Metabolomics : Official journal of the Metabolomic Society最新文献

{"title":"Correction to: Use of information dependent acquisition mass spectra and sequential window acquisition of all theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication.","authors":"Lei Xu,&nbsp;Zhenzhen Xu,&nbsp;Ilya Strashnov,&nbsp;Xiaojun Liao","doi":"10.1007/s11306-020-01706-x","DOIUrl":"https://doi.org/10.1007/s11306-020-01706-x","url":null,"abstract":"<p><p>Following publication of the original article, the authors would like to correct the authors and author affiliations.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"84"},"PeriodicalIF":3.6,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-020-01706-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38212185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NMR metabolomic analysis of bacterial resistance pathways using multivalent quaternary ammonium functionalized macromolecules.","authors":"Michelle L Aries,&nbsp;Mary J Cloninger","doi":"10.1007/s11306-020-01702-1","DOIUrl":"https://doi.org/10.1007/s11306-020-01702-1","url":null,"abstract":"<p><strong>Introduction: </strong>Multivalent antimicrobial dendrimers are an exciting new system that is being developed to address the growing problem of drug resistant bacteria. Nuclear Magnetic Resonance (NMR) metabolomics is a quantitative and reproducible method for the determination of bacterial response to environmental stressors and for visualization of perturbations to biochemical pathways.</p><p><strong>Objectives: </strong>NMR metabolomics is used to elucidate metabolite differences between wild type and antimicrobially mutated Escherichia coli (E. coli) samples.</p><p><strong>Methods: </strong>Proton (¹H) NMR hydrophilic metabolite analysis was conducted on samples of E. coli after 33 growth cycles of a minimum inhibitory challenge to E. coli by poly(amidoamine) dendrimers functionalized with mannose and with C₁₆-DABCO quaternary ammonium endgroups and compared to the metabolic profile of wild type E. coli.</p><p><strong>Results: </strong>The wild type and mutated E. coli samples were separated into distinct sample sets by hierarchical clustering, principal component analysis (PCA) and sparse partial least squares discriminate analysis (sPLS-DA). Metabolite components of membrane fortification and energy related pathways had a significant p value and fold change between the wild type and mutated E. coli. Amino acids commonly associated with membrane fortification from cationic antimicrobials, such as lysine, were found to have a higher concentration in the mutated E. coli than in the wild type E. coli. N-acetylglucosamine, a major component of peptidoglycan synthesis, was found to have a 25-fold higher concentration in the mid log phase of the mutated E. coli than in the mid log phase of the wild type.</p><p><strong>Conclusion: </strong>The metabolic profile suggests that E. coli change their peptidoglycan composition in order to garner protection from the highly positively charged and multivalent C₁₆-DABCO and mannose functionalized dendrimer.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"82"},"PeriodicalIF":3.6,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-020-01702-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38194989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy.","authors":"Shih-Chun Cheng,&nbsp;Kueian Chen,&nbsp;Chih-Yung Chiu,&nbsp;Kuan-Ying Lu,&nbsp;Hsin-Ying Lu,&nbsp;Meng-Han Chiang,&nbsp;Cheng-Kun Tsai,&nbsp;Chi-Jen Lo,&nbsp;Mei-Ling Cheng,&nbsp;Ting-Chang Chang,&nbsp;Gigin Lin","doi":"10.1007/s11306-019-1609-z","DOIUrl":"https://doi.org/10.1007/s11306-019-1609-z","url":null,"abstract":"<p><strong>Introduction: </strong>Endometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers.</p><p><strong>Objectives: </strong>We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy.</p><p><strong>Methods: </strong>We screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms-support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection.</p><p><strong>Results: </strong>A total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively.</p><p><strong>Conclusion: </strong>Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"146"},"PeriodicalIF":3.6,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-019-1609-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40552836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NMR metabolomics identifies over 60 biomarkers associated with Type II Diabetes impairment in db/db mice.","authors":"Marina Mora-Ortiz,&nbsp;Patricia Nuñez Ramos,&nbsp;Alain Oregioni,&nbsp;Sandrine P Claus","doi":"10.1007/s11306-019-1548-8","DOIUrl":"https://doi.org/10.1007/s11306-019-1548-8","url":null,"abstract":"<p><strong>Introduction: </strong>The rapid expansion of Type 2 Diabetes (T2D), that currently affects 90% of people suffering from diabetes, urges us to develop a better understanding of the metabolic processes involved in the disease process in order to develop better therapies. The most commonly used model for T2D research is the db/db (BKS.Cg-Dock7 < m > +/+ Lepr < db >/J) mouse model. Yet, a systematic ¹H NMR based metabolomics characterisation of most tissues in this animal model has not been published. Here, we provide a systematic organ-specific metabolomics analysis of this widely employed model using NMR spectroscopy.