.
| | - Laura Gragnani
- Careggi University Hospital, Department of Experimental and Clinical Medicine, Interdepartmental Center for Systemic Manifestations of Hepatitis Viruses (MaSVE), Florence, 50134, Italy.
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. High-Throughput Metabolomics by 1D NMR. Angew Chem Int Ed Engl 2019; 58:968-994. [PMID: 29999221 PMCID: PMC6391965 DOI: 10.1002/anie.201804736] [Citation(s) in RCA: 218] [Impact Index Per Article: 43.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Indexed: 12/12/2022]
Abstract
Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.
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Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P.Via Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Veronica Ghini
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Gaia Meoni
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | - Cristina Licari
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of FlorenceLargo Brambilla 3FlorenceItaly
| | - Paola Turano
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
| | - Claudio Luchinat
- CERMUniversity of FlorenceVia Luigi Sacconi 650019 Sesto FiorentinoFlorenceItaly
- Department of Chemistry “Ugo Schiff”University of FlorenceVia della Lastruccia 3–1350019 Sesto FiorentinoFlorenceItaly
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Vignoli A, Ghini V, Meoni G, Licari C, Takis PG, Tenori L, Turano P, Luchinat C. Hochdurchsatz‐Metabolomik mit 1D‐NMR. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201804736] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Alessia Vignoli
- C.I.R.M.M.P. Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Veronica Ghini
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Gaia Meoni
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | - Cristina Licari
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
| | | | - Leonardo Tenori
- Department of Experimental and Clinical MedicineUniversity of Florence Largo Brambilla 3 Florence Italien
| | - Paola Turano
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
| | - Claudio Luchinat
- CERMUniversity of Florence Via Luigi Sacconi 6 50019 Sesto Fiorentino Florence Italien
- Department of Chemistry “Ugo Schiff”University of Florence Via della Lastruccia 3–13 50019 Sesto Fiorentino Florence Italien
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Balashova EE, Maslov DL, Lokhov PG. A Metabolomics Approach to Pharmacotherapy Personalization. J Pers Med 2018; 8:jpm8030028. [PMID: 30189667 PMCID: PMC6164342 DOI: 10.3390/jpm8030028] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 08/17/2018] [Accepted: 09/03/2018] [Indexed: 12/27/2022] Open
Abstract
The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.
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Affiliation(s)
- Elena E Balashova
- Institute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia.
| | - Dmitry L Maslov
- Institute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia.
| | - Petr G Lokhov
- Institute of Biomedical Chemistry, Pogodinskaya St. 10, Moscow 119121, Russia.
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Instability of personal human metabotype is linked to all-cause mortality. Sci Rep 2018; 8:9810. [PMID: 29955084 PMCID: PMC6023858 DOI: 10.1038/s41598-018-27958-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 06/13/2018] [Indexed: 12/17/2022] Open
Abstract
Disruption of metabolic homeostasis is an important factor in many diseases. Various metabolites have been linked to higher risk of morbidity and all-cause mortality using metabolomics in large population-based cohorts. In these studies, baseline metabolite levels were compared across subjects to identify associations with health outcomes, implying the existence of 'healthy' concentration ranges that are equally applicable to all individuals. Here, we focused on intra-individual changes in metabolite levels over time and their link to mortality, potentially allowing more personalized risk assessment. We analysed targeted metabolomics data for 134 blood metabolites from 1409 participants in the population-based CARLA cohort at baseline and after four years. Metabotypes of the majority of participants (59%) were extremely stable over time indicated by high correlation between the subjects' metabolite profiles at the two time points. Metabotype instability and, in particular, decrease of valine were associated with higher risk of all-cause mortality in 7.9 years of follow-up (hazard ratio (HR) = 1.5(95%CI = 1.0-2.3) and 0.2(95%CI = 0.1-0.3)) after multifactorial adjustment. Excluding deaths that occurred in the first year after metabolite profiling showed similar results (HR = 1.8(95%CI = 1.1-2.8)). Lower metabotype stability was also associated with incident cardiovascular disease (OR = 1.2(95%CI = 1.0-1.3)). Therefore, changes in the personal metabotype might be a valuable indicator of pre-clinical disease.
