Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

Finding correspondence between metabolomic features in untargeted liquid chromatography-mass spectrometry metabolomics datasets.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S

The Effect of Viticultural Climate on Red and White Wine Typicity - Characterization in Ibero-American grape-growing regions

Aim: This study is part of a CYTED (Ibero-American Program for Science, Technology and Development) project on vitivinicultural zoning. The objective was to characterize the effect of viticultural climate on red and white wine typicity in the macro Ibero-American viticultural region. Methods and results: The climate of 46 grape-growing regions in 6 Ibero-American countries (Argentina, Bolivia, Brazil, Chile, Spain and Portugal) was characterized using the three viticultural climate index of the Geoviticulture MCC System: the Heliothermal index HI, the Cool Night index CI and the Dryness index DI. The main sensory characteristics frequently observed in representative red and white wines of each of these regions were described by enology experts in the respective countries: intensity of colour, aroma, aroma-ripe fruit, body-palate concentration, alcohol, tannins (for red wines) and acidity as well as persistence on the palate. The data were submitted to a correlation analysis of the variables and Principal Component Analysis (PCA). Conclusion: The typicity of red and white wines was correlated with the HI, CI and DI viticultural climate indexes from the MCC System. The main wine sensory variables affected by viticultural climate were identified. Significance and impact of the study : The results can be used to project the potential impacts of climate change on wine sensory characteristics

L'effet du climat viticole sur la typicité des vins blancs: caractérisation au niveau des régions viticoles ibéro-américaines.

There are many studies in the world that characterize the effect of the climate on grape composition and wine characteristics and typicity concerning different viticultural regions. However, the same is not true concerning studies in a worldwide scale to characterize this effect considering different climate types. This study is part of a CYTED (Ibero-American Program for Science, Technology and Development) project in vitivinicultural zoning. The objective was to characterize the effect of the viticultural climate on white wine typicity on the macro Ibero-American viticultural region. The methodology used 46 grape-growing regions in 6 Ibero-American countries: Argentina, Bolivia, Brazil, Chile, Portugal and Spain. The viticultural climate of each region was characterized by the 3 viticultural climate index of the Geoviticulture MCC System (1): HI (Heliothermal index), CI (Cool night index) and DI (Dryness index). The main sensory characteristics observed frequently in representative white wines produced with grapes of each of these 46 grape-growing regions were described by enologists in the respective countries, using the methodology of Zanus & Tonietto (2). The sensory description concerned the intensity of perception of Color (Cou), Aroma - Intensity (Ar), Aroma - Ripe Fruit (Ar-Fm), Body ? Palate Concentration (Con), Alcohol (Al) and Acidity (Ac). The Persistence in Mouth (Per) was also evaluated. The data were submitted to a correlation analysis of the variables and to a Principal Component Analysis (PCA). The results showed that the typicity of the white wines was correlated with the viticultural climate indexes HI, CI and DI from MCC System. The main wine sensory variables affected by viticultural climate are identified

Determinants of accelerated metabolomic and epigenetic aging in a UK cohort

Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.Horizon 2020 Framework Programme. Grant Number: 633666, 633595, 733206; Home Office. Grant Number: 780‐TETRA; National Institute for Health Research (NIHR) Biomedical Research Centre; UK MEDical BIOinformatics Partnership. Grant Number: MR/L01632X/1

Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes

Plasma levels of liver enzymes provide insights into hepatic function and related diseases. Here, the authors perform a genome-wide association study on three liver enzymes, identifying genetic variants associated with their plasma concentration as well as links to metabolic and cardiovascular diseases. Serum concentration of hepatic enzymes are linked to liver dysfunction, metabolic and cardiovascular diseases. We perform genetic analysis on serum levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) using data on 437,438 UK Biobank participants. Replication in 315,572 individuals from European descent from the Million Veteran Program, Rotterdam Study and Lifeline study confirms 517 liver enzyme SNPs. Genetic risk score analysis using the identified SNPs is strongly associated with serum activity of liver enzymes in two independent European descent studies (The Airwave Health Monitoring study and the Northern Finland Birth Cohort 1966). Gene-set enrichment analysis using the identified SNPs highlights involvement in liver development and function, lipid metabolism, insulin resistance, and vascular formation. Mendelian randomization analysis shows association of liver enzyme variants with coronary heart disease and ischemic stroke. Genetic risk score for elevated serum activity of liver enzymes is associated with higher fat percentage of body, trunk, and liver and body mass index. Our study highlights the role of molecular pathways regulated by the liver in metabolic disorders and cardiovascular disease

L'effet du climat viticole sur la typicité des vins blancs et rouges: caractérisation au niveau des régionsviticoles Ibéro-Américaines.

There are many studies in the world that characterize the effect os the climate on grape composition and wine characteristcs and typicity concerning different viticultural regions

L'effet du climat viticole sur la typicité des vins blancs: caractérisation au niveau des régions viticoles ibéro-américaines.

There are many studies in the world that characterize the effect of the climate on grape composition and wine characteristics and typicity concerning different viticultural regions. However, the same is not true concerning studies in a worldwide scale to characterize this effect considering different climate types. This study is part of a CYTED (Ibero-American Program for Science, Technology and Development) project in vitivinicultural zoning. The objective was to characterize the effect of the viticultural climate on white wine typicity on the macro Ibero-American viticultural region. The methodology used 46 grape-growing regions in 6 Ibero-American countries: Argentina, Bolivia, Brazil, Chile, Portugal and Spain. The viticultural climate of each region was characterized by the 3 viticultural climate index of the Geoviticulture MCC System (1): HI (Heliothermal index), CI (Cool night index) and DI (Dryness index). The main sensory characteristics observed frequently in representative white wines produced with grapes of each of these 46 grape-growing regions were described by enologists in the respective countries, using the methodology of Zanus & Tonietto (2). The sensory description concerned the intensity of perception of Color (Cou), Aroma - Intensity (Ar), Aroma - Ripe Fruit (Ar-Fm), Body ? Palate Concentration (Con), Alcohol (Al) and Acidity (Ac). The Persistence in Mouth (Per) was also evaluated. The data were submitted to a correlation analysis of the variables and to a Principal Component Analysis (PCA). The results showed that the typicity of the white wines was correlated with the viticultural climate indexes HI, CI and DI from MCC System. The main wine sensory variables affected by viticultural climate are identified