TY  - JOUR
AB  - INTRODUCTION AND OBJECTIVE: Rumex sanguineus, a traditional medicinal plant of the Polygonaceae family, is gaining popularity as an edible resource. However, despite its historical and nutritional significance, its chemical composition remains poorly understood. To deepen the understanding of the of Rumex sanguineus composition, an in-depth analysis using non-targeted, mass spectrometry-based metabolomics was performed.  METHODS: Rumex roots, stems and leaves samples were analyzed by UHPLC-HRMS and subsequently subjected to feature-based molecular networking. RESULTS AND CONCLUSION: Overall, 347 primary and specialized metabolites grouped into 8 biochemical classes were annotated. Most of these metabolites (60%) belong to the polyphenols and anthraquinones classes. To investigate potential' toxicity due to the presence of anthraquinones, the amount of emodin was quantified with analytical standard, revealing higher accumulation in leaves compared to stems and roots. This highlights the need for thorough metabolomic studies to understand both beneficial and harmful compounds, especially in plants with historical medicinal use transitioning to modern culinary use.
AU  - Ramundi, V.
AU  - Zdouc, M.M.*
AU  - Donati, E.*
AU  - van der Hooft, J.J.J.*
AU  - Cimini, S.*
AU  - Righetti, L.*
C1  - 73164
C2  - 56940
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Non-targeted metabolomics-based molecular networking enables the chemical characterization of Rumex sanguineus, a wild edible plant.
JO  - Metabolomics
VL  - 21
IS  - 1
PB  - Springer
PY  - 2025
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - INTRODUCTION: The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipid species for which no reference spectra are available is tedious and often involves a lot of manual work. Integrating high-resolution mass spectrometry with enhancements from chromatographic and ion mobility separation enables the in-depth investigation of intact lipids. OBJECTIVES: We investigated phosphorylated glycosphingolipids from the nematode Caenorhabditis elegans, a biomedical model organism, and aimed to identify different species from this class of lipids, which have been described in one particular publication only. We checked if these lipids can be detected in lipid extracts of C. elegans. METHODS: We used UHPLC-UHR-TOF-MS and UHPLC-TIMS-TOF-MS in combination with dedicated data analysis to check for the presence of phosphorylated glycosphingolipids. Specifically, candidate features were identified in two datasets using Mass Spec Query Language (MassQL) to search fragmentation data. The additional use of retention time (RT) and collisional cross section (CCS) information allowed to filter false positive annotations. RESULTS: As a result, we detected all previously described phosphorylated glycosphingolipids and novel species as well as their biosynthetic precursors in two different lipidomics datasets. MassQL significantly speeds up the process by saving time that would otherwise be spent on manual data investigations. In total over 20 sphingolipids could be described. CONCLUSION: MassQL allowed us to search for phosphorylated glycosphingolipids and their potential biosynthetic precursors systematically. Using orthogonal information such as RT and CCS helped filter false positive results. With the detection in two different datasets, we demonstrate that these sphingolipids are a general part of the C. elegans lipidome.
AU  - Witting, M.
AU  - Salzer, L.
AU  - Meyer, S.W.*
AU  - Barsch, A.*
C1  - 73438
C2  - 57063
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Phosphorylated glycosphingolipids are commonly detected in Caenorhabditis elegans lipidomes.
JO  - Metabolomics
VL  - 21
IS  - 2
PB  - Springer
PY  - 2025
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - BACKGROUND: Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools. AIM OF REVIEW: In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists. KEY SCIENTIFIC CONCEPTS OF REVIEW: We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.
AU  - Novoa-del-Toro, E.M.*
AU  - Witting, M.
C1  - 71790
C2  - 56339
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Navigating common pitfalls in metabolite identification and metabolomics bioinformatics.
JO  - Metabolomics
VL  - 20
IS  - 5
PB  - Springer
PY  - 2024
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - INTRODUCTION/OBJECTIVES: Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation. METHODS: We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites. RESULTS: The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets. CONCLUSIONS: The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.
AU  - Ponce-de-Leon, M.*
AU  - Wang-Sattler, R.
AU  - Peters, A.
AU  - Rathmann, W.*
AU  - Grallert, H.
AU  - Artati, A.
AU  - Prehn, C.
AU  - Adamski, J.
AU  - Meisinger, C.*
AU  - Linseisen, J.*
C1  - 71798
C2  - 56167
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Stool and blood metabolomics in the metabolic syndrome: A cross-sectional study.
JO  - Metabolomics
VL  - 20
IS  - 5
PB  - Springer
PY  - 2024
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - INTRODUCTION: Biliary atresia (BA) is a rare progressive neonatal cholangiopathy with unknown pathophysiology and time of onset. Newborn Screening (NBS) in Germany is routinely performed in the first days of life to identify rare congenital diseases utilizing dried blood spot (DBS) card analyses. Infants with biliary atresia (BA) are known to have altered amino acid profiles (AAP) at the time point of diagnosis, but it is unclear whether these alterations are present at the time point of NBS. OBJECTIVES: We aimed to analyze amino acid profiles in NBS-DBS of infants with Biliary Atresia. METHODS: Original NBS-DBS cards of 41 infants who were later on diagnosed with BA were retrospectively obtained. NBS-DBS cards from healthy newborns (n = 40) served as controls. In some BA infants (n = 14) a second DBS card was obtained at time of Kasai surgery. AAP in DBS cards were analyzed by targeted metabolomics. RESULTS: DBS metabolomics in the NBS of at that time point seemingly healthy infants later diagnosed with BA revealed significantly higher levels of Methionine (14.6 ± 8.6 μmol/l), Histidine (23.5 ± 50.3 μmol/l), Threonine (123.9 ± 72.8 μmol/l) and Arginine (14.1 ± 11.8 μmol/l) compared to healthy controls (Met: 8.1 ± 2.6 μmol/l, His: 18.6 ± 10.1 μmol/l, Thr: 98.1 ± 34.3 μmol/l, Arg: 9.3 ± 6.6 μmol/l). Methionine, Arginine and Histidine showed a further increase at time point of Kasai procedure. No correlation between amino acid levels and clinical course was observed. CONCLUSION: Our data demonstrate that BA patients exhibit an altered AAP within 72 h after birth, long before the infants become symptomatic. This supports the theory of a prenatal onset of the disease and, thus, the possibility of developing a sensitive and specific NBS. Methionine might be particularly relevant due to its involvement in glutathione metabolism. Further investigation of AAP in BA may help in understanding the underlying pathophysiology.