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Caracausi M, Ghini V, Locatelli C, Mericio M, Piovesan A, Antonaros F, Pelleri MC, Vitale L, Vacca RA, Bedetti F, Mimmi MC, Luchinat C, Turano P, Strippoli P, Cocchi G. Plasma and urinary metabolomic profiles of Down syndrome correlate with alteration of mitochondrial metabolism. Sci Rep 2018; 8:2977. [PMID: 29445163 PMCID: PMC5813015 DOI: 10.1038/s41598-018-20834-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 01/22/2018] [Indexed: 01/16/2023] Open
Abstract
Down syndrome (DS) is caused by the presence of a supernumerary copy of the human chromosome 21 (Hsa21) and is the most frequent genetic cause of intellectual disability (ID). Key traits of DS are the distinctive facies and cognitive impairment. We conducted for the first time an analysis of the Nuclear Magnetic Resonance (NMR)-detectable part of the metabolome in plasma and urine samples, studying 67 subjects with DS and 29 normal subjects as controls selected among DS siblings. Multivariate analysis of the NMR metabolomic profiles showed a clear discrimination (up to of 80% accuracy) between the DS and the control groups. The univariate analysis of plasma and urine revealed a significant alteration for some interesting metabolites. Remarkably, most of the altered concentrations were consistent with the 3:2 gene dosage model, suggesting effects caused by the presence of three copies of Hsa21 rather than two: DS/normal ratio in plasma was 1.23 (pyruvate), 1.47 (succinate), 1.39 (fumarate), 1.33 (lactate), 1.4 (formate). Several significantly altered metabolites are produced at the beginning or during the Krebs cycle. Accounting for sex, age and fasting state did not significantly affect the main result of both multivariate and univariate analysis.
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Affiliation(s)
- Maria Caracausi
- Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy
| | - Veronica Ghini
- CERM, Center of Magnetic Resonance, University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Florence, Italy.,CIRMMP, Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Florence, Italy
| | - Chiara Locatelli
- Neonatology Unit, St. Orsola-Malpighi Polyclinic, Via Massarenti 9, 40138, Bologna, BO, Italy
| | - Martina Mericio
- Neonatology Unit, St. Orsola-Malpighi Polyclinic, Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, Italy
| | - Allison Piovesan
- Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy
| | - Francesca Antonaros
- Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy
| | - Maria Chiara Pelleri
- Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy
| | - Lorenza Vitale
- Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy
| | - Rosa Anna Vacca
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Council of Research, Via Amendola 165/A, I-70126, Bari, Italy
| | - Federica Bedetti
- Neonatology Unit, St. Orsola-Malpighi Polyclinic, Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, Italy
| | - Maria Chiara Mimmi
- Department of Medical and Biological Sciences, University of Udine, P.le Massimiliano Kolbe 4, 33100, Udine, Italy
| | - Claudio Luchinat
- CERM, Center of Magnetic Resonance, University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Florence, Italy.,Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019, Sesto Fiorentino, Florence, Italy
| | - Paola Turano
- CERM, Center of Magnetic Resonance, University of Florence, Via Luigi Sacconi 6, 50019, Sesto Fiorentino, Florence, Italy.,Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019, Sesto Fiorentino, Florence, Italy
| | - Pierluigi Strippoli
- Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Via Belmeloro 8, 40126, Bologna, BO, Italy.
