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Article ; Online: Salivary metabolites associated with a 5-year tooth loss identified in a population-based setting

Leonie Andörfer / Birte Holtfreter / Stefan Weiss / Rutger Matthes / Vinay Pitchika / Carsten Oliver Schmidt / Stefanie Samietz / Gabi Kastenmüller / Matthias Nauck / Uwe Völker / Henry Völzke / Laszlo N. Csonka / Karsten Suhre / Maik Pietzner / Thomas Kocher

BMC Medicine, Vol 19, Iss 1, Pp 1-

2021 Volume 13

Abstract: Abstract Background Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways ... ...

Abstract	Abstract Background Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss. Methods We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models. Results We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation. Conclusions Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.
Keywords	Metabolomics ; Periodontitis ; Progression ; Tooth loss ; 2-Pyrrolidineacetic acid ; Butyrylputrescine ; Medicine ; R
Subject code	500
Language	English
Publishing date	2021-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring

Raffael Ott / Xenia Pawlow / Andreas Weiß / Anna Hofelich / Melanie Herbst / Nadine Hummel / Cornelia Prehn / Jerzy Adamski / Werner Römisch-Margl / Gabi Kastenmüller / Anette-G. Ziegler / Sandra Hummel

International Journal of Molecular Sciences, Vol 21, Iss 9647, p

2020 Volume 9647

Abstract: Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show ... ...

Abstract	Shared metabolomic patterns at delivery have been suggested to underlie the mother-to-child transmission of adverse metabolic health. This study aimed to investigate whether mothers with gestational diabetes mellitus (GDM) and their offspring show similar metabolomic patterns several years postpartum. Targeted metabolomics (including 137 metabolites) was performed in plasma samples obtained during an oral glucose tolerance test from 48 mothers with GDM and their offspring at a cross-sectional study visit 8 years after delivery. Partial Pearson’s correlations between the area under the curve (AUC) of maternal and offspring metabolites were calculated, yielding so-called Gaussian graphical models. Spearman’s correlations were applied to investigate correlations of body mass index (BMI), Matsuda insulin sensitivity index (ISI-M), dietary intake, and physical activity between generations, and correlations of metabolite AUCs with lifestyle variables. This study revealed that BMI, ISI-M, and the AUC of six metabolites (carnitine, taurine, proline, SM(-OH) C14:1, creatinine, and PC ae C34:3) were significantly correlated between mothers and offspring several years postpartum. Intergenerational metabolite correlations were independent of shared BMI, ISI-M, age, sex, and all other metabolites. Furthermore, creatinine was correlated with physical activity in mothers. This study suggests that there is long-term metabolic programming in the offspring of mothers with GDM and informs us about targets that could be addressed by future intervention studies.
Keywords	gestational diabetes ; overweight ; intergenerational metabolomics ; lifestyle ; Biology (General) ; QH301-705.5 ; Chemistry ; QD1-999
Subject code	796
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism

Valentina S. Klaus / Sonja C. Schriever / José Manuel Monroy Kuhn / Andreas Peter / Martin Irmler / Janina Tokarz / Cornelia Prehn / Gabi Kastenmüller / Johannes Beckers / Jerzy Adamski / Alfred Königsrainer / Timo D. Müller / Martin Heni / Matthias H. Tschöp / Paul T. Pfluger / Dominik Lutter

Molecular Metabolism, Vol 53, Iss , Pp 101295- (2021)

2021

Abstract: Objective: Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap ... ...

Abstract	Objective: Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce. Methods: We present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways. Results: By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). Conclusion: Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.
Keywords	Data integration ; Hepatic steatosis ; Multi-omics ; Systems biology ; Internal medicine ; RC31-1245
Subject code	004
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comprehensive metabolic profiling of chronic low-grade inflammation among generally healthy individuals

Maik Pietzner / Anne Kaul / Ann-Kristin Henning / Gabi Kastenmüller / Anna Artati / Markus M. Lerch / Jerzy Adamski / Matthias Nauck / Nele Friedrich

BMC Medicine, Vol 15, Iss 1, Pp 1-

2017 Volume 12

Abstract: Abstract Background Inflammation occurs as an immediate protective response of the immune system to a harmful stimulus, whether locally confined or systemic. In contrast, a persisting, i.e., chronic, inflammatory state, even at a low-grade, is a well- ... ...