AU  - Uecker, M.*
AU  - Prehn, C.
AU  - Janzen, N.*
AU  - Adamski, J.
AU  - Vieten, G.*
AU  - Petersen, C.*
AU  - Kuebler, J.F.*
AU  - Madadi-Sanjani, O.*
AU  - Klemann, C.*
C1  - 71928
C2  - 56297
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Infants with biliary atresia exhibit an altered amino acid profile in their newborn screening.
JO  - Metabolomics
VL  - 20
IS  - 5
PB  - Springer
PY  - 2024
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - INTRODUCTION: Lipids are key compounds in the study of metabolism and are increasingly studied in biology projects. It is a very broad family that encompasses many compounds, and the name of the same compound may vary depending on the community where they are studied. OBJECTIVES: In addition, their structures are varied and complex, which complicates their analysis. Indeed, the structural resolution does not always allow a complete level of annotation so the actual compound analysed will vary from study to study and should be clearly stated. For all these reasons the identification and naming of lipids is complicated and very variable from one study to another, it needs to be harmonized. METHODS & RESULTS: In this position paper we will present and discuss the different way to name lipids (with chemoinformatic and semantic identifiers) and their importance to share lipidomic results. CONCLUSION: Homogenising this identification and adopting the same rules is essential to be able to share data within the community and to map data on functional networks.
AU  - Witting, M.
AU  - Malik, A.*
AU  - Leach, A.*
AU  - Bridge, A.*
AU  - Aimo, L.*
AU  - Conroy, M.J.*
AU  - O'Donnell, V.B.*
AU  - Hoffmann, N.*
AU  - Kopczynski, D.*
AU  - Giacomoni, F.*
AU  - Paulhe, N.*
AU  - Gassiot, A.C.*
AU  - Poupin, N.*
AU  - Jourdan, F.*
AU  - Bertrand-Michel, J.*
C1  - 69868
C2  - 55294
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Challenges and perspectives for naming lipids in the context of lipidomics.
JO  - Metabolomics
VL  - 20
IS  - 1
PB  - Springer
PY  - 2024
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Assessing intraspecific variation in plant volatile organic compounds (VOCs) involves pitfalls that may bias biological interpretation, particularly when several laboratories collaborate on joint projects. Comparative, inter-laboratory ring trials can inform on the reproducibility of such analyses. Objectives: In a ring trial involving five laboratories, we investigated the reproducibility of VOC collections with polydimethylsiloxane (PDMS) and analyses by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). As model plant we used Tanacetum vulgare, which shows a remarkable diversity in terpenoids, forming so-called chemotypes. We performed our ring-trial with two chemotypes to examine the sources of technical variation in plant VOC measurements during pre-analytical, analytical, and post-analytical steps. Methods: Monoclonal root cuttings were generated in one laboratory and distributed to five laboratories, in which plants were grown under laboratory-specific conditions. VOCs were collected on PDMS tubes from all plants before and after a jasmonic acid (JA) treatment. Thereafter, each laboratory (donors) sent a subset of tubes to four of the other laboratories (recipients), which performed TD-GC-MS with their own established procedures. Results: Chemotype-specific differences in VOC profiles were detected but with an overall high variation both across donor and recipient laboratories. JA-induced changes in VOC profiles were not reproducible. Laboratory-specific growth conditions led to phenotypic variation that affected the resulting VOC profiles. Conclusion: Our ring trial shows that despite large efforts to standardise each VOC measurement step, the outcomes differed both qualitatively and quantitatively. Our results reveal sources of variation in plant VOC research and may help to avoid systematic errors in similar experiments.
AU  - Eckert, S.*
AU  - Eilers, E.J.*
AU  - Jakobs, R.*
AU  - Anaia, R.A.*
AU  - Aragam, K.S.*
AU  - Bloss, T.*
AU  - Popp, M.
AU  - Sasidharan, R.*
AU  - Schnitzler, J.-P.
AU  - Stein, F.*
AU  - Steppuhn, A.*
AU  - Unsicker, S.B.*
AU  - van Dam, N.M.*
AU  - Yepes, S.*
AU  - Ziaja, D.*
AU  - Müller, C.*
C1  - 68490
C2  - 54666
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity.
JO  - Metabolomics
VL  - 19
IS  - 7
PB  - Springer
PY  - 2023
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Polar metabolites in Caenorhabditis elegans (C. elegans) have predominantly been analyzed using hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC-MS). Capillary electrophoresis coupled to mass spectrometry (CE-MS) represents another complementary analytical platform suitable for polar and charged analytes. Objective: We compared CE-MS and HILIC-MS for the analysis of a set of 60 reference standards relevant for C. elegans and specifically investigated the strengths of CE separation. Furthermore, we employed CE-MS as a complementary analytical approach to study polar metabolites in C. elegans samples, particularly in the context of longevity, in order to address a different part of its metabolome. Method: We analyzed 60 reference standards as well as metabolite extracts from C. elegans daf-2 loss-of-function mutants and wild-type (WT) samples using HILIC-MS and CE-MS employing a Q-ToF-MS instrument. Results: CE separations showed narrower peak widths and a better linearity of the estimated response function across different concentrations which is linked to less saturation of the MS signals. Additionally, CE exhibited a distinct selectivity in the separation of compounds compared to HILIC-MS, providing complementary information for the analysis of the target compounds. Analysis of C. elegans metabolites of daf-2 mutants and WT samples revealed significant alterations in shared metabolites identified through HILIC-MS, as well as the presence of distinct metabolites. Conclusion: CE-MS was successfully applied in C. elegans metabolomics, being able to recover known as well as identify novel putative biomarkers of longevity.
AU  - Salzer, L.
AU  - Schmitt-Kopplin, P.
AU  - Witting, M.
C1  - 68491
C2  - 54665
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Capillary electrophoresis-mass spectrometry as a tool for Caenorhabditis elegans metabolomics research.
