The degradation of nucleotide triphosphates extracted under boiling ethanol conditions is prevented by the yeast cellular matrix

Gil, Andrés Barge; Siegel, David J.; Bonsing-Vedelaar, Silke; Permentier, Hjalmar P.; Reijngoud, Dirk-Jan; Dekker, Frank J.; Bischoff, Rainer

doi:10.1007/s11306-016-1140-4

Cited by 51 publications

(75 citation statements)

References 26 publications

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“…Contemporary metabolomic fingerprinting is relatively fast, providing extensive information about relationships among samples, chemical and functional diversity of living organisms (Ivanišević et al 2011 ), and has important roles in: (a) discovery of novel bioactive compounds, (b) chemotaxonomic evaluation of organisms (Christensen et al 1999 ; dos Santos et al 2017 ; Farag et al 2012a , 2013b ; Ivanišević et al 2011 ; Jing et al 2015 ), (c) quality control of herbal preparations and natural products (Farag et al 2013a ; Farag and Wessjohann 2012 ), (d) elucidating causative relations between exogenous factors and metabolic changes in organisms (Allwood et al 2008 ; Krstic et al 2016 ; Shulaev et al 2008 ; Xie et al 2014 ), and (e) tracking metabolome differences influenced by geographic origin (Farag et al 2012b ; Krstic et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

et al. 2018

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IntroductionContemporary metabolomic fingerprinting is based on multiple spectrometric and chromatographic signals, used either alone or combined with structural and chemical information of metabolic markers at the qualitative and semiquantitative level. However, signal shifting, convolution, and matrix effects may compromise metabolomic patterns. Recent increase in the use of qualitative metabolomic data, described by the presence (1) or absence (0) of particular metabolites, demonstrates great potential in the field of metabolomic profiling and fingerprint analysis.ObjectivesThe aim of this study is a comprehensive evaluation of binary similarity measures for the elucidation of patterns among samples of different botanical origin and various metabolomic profiles.MethodsNine qualitative metabolomic data sets covering a wide range of natural products and metabolomic profiles were applied to assess 44 binary similarity measures for the fingerprinting of plant extracts and natural products. The measures were analyzed by the novel sum of ranking differences method (SRD), searching for the most promising candidates.ResultsBaroni-Urbani–Buser (BUB) and Hawkins–Dotson (HD) similarity coefficients were selected as the best measures by SRD and analysis of variance (ANOVA), while Dice (Di1), Yule, Russel-Rao, and Consonni-Todeschini 3 ranked the worst. ANOVA revealed that concordantly and intermediately symmetric similarity coefficients are better candidates for metabolomic fingerprinting than the asymmetric and correlation based ones. The fingerprint analysis based on the BUB and HD coefficients and qualitative metabolomic data performed equally well as the quantitative metabolomic profile analysis.ConclusionFingerprint analysis based on the qualitative metabolomic profiles and binary similarity measures proved to be a reliable way in finding the same/similar patterns in metabolomic data as that extracted from quantitative data.Electronic supplementary materialThe online version of this article (10.1007/s11306-018-1327-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

et al. 2018

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“…Another study applied an untargeted multi-platform (GC– and LC–MS) approach to measure the stool and plasma metabolomes of Nigerian children aged 6–48 months with SAM (21 with marasmus and 26 with kwashiorkor) and compared them to 11 hospital control children [ 51 ]. While no SAM-associated metabolic variation was identified in the fecal profiles the SAM and control children could be discriminated based on their plasma metabolomes.…”

Section: Metabolic Phenotyping Of Malnutrition During the First 1000 mentioning

confidence: 99%

“…In these animals, protein–energy undernutrition reduced the diversity of the intestinal microbiota and depleted the microbiome of genes that extract energy from non-digestible components [ 60 ]. However, no differences were observed in the fecal microbiota of Nigerian children with SAM compared to nourished hospital controls and no fecal metabolites derived from the gut microbiota were observed to change [ 51 ]. The limited sample size in this study may have prevented such differences from being detected.…”

Section: Metabolic Phenotyping Of Malnutrition During the First 1000 mentioning

confidence: 99%

Metabolic phenotyping of malnutrition during the first 1000 days of life

Mayneris‐Perxachs

Swann

2018

Eur J Nutr

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Nutritional restrictions during the first 1000 days of life can impair or delay the physical and cognitive development of the individual and have long-term consequences for their health. Metabolic phenotyping (metabolomics/metabonomics) simultaneously measures a diverse range of low molecular weight metabolites in a sample providing a comprehensive assessment of the individual's biochemical status. There are a growing number of studies applying such approaches to characterize the metabolic derangements induced by various forms of early-life malnutrition. This includes acute and chronic undernutrition and specific micronutrient deficiencies. Collectively, these studies highlight the diverse and dynamic metabolic disruptions resulting from various forms of nutritional deficiencies. Perturbations were observed in many pathways including those involved in energy, amino acid, and bile acid metabolism, the metabolic interactions between the gut microbiota and the host, and changes in metabolites associated with gut health. The information gleaned from such studies provides novel insights into the mechanisms linking malnutrition with developmental impairments and assists in the elucidation of candidate biomarkers to identify individuals at risk of developmental shortfalls. As the metabolic profile represents a snapshot of the biochemical status of an individual at a given time, there is great potential to use this information to tailor interventional strategies specifically to the metabolic needs of the individual.

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“…Therefore, variability in just the data processing limits integration of the MSI. One way to enable standardized data processing and biostatistics is to encourage the use of a universal workflow platform such as Galaxy ( https://galaxyproject.org ) [ 160 ]. In addition, the use of a standard reference material that can normalize and compare the detection levels from different instruments would be of value.…”

Section: Transparency Reproducibility and Open Sciencementioning

confidence: 99%

Beyond genomics: understanding exposotypes through metabolomics

Rattray

Deziel

Wallach³

et al. 2018

Hum Genomics

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BackgroundOver the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.Main textMetabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.ConclusionsComparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

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The degradation of nucleotide triphosphates extracted under boiling ethanol conditions is prevented by the yeast cellular matrix

Cited by 51 publications

References 26 publications

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Binary similarity measures for fingerprint analysis of qualitative metabolomic profiles

Metabolic phenotyping of malnutrition during the first 1000 days of life

Beyond genomics: understanding exposotypes through metabolomics

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