</p><p><strong>Objectives: </strong>The aim of this study was to characterise the metabolic modulations associated with T2D in db/db mice in 18 relevant biological matrices.</p><p><strong>Methods: </strong>High-resolution ¹H-NMR and 2D-NMR spectroscopy were applied to 18 biological matrices of 12 db/db mice (WT control n = 6, db/db = 6) aged 22 weeks, when diabetes is fully established.</p><p><strong>Results: </strong>61 metabolites associated with T2D were identified. Kidney, spleen, eye and plasma were the biological matrices carrying the largest metabolomics modulations observed in established T2D, based on the total number of metabolites that showed a statistical difference between the diabetic and control group in each tissue (16 in each case) and the strength of the O-PLS DA model for each tissue. Glucose and glutamate were the most commonly associated metabolites found significantly increased in nine biological matrices. Investigated sections where no increase of glucose was associated with T2D include all intestinal segments (i.e. duodenum, jejunum, ileum and colon). Microbial co-metabolites such as acetate and butyrate, used as carbon sources by the host, were identified in excess in the colonic tissues of diabetic individuals.</p><p><strong>Conclusions: </strong>The metabolic biomarkers identified using ¹H NMR-based metabolomics will represent a useful resource to explore metabolic pathways involved in T2D in the db/db mouse model.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"89"},"PeriodicalIF":3.6,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-019-1548-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40540431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breath metabolome of mice infected with Pseudomonas aeruginosa.","authors":"Giorgia Purcaro,&nbsp;Mavra Nasir,&nbsp;Flavio A Franchina,&nbsp;Christiaan A Rees,&nbsp;Minara Aliyeva,&nbsp;Nirav Daphtary,&nbsp;Matthew J Wargo,&nbsp;Lennart K A Lundblad,&nbsp;Jane E Hill","doi":"10.1007/s11306-018-1461-6","DOIUrl":"https://doi.org/10.1007/s11306-018-1461-6","url":null,"abstract":"<p><strong>Introduction: </strong>The measurement of specific volatile organic compounds in breath has been proposed as a potential diagnostic for a variety of diseases. The most well-studied bacterial lung infection in the breath field is that caused by Pseudomonas aeruginosa.</p><p><strong>Objectives: </strong>To determine a discriminatory core of molecules in the \"breath-print\" of mice during a lung infection with four strains of P. aeruginosa (PAO1, PA14, PAK, PA7). Furthermore, we attempted to extrapolate a strain-specific \"breath-print\" signature to investigate the possibility of recapitulating the genetic phylogenetic groups (Stewart et al. Pathog Dis 71(1), 20-25, 2014. https://doi.org/10.1111/2049-632X.12107 ).</p><p><strong>Methods: </strong>Breath was collected into a Tedlar bag and shortly after drawn into a thermal desorption tube. The latter was then analyzed into a comprehensive multidimensional gas chromatography coupled with a time-of-flight mass spectrometer. Random forest algorithm was used for selecting the most discriminatory features and creating a prediction model.</p><p><strong>Results: </strong>Three hundred and one molecules were significantly different between animals infected with P. aeruginosa, and those given a sham infection (PBS) or inoculated with UV-killed P. aeruginosa. Of those, nine metabolites could be used to discriminate between the three groups with an accuracy of 81%. Hierarchical clustering showed that the signature from breath was due to a specific response to live bacteria instead of a generic infection response. Furthermore, we identified ten additional volatile metabolites that could differentiate mice infected with different strains of P. aeruginosa. A phylogram generated from the ten metabolites showed that PAO1 and PA7 were the most distinct group, while PAK and PA14 were interspersed between the former two groups.</p><p><strong>Conclusions: </strong>To the best of our knowledge, this is the first study to report on a 'core' murine breath print, as well as, strain level differences between the compounds in breath. We provide identifications (by running commercially available analytical standards) to five breath compounds that are predictive of P. aeruginosa infection.