JO  - Metabolomics
VL  - 19
IS  - 7
PB  - Springer
PY  - 2023
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: The structural identification of metabolites represents one of the current bottlenecks in non-targeted liquid chromatography-mass spectrometry (LC–MS) based metabolomics. The Metabolomics Standard Initiative has developed a multilevel system to report confidence in metabolite identification, which involves the use of MS, MS/MS and orthogonal data. Limitations due to similar or same fragmentation pattern (e.g. isomeric compounds) can be overcome by the additional orthogonal information of the retention time (RT), since it is a system property that is different for each chromatographic setup. Objectives: In contrast to MS data, sharing of RT data is not as widespread. The quality of data and its (re-)useability depend very much on the quality of the metadata. We aimed to evaluate the coverage and quality of this metadata from public metabolomics repositories. Methods: We acquired an overview on the current reporting of chromatographic separation conditions. For this purpose, we defined the following information as important details that have to be provided: column name and dimension, flow rate, temperature, composition of eluents and gradient. Results: We found that 70% of descriptions of the chromatographic setups are incomplete (according to our definition) and an additional 10% of the descriptions contained ambiguous and/or incorrect information. Accordingly, only about 20% of the descriptions allow further (re-)use of the data, e.g. for RT prediction. Therefore, we have started to develop a unified and standardized notation for chromatographic metadata with detailed and specific description of eluents, columns and gradients. Conclusion: Reporting of chromatographic metadata is currently not unified. Our recommended suggestions for metadata reporting will enable more standardization and automatization in future reporting.
AU  - Harrieder, E.-M.
AU  - Kretschmer, F.*
AU  - Dunn, W.*
AU  - Böcker, S.*
AU  - Witting, M.
C1  - 66826
C2  - 53301
TI  - Critical assessment of chromatographic metadata in publicly available metabolomics data repositories.
JO  - Metabolomics
VL  - 18
IS  - 12
PY  - 2022
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - BACKGROUND: Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. OBJECTIVE: The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. KEY REPORTING PRACTICES: Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.
AU  - Kirwan, J.A.*
AU  - Gika, H.G.*
AU  - Beger, R.D.*
AU  - Bearden, D.*
AU  - Dunn, W.B.*
AU  - Goodacre, R.*
AU  - Theodoridis, G.*
AU  - Witting, M.
AU  - Yu, L.R.*
AU  - Wilson, I.D.*
C1  - 65969
C2  - 53008
TI  - Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management.
JO  - Metabolomics
VL  - 18
IS  - 9
PY  - 2022
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - BACKGROUND: Metabolomics is a highly multidisciplinary and non-standardised research field. Metabolomics researchers must possess and apply extensive cross-disciplinary content knowledge, subjective experience-based judgement, and the associated diverse skill sets. Accordingly, appropriate educational and training initiatives are important in developing this knowledge and skills base in the metabolomics community. For these initiatives to be successful, they must consider both pedagogical best practice and metabolomics-specific contextual challenges. AIM OF REVIEW: The aim of this review is to provide consolidated pedagogical guidance for educators and trainers in metabolomics educational and training programmes. KEY SCIENTIFIC CONCEPTS OF REVIEW: In this review, we discuss the principles of pedagogical best practice as they relate to metabolomics. We then discuss the challenges and considerations in developing and delivering education and training in metabolomics. Finally, we present examples from our own teaching practice to illustrate how pedagogical best practice can be integrated into metabolomics education and training programmes.
AU  - Winder, C.L.*
AU  - Witting, M.
AU  - Tugizimana, F.*
AU  - Dunn, W.B.*
AU  - Reinke, S.N.*
C1  - 66992
C2  - 53400
TI  - Providing metabolomics education and training: Pedagogy and considerations.
JO  - Metabolomics
VL  - 18
IS  - 12
PY  - 2022
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - We would like to make the following correction: Incorrect Spelling of Author’s Name. In reference to the article Comparison of lipidome profiles of Caenorhabditis elegans- results from an inter-laboratory ring trial Britta Spanier, Anne Laurençon, Anna Weiser, Nathalie Pujol, Shizue Omi, Aiko Barsch, Ansgar Korf, Sven W. Meyer, Jonathan J. Ewbank, Francesca Paladino, Steve Garvis, Hugo Aguilaniu, Michael Witting There was an error in the spelling of the surname of one of the co-authors in the above paper. The name should read Francesca Palladino, and not Paladino. This has been corrected with this erratum.
AU  - Spanier, B.*
AU  - Laurençon, A.*
AU  - Weiser, A.*
AU  - Pujol, N.*
AU  - Omi, S.*
AU  - Barsch, A.*
AU  - Korf, A.*
AU  - Meyer, S.W.*
AU  - Ewbank, J.J.*
AU  - Palladino, F.*
AU  - Garvis, S.*
AU  - Aguilaniu, H.*
AU  - Witting,M.
C1  - 61546
C2  - 50336
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Correction to: Comparison of lipidome profiles of Caenorhabditis elegans-results from an inter-laboratory ring trial.
JO  - Metabolomics
VL  - 17
IS  - 3
PB  - Springer
PY  - 2021
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Lipidomic profiling allows 100s if not 1000s of lipids in a sample to be detected and quantified. Modern lipidomics techniques are ultra-sensitive assays that enable the discovery of novel biomarkers in a variety of fields and provide new insight in mechanistic investigations. Despite much progress in lipidomics, there remains, as for all high throughput “omics” strategies, the need to develop strategies to standardize and integrate quality control into studies in order to enhance robustness, reproducibility, and usability of studies within specific fields and beyond. Objectives: We aimed to understand how much results from lipid profiling in the model organism Caenorhabditis elegans are influenced by different culture conditions in different laboratories. Methods: In this work we have undertaken an inter-laboratory study, comparing the lipid profiles of N2 wild type C. elegans and daf-2(e1370) mutants lacking a functional insulin receptor. Sample were collected from worms grown in four separate laboratories under standardized growth conditions. We used an UPLC-UHR-ToF–MS system allowing chromatographic separation before MS analysis. Results: We found common qualitative changes in several marker lipids in samples from the individual laboratories. On the other hand, even in this controlled experimental system, the exact fold-changes for each marker varied between laboratories. Conclusion: Our results thus reveal a serious limitation to the reproducibility of current lipid profiling experiments and reveal challenges to the integration of such data from different laboratories.
AU  - Spanier, B.*
AU  - Laurençon, A.*
AU  - Weiser, A.*
AU  - Pujol, N.*
AU  - Omi, S.*
AU  - Barsch, A.*
AU  - Korf, A.*
AU  - Meyer, S.W.*
AU  - Ewbank, J.J.*
AU  - Paladino, F.*
AU  - Garvis, S.*
AU  - Aguilaniu, H.*
AU  - Witting,M.
C1  - 61393
C2  - 50184
CY  - One New York Plaza, Suite 4600, New York, Ny, United States
TI  - Comparison of lipidome profiles of Caenorhabditis elegans—results from an inter-laboratory ring trial.