Abstract	Abstract Background Inflammation occurs as an immediate protective response of the immune system to a harmful stimulus, whether locally confined or systemic. In contrast, a persisting, i.e., chronic, inflammatory state, even at a low-grade, is a well-known risk factor in the development of common diseases like diabetes or atherosclerosis. In clinical practice, laboratory markers like high-sensitivity C-reactive protein (hsCRP), white blood cell count (WBC), and fibrinogen, are used to reveal inflammatory processes. In order to gain a deeper insight regarding inflammation-related changes in metabolism, the present study assessed the metabolic patterns associated with alterations in inflammatory markers. Methods Based on mass spectrometry and nuclear magnetic resonance spectroscopy we determined a comprehensive panel of 613 plasma and 587 urine metabolites among 925 apparently healthy individuals. Associations between inflammatory markers, namely hsCRP, WBC, and fibrinogen, and metabolite levels were tested by linear regression analyses controlling for common confounders. Additionally, we tested for a discriminative signature of an advanced inflammatory state using random forest analysis. Results HsCRP, WBC, and fibrinogen were significantly associated with 71, 20, and 19 plasma and 22, 3, and 16 urine metabolites, respectively. Identified metabolites were related to the bradykinin system, involved in oxidative stress (e.g., glutamine or pipecolate) or linked to the urea cycle (e.g., ornithine or citrulline). In particular, urine 3’-sialyllactose was found as a novel metabolite related to inflammation. Prediction of an advanced inflammatory state based solely on 10 metabolites was well feasible (median AUC: 0.83). Conclusions Comprehensive metabolic profiling confirmed the far-reaching impact of inflammatory processes on human metabolism. The identified metabolites included not only those already described as immune-modulatory but also completely novel patterns. Moreover, the observed alterations provide molecular ...
Keywords	Inflammation ; White blood cell count ; C-reactive protein ; Fibrinogen ; Metabolomics ; Mass spectrometry ; Medicine ; R
Subject code	610
Language	English
Publishing date	2017-11-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Metabomatching

Rico Rueedi / Roger Mallol / Johannes Raffler / David Lamparter / Nele Friedrich / Peter Vollenweider / Gérard Waeber / Gabi Kastenmüller / Zoltán Kutalik / Sven Bergmann

PLoS Computational Biology, Vol 13, Iss 12, p e

Using genetic association to identify metabolites in proton NMR spectroscopy.

2017 Volume 1005839

Abstract: A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from ... ...

Abstract	A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited.
Keywords	Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2017-12-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Dynamic modelling of an ACADS genotype in fatty acid oxidation - Application of cellular models for the analysis of common genetic variants.

Kerstin Matejka / Ferdinand Stückler / Michael Salomon / Regina Ensenauer / Eva Reischl / Lena Hoerburger / Harald Grallert / Gabi Kastenmüller / Annette Peters / Hannelore Daniel / Jan Krumsiek / Fabian J Theis / Hans Hauner / Helmut Laumen

PLoS ONE, Vol 14, Iss 5, p e

2019 Volume 0216110

Abstract: BACKGROUND:Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for ... ...