| | - Guido Cocchi
- Neonatology Unit, St. Orsola-Malpighi Polyclinic, Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Via Massarenti 9, 40138, Bologna, BO, Italy
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Saccenti E, Smilde AK, Camacho J. Group-wise ANOVA simultaneous component analysis for designed omics experiments. Metabolomics 2018; 14:73. [PMID: 29861703 PMCID: PMC5962647 DOI: 10.1007/s11306-018-1369-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/05/2018] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretation. OBJECTIVES We introduce here a sparse implementation of ASCA termed group-wise ANOVA-simultaneous component analysis (GASCA) with the aim of obtaining models that are easier to interpret. METHODS GASCA is based on the concept of group-wise sparsity introduced in group-wise principal components analysis where structure to impose sparsity is defined in terms of groups of correlated variables found in the correlation matrices calculated from the effect matrices. RESULTS The GASCA model, containing only selected subsets of the original variables, is easier to interpret and describes relevant biological processes. CONCLUSIONS GASCA is applicable to any kind of omics data obtained through designed experiments such as, but not limited to, metabolomic, proteomic and gene expression data.
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Blümich B, Singh K. Desktop NMR and Its Applications From Materials Science To Organic Chemistry. Angew Chem Int Ed Engl 2017; 57:6996-7010. [PMID: 29230908 DOI: 10.1002/anie.201707084] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Indexed: 12/19/2022]
Abstract
NMR spectroscopy is an indispensable method of analysis in chemistry, which until recently suffered from high demands for space, high costs for acquisition and maintenance, and operational complexity. This has changed with the introduction of compact NMR spectrometers suitable for small-molecule analysis on the chemical workbench. These spectrometers contain permanent magnets giving rise to proton NMR frequencies between 40 and 80 MHz. The enabling technology is to make small permanent magnets with homogeneous fields. Tabletop instruments with inhomogeneous fields have been in use for over 40 years for characterizing food and hydrogen-containing materials by relaxation and diffusion measurements. Related NMR instruments measure these parameters in the stray field outside the magnet. They are used to inspect the borehole walls of oil wells and to test objects nondestructively. The state-of-the-art of NMR spectroscopy, imaging and relaxometry with compact instruments is reviewed.
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Affiliation(s)
- Bernhard Blümich
- Institut für Technische und Makromolekulare Chemie, RWTH Aachen University, Aachen, Germany
| | - Kawarpal Singh
- Institut für Technische und Makromolekulare Chemie, RWTH Aachen University, Aachen, Germany
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Blümich B, Singh K. NMR mit Tischgeräten und deren Anwendungen von der Materialwissenschaft bis zur organischen Chemie. Angew Chem Int Ed Engl 2017. [DOI: 10.1002/ange.201707084] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Bernhard Blümich
- Institut für Technische und Makromolekulare Chemie; RWTH Aachen University; Aachen Deutschland
| | - Kawarpal Singh
- Institut für Technische und Makromolekulare Chemie; RWTH Aachen University; Aachen Deutschland
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Takis PG, Schäfer H, Spraul M, Luchinat C. Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool. Nat Commun 2017; 8:1662. [PMID: 29162796 PMCID: PMC5698486 DOI: 10.1038/s41467-017-01587-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 09/29/2017] [Indexed: 02/08/2023] Open
Abstract
The NMR chemical shifts of a substance in a complex mixture strongly depend on the composition of the mixture itself, as many weak interactions occur that are hardly predictable. Chemical shift variability is the major obstacle to automatically assigning, and subsequently quantitating, metabolite signals in body fluids, particularly urine. Here we demonstrate that the chemical shifts of signals in urine are actually predictable. This is achieved by constructing ca. 4000 artificial mixtures where the concentrations of 52 most abundant urine metabolites-including 11 inorganic ions-are varied, to sparsely but efficiently populate an N-dimensional concentration matrix. A strong relationship is established between the concentration matrix and the chemical shift matrix, so that chemical shifts of > 90 metabolite signals can be accurately predicted in real urine samples. The concentrations of the invisible inorganic ions are also accurately predicted, along with those of albumin and of several other abundant urine components.