JO  - Metabolomics
VL  - 17
IS  - 3
PB  - Springer
PY  - 2021
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Environmental chemicals acting as metabolic disruptors have been implicated with diabetogenesis, but evidence is weak among short-lived chemicals, such as disinfection byproducts (trihalomethanes, THM composed of chloroform, TCM and brominated trihalomethanes, BrTHM). Objectives: We assessed whether THM were associated with type 2 diabetes (T2D) and we explored alterations in metabolic profiles due to THM exposures or T2D status. Methods: A prospective 1:1 matched case–control study (n = 430) and a cross-sectional 1:1 matched case–control study (n = 362) nested within the HUNT cohort (Norway) and the Lifelines cohort (Netherlands), respectively, were set up. Urinary biomarkers of THM exposure and mass spectrometry-based serum metabolomics were measured. Associations between THM, clinical markers, metabolites and disease status were evaluated using logistic regressions with Least Absolute Shrinkage and Selection Operator procedure. Results: Low median THM exposures (ng/g, IQR) were measured in both cohorts (cases and controls of HUNT and Lifelines, respectively, 193 (76, 470), 208 (77, 502) and 292 (162, 595), 342 (180, 602). Neither BrTHM (OR = 0.87; 95% CI: 0.67, 1.11 | OR = 1.09; 95% CI: 0.73, 1.61), nor TCM (OR = 1.03; 95% CI: 0.88, 1.2 | OR = 1.03; 95% CI: 0.79, 1.35) were associated with incident or prevalent T2D, respectively. Metabolomics showed 48 metabolites associated with incident T2D after adjusting for sex, age and BMI, whereas a total of 244 metabolites were associated with prevalent T2D. A total of 34 metabolites were associated with the progression of T2D. In data driven logistic regression, novel biomarkers, such as cinnamoylglycine or 1-methylurate, being protective of T2D were identified. The incident T2D risk prediction model (HUNT) predicted well incident Lifelines cases (AUC = 0.845; 95% CI: 0.72, 0.97). Conclusion: Such exposome-based approaches in cohort-nested studies are warranted to better understand the environmental origins of diabetogenesis.
AU  - Gängler, S.*
AU  - Waldenberger, M.
AU  - Artati, A.
AU  - Adamski, J.
AU  - van Bolhuis, J.N.*
AU  - Sørgjerd, E.P.*
AU  - van Vliet-Ostaptchouk, J.V.*
AU  - Makris, K.C.*
C1  - 55824
C2  - 46604
CY  - 233 Spring St, New York, Ny 10013 Usa
TI  - Exposure to disinfection byproducts and risk of type 2 diabetes: A nested case–control study in the HUNT and Lifelines cohorts.
JO  - Metabolomics
VL  - 15
IS  - 4
PB  - Springer
PY  - 2019
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - IntroductionObesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO).ObjectivesWe aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress.MethodsWe reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS.ResultsThirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans.ConclusionThe present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.
AU  - Rangel-Huerta, O.D.*
AU  - Pastor-Villaescusa, B.
AU  - Gil, A.*
C1  - 56329
C2  - 47003
CY  - 233 Spring St, New York, Ny 10013 Usa
TI  - Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies.
JO  - Metabolomics
VL  - 15
IS  - 6
PB  - Springer
PY  - 2019
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.
AU  - Do, K.T.
AU  - Wahl, S.
AU  - Raffler, J.
AU  - Molnos, S.
AU  - Laimighofer, M.
AU  - Adamski, J.
AU  - Suhre, K.*
AU  - Strauch, K.
AU  - Peters, A.
AU  - Gieger, C.
AU  - Langenberg, C.*
AU  - Stewart, I.D.*
AU  - Theis, F.J.
AU  - Grallert, H.
AU  - Kastenmüller, G.
AU  - Krumsiek, J.
C1  - 54398
C2  - 45505
CY  - 233 Spring St, New York, Ny 10013 Usa
TI  - Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.
JO  - Metabolomics
VL  - 14
IS  - 10
PB  - Springer
PY  - 2018
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction Fasting metabolite profiles have been shown to distinguish type 2 diabetes (T2D) patients from normal glucose tolerance (NGT) individuals. Objectives We investigated whether, besides fasting metabolite profiles, postprandial metabolite profiles associated with T2D can stratify individuals with impaired fasting glucose (IFG) by their similarities to T2D. Methods Three groups of individuals (age 45-65 years) without any history of IFG or T2D were selected from the Netherlands Epidemiology of Obesity study and stratified by baseline fasting glucose concentrations (NGT (n = 176), IFG (n = 186), T2D (n = 171)). 163 metabolites were measured under fasting and postprandial states (150 min after a meal challenge). Metabolite profiles specific for a high risk of T2D were identified by LASSO regression for fasting and postprandial states. The selected profiles were utilised to stratify IFG group into high (T2D probability >= 0.7) and low (T2D probability <= 0.5) risk subgroups. The stratification performances were compared with clinically relevant metabolic traits. Results Two metabolite profiles specific for T2D (n(fasting) = 12 metabolites, n(postprandial) = 4 metabolites) were identified, with all four postprandial metabolites also being identified in the fasting state. Stratified by the postprandial profile, the high-risk subgroup of IFG individuals (n = 72) showed similar glucose concentrations to the low-risk subgroup (n = 57), yet a higher BMI (difference: 3.3 kg/m(2) (95% CI 1.7-5.0)) and postprandial insulin concentrations (21.5 mU/L (95% CI 1.8-41.2)). Conclusion Postprandial metabolites identified T2D patients as good as fasting metabolites and exhibited enhanced signals for IFG stratification, which offers a proof of concept that metabolomics research should not focus on the fasting state alone.
AU  - Li-Gao, R.*
AU  - de Mutsert, R.*
AU  - Rensen, P.C.N.*
AU  - van Klinken, J.B.*
AU  - Prehn, C.
AU  - Adamski, J.*
AU  - van Hylckama Vlieg, A.*
AU  - den Heijer, M.*
AU  - le Cessie, S.*
AU  - Rosendaal, F.R.*
AU  - van Dijk, K.W.*
AU  - Mook-Kanamori, D.O.*
C1  - 52563
C2  - 44121
CY  - New York
TI  - Postprandial metabolite profiles associated with type 2 diabetes clearly stratify individuals with impaired fasting glucose.