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"10"},"PeriodicalIF":3.6,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1461-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37019838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomics in epidemiologic research: challenges and opportunities for early-career epidemiologists.","authors":"Eline H van Roekel,&nbsp;Erikka Loftfield,&nbsp;Rachel S Kelly,&nbsp;Oana A Zeleznik,&nbsp;Krista A Zanetti","doi":"10.1007/s11306-018-1468-z","DOIUrl":"https://doi.org/10.1007/s11306-018-1468-z","url":null,"abstract":"<p><strong>Background: </strong>The application of metabolomics to epidemiologic studies is increasing.</p><p><strong>Aim of review: </strong>Here, we describe the challenges and opportunities facing early-career epidemiologists aiming to apply metabolomics to their research.</p><p><strong>Key scientific concepts of review: </strong>Many challenges inherent to metabolomics may provide early-career epidemiologists with the opportunity to play a pivotal role in answering critical methodological questions and moving the field forward. Although generating large-scale high-quality metabolomics data can be challenging, data can be accessed through public databases, collaboration with senior researchers or participation within interest groups. Such efforts may also assist with obtaining funding, provide knowledge on training resources, and help early-career epidemiologists to publish in the field of metabolomics.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"9"},"PeriodicalIF":3.6,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1468-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37195779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolomic approaches to polyamines including acetylated derivatives in lung tissue of mice with asthma.","authors":"Hyeon-Seong Lee,&nbsp;Chan Seo,&nbsp;Yun-Ho Hwang,&nbsp;Tae Hwan Shin,&nbsp;Hyung-Jin Park,&nbsp;Youngbae Kim,&nbsp;Moongi Ji,&nbsp;Jeuk Min,&nbsp;Subin Choi,&nbsp;Hangun Kim,&nbsp;Ae Kyung Park,&nbsp;Sung-Tae Yee,&nbsp;Gwang Lee,&nbsp;Man-Jeong Paik","doi":"10.1007/s11306-018-1470-5","DOIUrl":"https://doi.org/10.1007/s11306-018-1470-5","url":null,"abstract":"<p><strong>Introduction: </strong>Recently, the relationship between polyamine (PA) metabolism and asthma has been studied in severe asthmatic therapy, but systematic PA metabolism including their acetylated derivatives was not fully understood.</p><p><strong>Objectives: </strong>Profiling analysis of polyamines (PAs) was performed to understand the biochemical events and monitor altered PA metabolism in lung tissue of mice with asthma.</p><p><strong>Methods: </strong>Polyamine profiling of lung tissue of mice with asthma was performed without derivatization by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with star pattern recognition analysis. The PA levels between control and asthma groups were evaluated by multivariate analysis.</p><p><strong>Results: </strong>In mouse lung tissue, seven PAs were determined by LC-MS/MS in multiple reaction monitoring (MRM) mode. Their levels were normalized to the corresponding mean levels of the control group for star pattern analysis, which showed distorted heptagonal shapes with characteristic and readily distinguishable patterns for each group. Levels of putrescine (p < 0.0034), N¹-acetylputrescine (p < 0.0652), and N⁸-acetylspermidine (p < 0.0827) were significantly increased in asthmatic lung tissue. The separation of the two groups was evaluated using multivariate analysis. In unsupervised learning, acetylated PAs including N¹-acetylspermine were the main metabolites for discrimination. In supervised learning, putrescine and N¹-acetylputrescine were evaluated as important metabolites.</p><p><strong>Conclusions: </strong>The present results provide basic data for understanding polyamine metabolism in asthma and may help to improve the therapy for severe asthma patients.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"8"},"PeriodicalIF":3.6,"publicationDate":"2019-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1470-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37195772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Avoiding selection bias in metabolomics studies: a tutorial.","authors":"S C Boone,&nbsp;S le Cessie,&nbsp;K Willems van Dijk,&nbsp;R de Mutsert,&nbsp;D O Mook-Kanamori","doi":"10.1007/s11306-018-1463-4","DOIUrl":"https://doi.org/10.1007/s11306-018-1463-4","url":null,"abstract":"<p><strong>Background: </strong>Metabolomics techniques are increasingly applied in epidemiologic research. Many available assays are still relatively expensive and therefore measurements are often performed in small patient population studies such as case series or case-control designs with strong participant selection criteria. Subsequently, metabolomics data are frequently used to assess secondary associations for which the original study was not explicitly designed. Especially in these secondary analyses, there is a risk that the original selection criteria and the conditioning that takes place due to this selection are not properly accounted for which can lead to selection bias.