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Affiliation(s)
- Panteleimon G Takis
- Giotto Biotech S.R.L., Via Madonna del Piano 6, 50019, Sesto Fiorentino (FI), Italy
| | - Hartmut Schäfer
- Bruker BioSpin, Silberstreifen, D-76287, Rheinstetten, Germany
| | - Manfred Spraul
- Bruker BioSpin, Silberstreifen, D-76287, Rheinstetten, Germany
| | - Claudio Luchinat
- Giotto Biotech S.R.L., Via Madonna del Piano 6, 50019, Sesto Fiorentino (FI), Italy.
- Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019, Sesto Fiorentino (FI), Italy.
- Department of Chemistry Ugo Schiff, University of Florence, Via della Lastruccia 3, 50019, Sesto Fiorentino (FI), Italy.
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Vignoli A, Tenori L, Luchinat C, Saccenti E. Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects. J Proteome Res 2017; 17:97-107. [DOI: 10.1021/acs.jproteome.7b00404] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Alessia Vignoli
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department
of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy
| | - Claudio Luchinat
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department
of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, the Netherlands
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Abstract
AbstractMetabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.
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Harmonisation of biobanking standards in endometrial cancer research. Br J Cancer 2017; 117:485-493. [PMID: 28664917 PMCID: PMC5558683 DOI: 10.1038/bjc.2017.194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 06/01/2017] [Accepted: 06/01/2017] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Endometrial cancer is the most common gynaecological cancer and its incidence is predicted to escalate by 50-100% in 2025 with a parallel increase in associated mortality. Variations in the collection, processing and storage of biospecimens can affect the generalisability of the scientific data. We aimed to harmonise the collection of biospecimens, clinical data relevant to endometrial cancer and to develop standard operative procedures for the collection, processing and storage of endometrial cancer biospecimens. METHODS We designed research tools, which were evaluated and revised through three consensus rounds - to obtain local/regional, national and European consensus. Modified final tools were disseminated to a panel (n=40) representing all stakeholders in endometrial cancer research for consensus generation. RESULTS The final consensus demonstrated unanimous agreement with the minimal surgical and patient data collection tools. A high level of agreement was also observed for the other remaining standard tools. CONCLUSIONS We here present the final versions of the tools, which are freely available and easily accessible to all endometrial cancer researchers. We believe that these tools will facilitate rapid progress in endometrial cancer research, both in future collaborations and in large-scale multicentre studies.
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Iizuka D, Yoshioka S, Kawai H, Izumi S, Suzuki F, Kamiya K. Metabolomic screening using ESI-FT MS identifies potential radiation-responsive molecules in mouse urine. JOURNAL OF RADIATION RESEARCH 2017; 58:273-280. [PMID: 27974505 PMCID: PMC5619916 DOI: 10.1093/jrr/rrw112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 09/23/2016] [Indexed: 05/24/2023]
Abstract
The demand for establishment of high-throughput biodosimetric methods is increasing. Our aim in this study was to identify low-molecular-weight urinary radiation-responsive molecules using electrospray ionization Fourier transform mass spectrometry (ESI-FT MS), and our final goal was to develop a sensitive biodosimetry technique that can be applied in the early triage of a radiation emergency medical system. We identified nine metabolites by statistical comparison of mouse urine before and 8 h after irradiation. Time-course analysis showed that, of these metabolites, thymidine and either thymine or imidazoleacetic acid were significantly increased dose-dependently 8 h after radiation exposure; these molecules have already been reported as potential radiation biomarkers. Phenyl glucuronide was significantly decreased 8 h after radiation exposure, irrespective of the dose. Histamine and 1-methylhistamine were newly identified by MS/MS and showed significant, dose-dependent increases 72 h after irradiation. Quantification of 1-methylhistamine by enzyme-linked immunosorbent assay (ELISA) analysis also showed a significant increase 72 h after 4 Gy irradiation. These results suggest that urinary metabolomics screening using ESI-FT MS can be a powerful tool for identifying promising radiation-responsive molecules, and that urinary 1-methylhistamine is a potential radiation-responsive molecule for acute, high-dose exposure.