JO  - Metabolomics
VL  - 14
IS  - 1
PB  - Springer
PY  - 2018
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction Global metabolomics analyses using body fluids provide valuable results for the understanding and prediction of diseases. However, the mechanism of a disease is often tissue-based and it is advantageous to analyze metabolomic changes directly in the tissue. Metabolomics from tissue samples faces many challenges like tissue collection, homogenization, and metabolite extraction. Objectives We aimed to establish a metabolite extraction protocol optimized for tissue metabolite quantification by the targeted metabolomics AbsoluteIDQ (TM) p180 Kit (Biocrates). The extraction method should be non-selective, applicable to different kinds and amounts of tissues, monophasic, reproducible, and amenable to high throughput. Methods We quantified metabolites in samples of eleven murine tissues after extraction with three solvents (methanol, phosphate buffer, ethanol/phosphate buffer mixture) in two tissue to solvent ratios and analyzed the extraction yield, ionization efficiency, and reproducibility. Results We found methanol and ethanol/phosphate buffer to be superior to phosphate buffer in regard to extraction yield, reproducibility, and ionization efficiency for all metabolites measured. Phosphate buffer, however, outperformed both organic solvents for amino acids and biogenic amines but yielded unsatisfactory results for lipids. The observed matrix effects of tissue extracts were smaller or in a similar range compared to those of human plasma. Conclusion We provide for each murine tissue type an optimized high-throughput metabolite extraction protocol, which yields the best results for extraction, reproducibility, and quantification of metabolites in the p180 kit. Although the performance of the extraction protocol was monitored by the p180 kit, the protocol can be applicable to other targeted metabolomics assays.
AU  - Zukunft, S.
AU  - Prehn, C.
AU  - Röhring, C.*
AU  - Möller, G.
AU  - Hrabě de Angelis, M.
AU  - Adamski, J.
AU  - Tokarz, J.
C1  - 52694
C2  - 44104
CY  - New York
TI  - High-throughput extraction and quantification method for targeted metabolomics in murine tissues.
JO  - Metabolomics
VL  - 14
IS  - 1
PB  - Springer
PY  - 2018
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - INTRODUCTION: Brown adipose tissue (BAT) recently emerged as a potential therapeutic target in the treatment of obesity and associated disorders due to its fat-burning capacity. The current gold standard in assessing BAT activity is [(18)F]FDG PET-CT scan, which has severe limitations including radiation exposure, being expensive, and being labor-intensive. Therefore, indirect markers are needed of human BAT activity and volume. OBJECTIVE: We aimed to identify metabolites in serum that are associated with BAT volume and activity in men. METHODS: We assessed 163 metabolites in fasted serum of a cohort of twenty-two healthy lean men (age 24.1 (21.7-26.6) years, BMI 22.1 (20.5-23.4) kg/m(2)) who subsequently underwent a cold-induced [(18)F]FDG PET-CT scan to assess BAT volume and activity. In addition, we included three replication cohorts consisting of in total thirty-seven healthy lean men that were similar with respect to age and BMI compared to the discovery cohort. RESULTS: After correction for multiple testing, fasting concentrations of lysophosphatidylcholine-acyl (LysoPC-acyl) C16:1, LysoPC-acyl C16:0 and phosphatidylcholine-diacyl C32:1 showed strong positive correlations with BAT volume (β= 116 (85-148) mL, R(2) = 0.81, p = 4.6 × 10(-7); β = 79 (93-119) mL, R(2) = 0.57, p = 5.9 × 10(-4) and β= 91 (40-141) mL, R(2) = 0.52, p = 1.0 × 10(-3), respectively) as well as with BAT activity (β= 0.20 (0.11-0.29) g/mL, R(2) = 0.59, p = 1.9 × 10(-4); β = 0.15 (0.06-0.23) g/mL, R(2) = 0.47, p = 2.0 × 10(-3) and β= 0.13 (0.01-0.25) g/mL, R(2) = 0.28, p = 0.04, respectively). When tested in three independent replication cohorts (total n = 37), the association remained significant between LysoPC-acyl C16:0 and BAT activity in a pooled analysis (β= 0.15 (0.07-0.23) g/mL, R(2) = 0.08, p = 4.2 × 10(-4)). CONCLUSIONS: LysoPC-acyl C16:0 is associated with BAT activity in men. Since BAT is regarded as a promising tool in the battle against obesity and related disorders, the identification of such a noninvasive marker is highly relevant.
AU  - Boon, M.R.*
AU  - Bakker, L.E.H.*
AU  - Prehn, C.
AU  - Adamski, J.
AU  - Vosselman, M.J.*
AU  - Jazet, I.M.*
AU  - Arias-Bouda, L.M.*
AU  - van Lichtenbelt, W.D.*
AU  - van Dijk, K.W.*
AU  - Rensen, P.C.*
AU  - Mook-Kanamori, D.O.*
C1  - 50752
C2  - 42527
CY  - New York
TI  - LysoPC-acyl C16:0 is associated with brown adipose tissue activity in men.
JO  - Metabolomics
VL  - 13
IS  - 5
PB  - Springer
PY  - 2017
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Few studies have investigated the influence of storage conditions on urine samples and none of them used targeted mass spectrometry (MS). Objectives: We investigated the stability of metabolite profiles in urine samples under different storage conditions using targeted metabolomics. Methods: Pooled, fasting urine samples were collected and stored at −80 °C (biobank standard), −20 °C (freezer), 4 °C (fridge), ~9 °C (cool pack), and ~20 °C (room temperature) for 0, 2, 8 and 24 h. Metabolite concentrations were quantified with MS using the AbsoluteIDQ™ p150 assay. We used the Welch-Satterthwaite-test to compare the concentrations of each metabolite. Mixed effects linear regression was used to assess the influence of the interaction of storage time and temperature. Results: The concentrations of 63 investigated metabolites were stable at −20 and 4 °C for up to 24 h when compared to samples immediately stored at −80 °C. When stored at ~9 °C for 24 h, few amino acids (Arg, Val and Leu/Ile) significantly decreased by 40% in concentration (P < 7.9E−04); for an additional three metabolites (Ser, Met, Hexose H1) when stored at ~20 °C reduced up to 60% in concentrations. The concentrations of four more metabolites (Glu, Phe, Pro, and Thr) were found to be significantly influenced when considering the interaction between exposure time and temperature. Conclusion: Our findings indicate that 78% of quantified metabolites were stable for all examined storage conditions. Particularly, some amino acid concentrations were sensitive to changes after prolonged storage at room temperature. Shipping or storing urine samples on cool packs or at room temperature for more than 8 h and multiple numbers of freeze and thaw cycles should be avoided.
AU  - Rotter, M.
AU  - Brandmaier, S.
AU  - Prehn, C.
AU  - Adam, J.
AU  - Rabstein, S.*
AU  - Gawrych, K.*
AU  - Brüning, T*
AU  - Illig, T.
AU  - Lickert, H.