</p><p><strong>Aim of review: </strong>In this tutorial, we start with a brief theoretical introduction on the issue of selection bias. Subsequently, we demonstrate how selection bias can occur in metabolomics studies by means of an investigation into associations of metabolites with total body fat in a nested case-control study that was originally designed to study effects of elevated fasting glucose.</p><p><strong>Key scientific concepts of review: </strong>We demonstrate that standard analytical methods, such as stratification or adjustment in regression analyses, are not suited to deal with selection bias and may even induce the bias when analysing metabolite-phenotype relationships in selected groups. Finally, we show that inverse probability weighting, also known as survey weighting, can be used in some situations to make unbiased estimates of the outcomes.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"7"},"PeriodicalIF":3.6,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1463-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37020368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies.","authors":"William C Joesten,&nbsp;Michael A Kennedy","doi":"10.1007/s11306-018-1465-2","DOIUrl":"https://doi.org/10.1007/s11306-018-1465-2","url":null,"abstract":"<p><strong>Introduction: </strong>The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The \"putatively annotated compound\" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of \"putatively annotated compound\" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.</p><p><strong>Objectives: </strong>To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to \"putatively annotated compound\" assignments in NMR-based metabolic profiling studies.</p><p><strong>Methods: </strong>The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.</p><p><strong>Results: </strong>Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to \"putatively annotated compounds\" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.</p><p><strong>Conclusion: </strong>RANCM is intended to reduce the ambiguity in \"putatively annotated compound\" assignments, to facilitate effective communication of the degree of confidence in \"putatively annotated compound\" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"5"},"PeriodicalIF":3.6,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1465-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37020365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Juice Index: an integrated Sauvignon blanc grape and wine metabolomics database shows mainly seasonal differences.","authors":"Farhana R Pinu,&nbsp;Sergey Tumanov,&nbsp;Claire Grose,&nbsp;Victoria Raw,&nbsp;Abby Albright,&nbsp;Lily Stuart,&nbsp;Silas G Villas-Boas,&nbsp;Damian Martin,&nbsp;Roger Harker,&nbsp;Marc Greven","doi":"10.1007/s11306-018-1469-y","DOIUrl":"https://doi.org/10.1007/s11306-018-1469-y","url":null,"abstract":"<p><strong>Introduction: </strong>Although Sauvignon Blanc (SB) grapes are cultivated widely throughout New Zealand, wines from the Marlborough region are most famous for their typical varietal combination of tropical and vegetal aromas. These wines differ in composition from season to season as well as among locations within the region, which makes the continual production of good quality wines challenging. Here, we developed a unique database of New Zealand SB grape juices and wines to develop tools to help winemakers to make blending decisions and assist in the development of new wine styles.</p><p><strong>Methods: </strong>About 400 juices were collected from different regions in New Zealand over three harvest seasons (2011-2013), which were then fermented under controlled conditions using a commercial yeast strain Saccharomyces cerevisiae EC1118. Comprehensive metabolite profiling of these juices and wines by gas chromatography-mass spectrometry (GC-MS) was combined with their detailed oenological parameters and associated meteorological data.</p><p><strong>Results: </strong>These combined metabolomics data clearly demonstrate that seasonal variation is more prominent than regional difference in both SB grape juices and wines, despite almost universal use of vineyard irrigation to mitigate seasonal rainfall and evapotranspiration differences, Additionally, we identified a group of juice metabolites that play central roles behind these variations, which may represent chemical signatures for juice and wine quality assessment.</p><p><strong>Conclusion: </strong>This database is the first of its kind in the world to be available for the wider scientific community and offers potential as a predictive tool for wine quality and innovation when combined with mathematical modelling.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"3"},"PeriodicalIF":3.6,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-018-1469-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37182160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}