Abstract	BACKGROUND:Genome-wide association studies of common diseases or metabolite quantitative traits often identify common variants of small effect size, which may contribute to phenotypes by modulation of gene expression. Thus, there is growing demand for cellular models enabling to assess the impact of gene regulatory variants with moderate effects on gene expression. Mitochondrial fatty acid oxidation is an important energy metabolism pathway. Common noncoding acyl-CoA dehydrogenase short chain (ACADS) gene variants are associated with plasma C4-acylcarnitine levels and allele-specific modulation of ACADS expression may contribute to the observed phenotype. METHODS AND FINDINGS:We assessed ACADS expression and intracellular acylcarnitine levels in human lymphoblastoid cell lines (LCL) genotyped for a common ACADS variant associated with plasma C4-acylcarnitine and found a significant genotype-dependent decrease of ACADS mRNA and protein. Next, we modelled gradual decrease of ACADS expression using a tetracycline-regulated shRNA-knockdown of ACADS in Huh7 hepatocytes, a cell line with high fatty acid oxidation-(FAO)-capacity. Assessing acylcarnitine flux in both models, we found increased C4-acylcarnitine levels with decreased ACADS expression levels. Moreover, assessing time-dependent changes of acylcarnitine levels in shRNA-hepatocytes with altered ACADS expression levels revealed an unexpected effect on long- and medium-chain fatty acid intermediates. CONCLUSIONS:Both, genotyped LCL and regulated shRNA-knockdown are valuable tools to model moderate, gradual gene-regulatory effects of common variants on cellular phenotypes. Decreasing ACADS expression levels modulate short and surprisingly also long/medium chain acylcarnitines, and may contribute to increased plasma acylcarnitine levels.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A strategy to incorporate prior knowledge into correlation network cutoff selection

Elisa Benedetti / Maja Pučić-Baković / Toma Keser / Nathalie Gerstner / Mustafa Büyüközkan / Tamara Štambuk / Maurice H. J. Selman / Igor Rudan / Ozren Polašek / Caroline Hayward / Hassen Al-Amin / Karsten Suhre / Gabi Kastenmüller / Gordan Lauc / Jan Krumsiek

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 12

Abstract: Correlation network inference is typically based on the significance of the correlation coefficients, but this procedure is not guaranteed to capture biological mechanisms. Here, the authors develop a cutoff selection algorithm that maximizes the overlap ...

Abstract	Correlation network inference is typically based on the significance of the correlation coefficients, but this procedure is not guaranteed to capture biological mechanisms. Here, the authors develop a cutoff selection algorithm that maximizes the overlap between inferred networks and prior knowledge.
Keywords	Science ; Q
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Author Correction

Maik Pietzner / Eleanor Wheeler / Julia Carrasco-Zanini / Johannes Raffler / Nicola D. Kerrison / Erin Oerton / Victoria P. W. Auyeung / Jian’an Luan / Chris Finan / Juan P. Casas / Rachel Ostroff / Steve A. Williams / Gabi Kastenmüller / Markus Ralser / Eric R. Gamazon / Nicholas J. Wareham / Aroon D. Hingorani / Claudia Langenberg

Nature Communications, Vol 12, Iss 1, Pp 1-

Genetic architecture of host proteins involved in SARS-CoV-2 infection

2021 Volume 1

Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21370- ... ...

Abstract	A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21370-6
Keywords	Science ; Q
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Genetic architecture of host proteins involved in SARS-CoV-2 infection

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 14

Abstract: Finding effective treatments for COVID-19 depends upon understanding genetic regulation of proteins involved in SARS-CoV-2 infection and host response. Here, the authors identify genetic variants linked to expression of such proteins, data which could ... ...

Abstract	Finding effective treatments for COVID-19 depends upon understanding genetic regulation of proteins involved in SARS-CoV-2 infection and host response. Here, the authors identify genetic variants linked to expression of such proteins, data which could lead to the discovery of therapeutic targets.
Keywords	Science ; Q
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: metaP-Server

Gabi Kastenmüller / Werner Römisch-Margl / Brigitte Wägele / Elisabeth Altmaier / Karsten Suhre

Journal of Biomedicine and Biotechnology, Vol

A Web-Based Metabolomics Data Analysis Tool

2011 Volume 2011

Abstract: Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high- ... ...

Abstract	Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developed metaP-server to facilitate data interpretation. metaP-server provides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided. metaP-server is freely accessible at http://metabolomics.helmholtz-muenchen.de/metap2/.
Keywords	Biotechnology ; TP248.13-248.65 ; Medicine ; R
Subject code	310
Language	English
Publishing date	2011-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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