AU  - Adamski, J.
AU  - Wang-Sattler, R.
C1  - 50064
C2  - 42056
CY  - New York
TI  - Stability of targeted metabolite profiles of urine samples under different storage conditions.
JO  - Metabolomics
VL  - 13
IS  - 1
PB  - Springer
PY  - 2017
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Cinnamon exerts insulin-enhancing activity in vitro and was demonstrated to improve blood glucose and lipid profiles in several human studies. Such effects may have an impact on metabolically stressed cows. Objective: To study the effects of cinnamon supplementation during the transition from late pregnancy to early lactation on the metabolism in dairy cows. Methods: Twenty-four Holstein cows (n = 8/group) were assigned to either the control group (CTR; without supplementation) or the supplementation groups [supplemental cinnamon at 20 (LCIN) or 40 (HCIN) g/cow per day (d)] from 28 d before calving until 21 d thereafter. Blood samples were assayed for glucose, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin; an index estimating insulin sensitivity (RQUICKI) was calculated. The serum metabolome was characterized in the samples collected from d 14 using a non-targeted approach. Results: The serum concentrations of glucose and insulin did not differ among groups and followed a similar pattern over time. The serum NEFA concentrations were greater in LCIN (d 2, 7, and 14) and HCIN (d 14) than in CTR. On d 14 and 21, LCIN and HCIN had greater serum BHBA concentrations than CTR cows. The top 10 metabolites identified with significantly higher levels in the supplemented than the CTR cows were related to fatty acid metabolism. Conclusion: The data suggest lipolytic and ketogenic effects of cinnamon supplementation in dairy cows during the transition from late gestation to early lactation. The fatty acid metabolites found elevated in the supplemented cows point towards impaired mitochondrial fatty acid β-oxidation.
AU  - Sadri, H.*
AU  - Alizadeh, A.R.*
AU  - Vakili, H.*
AU  - Ghorbani, A.*
AU  - Bruckmaier, R.M.*
AU  - Artati, A.
AU  - Adamski, J.
AU  - Sauerwein, H.P.*
C1  - 50534
C2  - 42384
CY  - New York
TI  - Cinnamon: Does it hold its promises in cows? Using non-targeted blood serum metabolomics profiling to test the effects of feeding cinnamon to dairy cows undergoing lactation-induced insulin resistance.
JO  - Metabolomics
VL  - 13
IS  - 3
PB  - Springer
PY  - 2017
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Background to metabolomics: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Conclusions: Key scientific concepts and recommendations for precision medicine: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
AU  - Beger, R.D.*
AU  - Dunn, W.A, Jr.*
AU  - Schmidt, M.A.*
AU  - Gross, S.S.*
AU  - Kirwan, J.A.*
AU  - Cascante, M.*
AU  - Brennan, L.*
AU  - Wishart, D.S.*
AU  - Oresič, M.*
AU  - Hankemeier, T.*
AU  - Broadhurst, D.I.*
AU  - Lane, A.N.*
AU  - Suhre, K.*
AU  - Kastenmüller, G.
AU  - Sumner, S.J.*
AU  - Thiele, I.*
AU  - Fiehn, O.*
AU  - Kaddurah-Daouk, R.*
C1  - 49472
C2  - 30599
CY  - New York
SP  - 149
TI  - Metabolomics enables precision medicine: “A White Paper, Community Perspective”.
JO  - Metabolomics
VL  - 12
IS  - 10
PB  - Springer
PY  - 2016
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Type 2 diabetes (T2D) is a multifactorial disease resulting from a complex interaction between environmental and genetic risk factors. Metabolomics provide a logical framework that reflects the functional endpoints of biological processes being triggered by genetic information and various external influences. Objectives: Identification of metabolite biomarkers can shed insight into etiological pathways and improve the prediction of disease risk. Here, we aimed to identify serum metabolites as putative biomarkers for T2D and their association with genetic variants in the Korean population. Methods: A targeted metabolomics approach was employed to quantify serum metabolites for 2240 participants in the Korea Association REsource (KARE) cohort. T2D-related metabolites were identified by statistical methods including multivariable linear and logistic regression, and were independently replicated in the Cooperative Health Research in the Region of Augsburg (KORA) cohort. Additionally, by combining a genome wide association study (GWAS) with metabolomics, genetic variants associated with the identified T2D-related metabolites were uncovered. Results: 123 metabolites were quantified from fasting serum samples and four metabolites, hexadecanoylcarnitine (C16), glycine, lysophosphatidylcholine acyl C18:2 (lysoPC a C18:2), and phosphatidylcholine acyl-alkyl C36:0 (PC ae C36:0), were significantly altered in T2D compared to non-T2D subjects (after the Bonferroni correction for multiple testing with P < 4.07E − 04, α = 0.05). Among them, C16, glycine, and lysoPC a C18:2 were independently replicated in the KORA cohort. Alterations of these metabolites were associated with ten genetic loci including six that were previously implicated in T2D or obesity. Conclusion: Using a targeted-metabolomics and in combination with GWAS approach, we identified three serum metabolites associated with risk of T2D in both the KARE and KORA cohort and discovered ten genetic variants in relation to the identified metabolites. These findings provide a better understanding to develop novel preventive strategies for T2D in the Korean population.
AU  - Lee, H.S.*
AU  - Xu, T.
AU  - Lee, Y.*
AU  - Kim, N.H.*
AU  - Kim, Y.J.*
AU  - Kim, J.M.*
AU  - Cho, S.Y.*
AU  - Kim, K.Y.*
AU  - Nam, M.*
AU  - Adamski, J.
AU  - Suhre, K.
AU  - Rathmann, W.G.*
AU  - Peters, A.
AU  - Wang-Sattler, R.
AU  - Han, B.G.*
AU  - Kim, B.J.*
C1  - 49700
C2  - 40831
CY  - New York
TI  - Identification of putative biomarkers for type 2 diabetes using metabolomics in the Korea Association REsource (KARE) cohort.