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Affiliation(s)
- Daisuke Iizuka
- Department of Experimental Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
- Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, 1-3-2, Kagamiyama, Higashi-Hiroshima 739-8511, Japan
| | - Susumu Yoshioka
- Department of Molecular Radiobiology, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
| | - Hidehiko Kawai
- Department of Experimental Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
- Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, 1-3-2, Kagamiyama, Higashi-Hiroshima 739-8511, Japan
| | - Shunsuke Izumi
- Department of Molecular Radiobiology, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
| | - Fumio Suzuki
- Department of Mathematical and Life Sciences, Graduate School of Science, Hiroshima University, 1-3-2, Kagamiyama, Higashi-Hiroshima 739-8511, Japan
| | - Kenji Kamiya
- Department of Experimental Oncology, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan
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Kantae V, Krekels EHJ, Esdonk MJV, Lindenburg P, Harms AC, Knibbe CAJ, Van der Graaf PH, Hankemeier T. Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy. Metabolomics 2016; 13:9. [PMID: 28058041 PMCID: PMC5165030 DOI: 10.1007/s11306-016-1143-1] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 11/26/2016] [Indexed: 02/05/2023]
Abstract
Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.
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Affiliation(s)
- Vasudev Kantae
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Elke H. J. Krekels
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Michiel J. Van Esdonk
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Peter Lindenburg
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Amy C. Harms
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Catherijne A. J. Knibbe
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Piet H. Van der Graaf
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Certara QSP, Canterbury Innovation Centre, Canterbury, UK
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
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46
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NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients. Anal Bioanal Chem 2016; 409:1405-1413. [PMID: 27900420 DOI: 10.1007/s00216-016-0074-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 10/05/2016] [Accepted: 10/31/2016] [Indexed: 12/27/2022]
Abstract
The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using 1H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.
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47
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Lipid metabolism is associated with developmental epigenetic programming. Sci Rep 2016; 6:34857. [PMID: 27713555 PMCID: PMC5054359 DOI: 10.1038/srep34857] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 09/19/2016] [Indexed: 12/24/2022] Open
Abstract
Maternal diet and metabolism impact fetal development. Epigenetic reprogramming facilitates fetal adaptation to these in utero cues. To determine if maternal metabolite levels impact infant DNA methylation globally and at growth and development genes, we followed a clinical birth cohort of 40 mother-infant dyads. Targeted metabolomics and quantitative DNA methylation were analyzed in 1st trimester maternal plasma (M1) and delivery maternal plasma (M2) as well as infant umbilical cord blood plasma (CB). We found very long chain fatty acids, medium chain acylcarnitines, and histidine were: (1) stable in maternal plasma from pregnancy to delivery, (2) significantly correlated between M1, M2, and CB, and (3) in the top 10% of maternal metabolites correlating with infant DNA methylation, suggesting maternal metabolites associated with infant DNA methylation are tightly controlled. Global DNA methylation was highly correlated across M1, M2, and CB. Thus, circulating maternal lipids are associated with developmental epigenetic programming, which in turn may impact lifelong health and disease risk. Further studies are required to determine the causal link between maternal plasma lipids and infant DNA methylation patterns.
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48
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Boizard F, Brunchault V, Moulos P, Breuil B, Klein J, Lounis N, Caubet C, Tellier S, Bascands JL, Decramer S, Schanstra JP, Buffin-Meyer B. A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome. Sci Rep 2016; 6:34453. [PMID: 27694997 PMCID: PMC5046087 DOI: 10.1038/srep34453] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 09/14/2016] [Indexed: 01/31/2023] Open
Abstract
Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.