JO  - Metabolomics
VL  - 12
IS  - 12
PB  - Springer
PY  - 2016
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can systematically stimulate (MLF+ phenotype) or inhibit (MLF−) bacteria and the MLF process as a function of numerous winemaking practices, but the underlying molecular evidence still remains a mystery. Objectives: The goal of the study was to elucidate such evidence by the direct comparison of extracellular metabolic profiles of MLF+ and MLF− phenotypes. Methods: We have applied a non-targeted metabolomic approach combining ultrahigh-resolution FT-ICR-MS analysis, powerful statistical tools and a comprehensive wine metabolite database. Results: We discovered around 2500 unknown masses and 800 putative biomarkers involved in phenotypic distinction. For the putative biomarkers, we also developed a biomarker identification workflow and elucidated the exact structure (by UPLC-Q-ToF–MS2) and/or exact physiological impact (by in vitro tests) of several novel biomarkers, such as D-gluconic acid, citric acid, trehalose and tripeptide Pro-Phe-Val. In addition to valid biomarkers, molecular evidence was reflected by unprecedented chemical diversity (around 3000 discriminant masses) that characterized both the yeast phenotypes. While distinct chemical families such as phenolic compounds, carbohydrates, amino acids and peptides characterize the extracellular metabolic profiles of the MLF+ phenotype, the MLF− phenotype is associated with sulphur-containing peptides. Conclusion: The non-targeted approach used in this study played an important role in finding new and unexpected molecular evidence.
AU  - Liu, Y.
AU  - Forcisi, S.
AU  - Harir, M.
AU  - Deleris-Bou, M.*
AU  - Krieger-Weber, S.*
AU  - Lucio, M.
AU  - Longin, C.*
AU  - Degueurce, C.*
AU  - Gougeon, R.D.*
AU  - Schmitt-Kopplin, P.
AU  - Alexandre, H.*
C1  - 48159
C2  - 39985
CY  - New York
TI  - New molecular evidence of wine yeast-bacteria interaction unraveled by non-targeted exometabolomic profiling.
JO  - Metabolomics
VL  - 12
IS  - 4
PB  - Springer
PY  - 2016
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Metabolic changes have been frequently associated with Huntington’s disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress to Huntington’s disease patients especially when brain or other tissue samples are difficult to collect. Objectives: We investigated the levels of 163 metabolites in HD patient and control serum samples in order to identify disease related changes. Additionally, we integrated the metabolomics data with our previously published next generation sequencing-based gene expression data from the same patients in order to interconnect the metabolomics changes with transcriptional alterations. Methods: This analysis was performed using targeted metabolomics and flow injection electrospray ionization tandem mass spectrometry in 133 serum samples from 97 Huntington’s disease patients (29 pre-symptomatic and 68 symptomatic) and 36 controls. Results: By comparing HD mutation carriers with controls we identified 3 metabolites significantly changed in HD (serine and threonine and one phosphatidylcholine—PC ae C36:0) and an additional 8 phosphatidylcholines (PC aa C38:6, PC aa C36:0, PC ae C38:0, PC aa C38:0, PC ae C38:6, PC ae C42:0, PC aa C36:5 and PC ae C36:0) that exhibited a significant association with disease severity. Using workflow based exploitation of pathway databases and by integrating our metabolomics data with our gene expression data from the same patients we identified 4 deregulated phosphatidylcholine metabolism related genes (ALDH1B1, MBOAT1, MTRR and PLB1) that showed significant association with the changes in metabolite concentrations. Conclusion: Our results support the notion that phosphatidylcholine metabolism is deregulated in HD blood and that these metabolite alterations are associated with specific gene expression changes.
AU  - Mastrokolias, A.*
AU  - Pool, R.*
AU  - Mina, E.*
AU  - Hettne, K.M.*
AU  - van Duijn, E.*
AU  - van der Mast, R.C.*
AU  - van Ommen, G.J.*
AU  - ‘t Hoen, P.A.C.*
AU  - Prehn, C.
AU  - Adamski, J.
AU  - van Roon-Mom, W.*
C1  - 49217
C2  - 34180
CY  - New York
TI  - Integration of targeted metabolomics and transcriptomics identifies deregulation of phosphatidylcholine metabolism in Huntington’s disease peripheral blood samples.
JO  - Metabolomics
VL  - 12
IS  - 8
PB  - Springer
PY  - 2016
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number. Objectives: We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles. Methods: We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile. Results: We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82–97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells. Conclusion: We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.
AU  - Muschet, C.
AU  - Möller, G.
AU  - Prehn, C.
AU  - Hrabě de Angelis, M.
AU  - Adamski, J.
AU  - Tokarz, J.
C1  - 49552
C2  - 30098
CY  - New York
TI  - Removing the bottlenecks of cell culture metabolomics: Fast normalization procedure, correlation of metabolites to cell number, and impact of the cell harvesting method.
JO  - Metabolomics
VL  - 12
IS  - 10
PB  - Springer
PY  - 2016
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Introduction: A general detrimental effect of smoking during pregnancy on the health of newborn children is well-documented, but the detailed mechanisms remain elusive. Objectives: Beside the specific influence of environmental tobacco smoke derived toxicants on developmental regulation the impact on the metabolism of newborn children is of particular interest, first as a general marker of foetal development and second due to its potential predictive value for the later occurrence of metabolic diseases. Methods: Tobacco smoke exposure information from a questionnaire was confirmed by measuring the smoking related metabolites S-Phenyl mercapturic acid, S-Benzyl mercapturic acid and cotinine in maternal urine by LC–MS/MS. The impact of smoking on maternal endogenous serum metabolome and children’s cord blood metabolome was assessed in a targeted analysis of 163 metabolites by an LC–MS/MS based assay. The anti-oxidative status of maternal serum samples was analysed by chemoluminiscence based method. Results: Here we present for the first time results of a metabolomic assessment of the cordblood of 40 children and their mothers. Several analytes from the group of phosphatidylcholines, namely PCaaC28:1, PCaaC32:3, PCaeC30:1, PCaeC32:2, PCaeC40:1, and sphingomyelin SM C26:0, differed significantly in mothers and children’s sera depending on smoking status. In serum of smoking mothers the antioxidative capacity of water soluble compounds was not significantly changed while there was a significant decrease in the lipid fraction. Conclusion: Our data give evidence that smoking during pregnancy alters both the maternal and children’s metabolome. Whether the different pattern found in adults compared to newborn children could be related to different disease outcomes should be in the focus of future studies.
AU  - Rolle-Kampczyk, U.E.*
AU  - Krumsiek, J.
AU  - Otto, W.*
AU  - Röder, S.W.*
AU  - Kohajda, T.*
AU  - Borte, M.*
AU  - Theis, F.J.
AU  - Lehmann, I.*
AU  - von Bergen, M.*
C1  - 48157
C2  - 39987
CY  - New York
TI  - Metabolomics reveals effects of maternal smoking on endogenous metabolites from lipid metabolism in cord blood of newborns.
JO  - Metabolomics
VL  - 12
IS  - 4
PB  - Springer
PY  - 2016
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Recently research provides evidence that blood metabolite profiles predicted type 2 diabetes. We aimed to assess the relation of urine metabolites measured via nuclear magnetic resonance spectroscopy with incident type 2 diabetes in a sample of 1353 men and 1356 women. Within 5 years, 87 men and 50 women developed diabetes. Five and 16 urine metabolites were associated with incident diabetes in men and women, respectively. Only three of these metabolites (glucose, lactate and glycine) were found in both sexes. In women, e.g. acetate, carnitine, N,N-dimethylglycine, trigonelline, 3-hydroxyisovalerate, alanine, formate, glycolate, trimethylamine N-oxide and tau-methylhistidine were positively related with diabetes. Receiver operating characteristic (ROC) analysis revealed that compared with a standard model, a model additionally adjusted for urine glucose, trigonelline and trimethylamine N-oxide levels showed a better discrimination between incident diabetes cases and non-cases in women (AUC = 0.874 and 0.903, p = 0.019). In men, valine and 4-hydroxyphenylacetate were found as markers of diabetes. However, ROC analysis did not reveal any improvement in discrimination based on urine metabolites. In conclusion, we confirmed the potential of metabolomics to assess the risk of type 2 diabetes and detected pronounced sex differences. Moreover, we demonstrated the practicability of spot urine samples as potential non-invasive diabetes screening approach.
AU  - Friedrich, N.*
AU  - Budde, K.*
AU  - Suhre, K.
AU  - Völker, U.*
AU  - John, U.*
AU  - Felix, S.B.*
AU  - Kroemer, H.K.*
AU  - Grabe, H.J.*
AU  - Völzke, H.*
AU  - Nauck, M.*
AU  - Wallaschofski, H.*
C1  - 45571
C2  - 37347
CY  - New York
SP  - 1405-1415
TI  - Sex differences in urine metabolites related with risk of diabetes using NMR spectroscopy: Results of the study of health in pomerania.
JO  - Metabolomics
VL  - 11
IS  - 5
PB  - Springer
PY  - 2015
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.
AU  - Krumsiek, J.
AU  - Mittelstraß, K.
AU  - Do, K.T.
AU  - Stückler, F.
AU  - Ried, J.S.
AU  - Adamski, J.
AU  - Peters, A.
AU  - Illig, T.*
AU  - Kronenberg, F.*
AU  - Friedrich, N.*
AU  - Nauck, M.*
AU  - Pietzner, M.*
AU  - Mook-Kanamori, D.O.*
AU  - Suhre, K.
AU  - Gieger, C.
AU  - Grallert, H.
AU  - Theis, F.J.
AU  - Kastenmüller, G.
C1  - 46559
C2  - 37636
SP  - 1815-1833
TI  - Gender-specific pathway differences in the human serum metabolome.
JO  - Metabolomics
VL  - 11
IS  - 6
PY  - 2015
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum urate levels highly differ between men and women; however the underlying biological processes in its regulation are still not completely understood and are assumed to result from a complex interplay between genetic, environmental and lifestyle factors. In order to describe the metabolic vicinity of serum urate, we analyzed 355 metabolites in 1,764 individuals of the population-based KORA F4 study and constructed a metabolite network around serum urate using Gaussian Graphical Modeling in a hypothesis-free approach. We subsequently investigated the effect of sex and urate lowering medication on all 38 metabolites assigned to the network. Within the resulting network three main clusters could be detected around urate, including the well-known pathway of purine metabolism, as well as several dipeptides, a group of essential amino acids, and a group of steroids. Of the 38 assigned metabolites, 25 showed strong differences between sexes. Association with uricostatic medication intake was not only confined to purine metabolism but seen for seven metabolites within the network. Our findings highlight pathways that are important in the regulation of serum urate and suggest that dipeptides, amino acids, and steroid hormones are playing a role in its regulation. The findings might have an impact on the development of specific targets in the treatment and prevention of hyperuricemia.
AU  - Albrecht, E.
AU  - Waldenberger, M.
AU  - Krumsiek, J.
AU  - Evans, A.M.*
AU  - Jeratsch, U.
AU  - Breier, M.
AU  - Adamski, J.
AU  - Koenig, W.*
AU  - Zeilinger, S.
AU  - Fuchs, C.
AU  - Klopp, N.
AU  - Theis, F.J.
AU  - Wichmann, H.-E.
AU  - Suhre, K.
AU  - Illig, T.
AU  - Strauch, K.
AU  - Peters, A.
AU  - Gieger, C.
AU  - Kastenmüller, G.
AU  - Döring, A.
AU  - Meisinger, C.
C1  - 25966
C2  - 32007
CY  - New York
SP  - 141-151
TI  - Metabolite profiling reveals new insights into the regulation of serum urate in humans.
JO  - Metabolomics
VL  - 10
IS  - 1
PB  - Springer
PY  - 2014
SN  - 1573-3882
ER  - 

TY  - JOUR
AB  - Reproducible quantification of metabolites in tissue samples is of high importance for characterization of animal models and identification of metabolic changes that occur in different tissue types in specific diseases. However, the extraction of metabolites from tissue is often the most labor-intensive and error-prone step in metabolomics studies. Here, we report the development of a standardized high-throughput method for rapid and reproducible extraction of metabolites from multiple tissue samples from different organs of several species. The method involves a bead-based homogenizer in combination with a simple extraction protocol and is compatible with state-ofthe-art metabolomics kit technology for quantitative and targeted flow injection tandem mass spectrometry. We analyzed different extraction solvents for both reproducibility as well as suppression effects for a range of different animal tissue types including liver, kidney, muscle, brain, and fat tissue from mouse and bovine. In this study, we show that for most metabolites a simple methanolic extraction is best suited for reliable results. An additional extraction step with phosphate buffer can be used to improve the extraction yields for a few more polar metabolites. We provide a verified tissue extraction setup to be used with different indications. Our results demonstrate that this high-throughput procedure provides a basis for metabolomic assays with a wide spectrum of metabolites. The developed method can be used for tissue extraction setup for different indications like studies of metabolic syndrome, obesity, diabetes or cardiovascular disorders and nutrient transformation in livestock.
AU  - Römisch-Margl, W.
AU  - Prehn, C.
AU  - Bogumil, R.*
AU  - Röhring, C.*
AU  - Suhre, K.
AU  - Adamski, J.
C1  - 6831
C2  - 29335
SP  - 133-142
TI  - Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics.
JO  - Metabolomics
VL  - 8
IS  - 1
PB  - Springer
PY  - 2012
SN  - 1573-3882
ER  -