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Affiliation(s)
- Franck Boizard
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | - Valérie Brunchault
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | | | - Benjamin Breuil
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | - Nadia Lounis
- Unité de Recherche Clinique Pédiatrique, Module Plurithématique Pédiatrique, Centre d'Investigation Clinique - Hôpital des Enfants, Toulouse, France
| | - Cécile Caubet
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | - Stéphanie Tellier
- CHU Toulouse, Hôpital des Enfants, Service de Néphrologie - Médecine Interne - Hypertension Pédiatrique, Toulouse, France
| | - Jean-Loup Bascands
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | - Stéphane Decramer
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France.,CHU Toulouse, Hôpital des Enfants, Service de Néphrologie - Médecine Interne - Hypertension Pédiatrique, Toulouse, France
| | - Joost P Schanstra
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
| | - Bénédicte Buffin-Meyer
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Equipe 12, 1 avenue Jean Poulhès, BP 84225, 31432 Toulouse Cedex 4, France.,Université Toulouse III Paul-Sabatier Toulouse, France
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49
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Lewis MR, Pearce JTM, Spagou K, Green M, Dona AC, Yuen AHY, David M, Berry DJ, Chappell K, Horneffer-van der Sluis V, Shaw R, Lovestone S, Elliott P, Shockcor J, Lindon JC, Cloarec O, Takats Z, Holmes E, Nicholson JK. Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal Chem 2016; 88:9004-13. [DOI: 10.1021/acs.analchem.6b01481] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Matthew R. Lewis
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Jake T. M. Pearce
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Konstantina Spagou
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Martin Green
- Waters Corporation, Stamford Avenue, Altrincham
Road, Wilmslow SK9 4AX, United Kingdom
| | - Anthony C. Dona
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Ada H. Y. Yuen
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
| | - Mark David
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
| | - David J. Berry
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
| | - Katie Chappell
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
| | - Verena Horneffer-van der Sluis
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
| | - Rachel Shaw
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Simon Lovestone
- Department
of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom
| | - Paul Elliott
- MRC-PHE
Centre for Environment and Health, Department of Epidemiology and
Biostatistics, Imperial College London, St Mary’s Campus, Norfolk Place, London W2 1PG, United Kingdom
| | - John Shockcor
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - John C. Lindon
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Olivier Cloarec
- Korrigan Sciences Ltd., 38 Wakemans, Upper Basildon, Reading RG8 8JE, United Kingdom
| | - Zoltan Takats
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Elaine Holmes
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
| | - Jeremy K. Nicholson
- MRC-NIHR
National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London W12 0NN, United Kingdom
- Division
of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom
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50
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Saccenti E, Menichetti G, Ghini V, Remondini D, Tenori L, Luchinat C. Entropy-Based Network Representation of the Individual Metabolic Phenotype. J Proteome Res 2016; 15:3298-307. [DOI: 10.1021/acs.jproteome.6b00454] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Edoardo Saccenti
- Laboratory
of Systems and Synthetic Biology, Wageningen University, Stippeneng
4, 6708 WE Wageningen, The Netherlands
| | - Giulia Menichetti
- Department
of Physics and Astronomy and INFN Sez. Bologna, University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
| | - Veronica Ghini
- Magnetic
Resonance Center, University of Florence, via Luigi Sacconi 6, 59100 Sesto Fiorentino, Italy
| | - Daniel Remondini
- Department
of Physics and Astronomy and INFN Sez. Bologna, University of Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
| | - Leonardo Tenori
- Magnetic
Resonance Center, University of Florence, via Luigi Sacconi 6, 59100 Sesto Fiorentino, Italy
- Department
of Experimental and Clinical Medicine, University of Florence, Largo Brambilla
3, 50134 Florence, Italy
| | - Claudio Luchinat
- Magnetic
Resonance Center, University of Florence, via Luigi Sacconi 6, 59100 Sesto Fiorentino, Italy
- Department
of Chemistry, University of Florence, via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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