Metabotyping of the <i>C. elegans sir-2.1</i> Mutant Using <i>in Vivo</i> Labeling and ¹³C‑Heteronuclear Multidimensional NMR Metabolomics

The roles of <i>sir-2.1</i> in <i>C. elegans</i> lifespan extension have been subjects of recent public and academic debates. We applied an efficient workflow for <i>in vivo</i> ¹³C-labeling of <i>C. elegans</i> and ¹³C-heteronuclear NMR metabolomics to characterizing the metabolic phenotypes of the <i>sir-2.1</i> mutant. Our method delivered sensitivity 2 orders of magnitude higher than that of the unlabeled approach, enabling 2D and 3D NMR experiments. Multivariate analysis of the NMR data showed distinct metabolic profiles of the mutant, represented by increases in glycolysis, nitrogen catabolism, and initial lipolysis. The metabolomic analysis defined the <i>sir-2.1</i> mutant metabotype as the decoupling between enhanced catabolic pathways and ATP generation. We also suggest the relationship between the metabotypes, especially the branched chain amino acids, and the roles of <i>sir-2.1</i> in the worm lifespan. Our results should contribute to solidifying the roles of <i>sir-2.1</i>, and the described workflow can be applied to studying many other proteins in metabolic perspectives

Metabotyping of the <i>C. elegans sir-2.1</i> Mutant Using <i>in Vivo</i> Labeling and ¹³C‑Heteronuclear Multidimensional NMR Metabolomics

The roles of <i>sir-2.1</i> in <i>C. elegans</i> lifespan extension have been subjects of recent public and academic debates. We applied an efficient workflow for <i>in vivo</i> ¹³C-labeling of <i>C. elegans</i> and ¹³C-heteronuclear NMR metabolomics to characterizing the metabolic phenotypes of the <i>sir-2.1</i> mutant. Our method delivered sensitivity 2 orders of magnitude higher than that of the unlabeled approach, enabling 2D and 3D NMR experiments. Multivariate analysis of the NMR data showed distinct metabolic profiles of the mutant, represented by increases in glycolysis, nitrogen catabolism, and initial lipolysis. The metabolomic analysis defined the <i>sir-2.1</i> mutant metabotype as the decoupling between enhanced catabolic pathways and ATP generation. We also suggest the relationship between the metabotypes, especially the branched chain amino acids, and the roles of <i>sir-2.1</i> in the worm lifespan. Our results should contribute to solidifying the roles of <i>sir-2.1</i>, and the described workflow can be applied to studying many other proteins in metabolic perspectives

Alanine-Metabolizing Enzyme Alt1 Is Critical in Determining Yeast Life Span, As Revealed by Combined Metabolomic and Genetic Studies

Alterations in metabolic pathways are gaining attention as important environmental factors affecting life span, but the determination of specific metabolic pathways and enzymes involved in life span remains largely unexplored. By applying an NMR-based metabolomics approach to a calorie-restricted yeast (<i>Saccharomyces cerevisiae</i>) model, we found that alanine level is inversely correlated with yeast chronological life span. The involvement of the alanine-metabolizing pathway in the life span was tested using a deletion mutant of <i>ALT1</i>, the gene for a key alanine-metabolizing enzyme. The mutant exhibited increased endogenous alanine level and much shorter life span, demonstrating the importance of <i>ALT1</i> and alanine metabolic pathways in the life span. <i>ALT1</i>’s effect on life span was independent of the TOR pathway, as revealed by a <i>tor1</i> deletion mutant. Further mechanistic studies showed that <i>alt1</i> deletion suppresses cytochrome <i>c</i> oxidase subunit 2 expression, ultimately generating reactive oxygen species. Overall, <i>ALT1</i> seems critical in determining yeast life span, and our approach should be useful for the mechanistic studies of life span determinations

Alanine-Metabolizing Enzyme Alt1 Is Critical in Determining Yeast Life Span, As Revealed by Combined Metabolomic and Genetic Studies

Alterations in metabolic pathways are gaining attention as important environmental factors affecting life span, but the determination of specific metabolic pathways and enzymes involved in life span remains largely unexplored. By applying an NMR-based metabolomics approach to a calorie-restricted yeast (<i>Saccharomyces cerevisiae</i>) model, we found that alanine level is inversely correlated with yeast chronological life span. The involvement of the alanine-metabolizing pathway in the life span was tested using a deletion mutant of <i>ALT1</i>, the gene for a key alanine-metabolizing enzyme. The mutant exhibited increased endogenous alanine level and much shorter life span, demonstrating the importance of <i>ALT1</i> and alanine metabolic pathways in the life span. <i>ALT1</i>’s effect on life span was independent of the TOR pathway, as revealed by a <i>tor1</i> deletion mutant. Further mechanistic studies showed that <i>alt1</i> deletion suppresses cytochrome <i>c</i> oxidase subunit 2 expression, ultimately generating reactive oxygen species. Overall, <i>ALT1</i> seems critical in determining yeast life span, and our approach should be useful for the mechanistic studies of life span determinations

Prediction of HbA1c using PLS multivariate regression.

<p>PLS regression models were built with the NMR profile at 7-day time point and the 3-month post-operative HbA1c values. The observed (X-axis) values are actually measured values and the predicted (Y-axis) values are from the PLS regression model obtained with two PLS components. The diagonal dashed line represents the theoretical perfect match, and the dotted line represents the least-square fitted line. Comparison between the observed and predicted values obtained from the training dataset (a), leave-one-out analysis (b), and three-fold cross validation (c). The predicted values in (a) do not represent true prediction since all the data were used in the model building. One (b) or seven (c) samples were left out at a time, and the predictions were made using the model built without the test data to be predicted until every sample was left out once.</p

Metabolite marker signals contributing to the differentiation and prediction.

<p>a. S-plot analysis showing the correlation and covariation. Metabolites on the upper right corner contribute to the improved group and on the lower left corner contribute to the non-improved group. b. PLS loading plot showing the contribution to the prediction of the HbA1c. Metabolites signals were identified using Chenomx and in-house built metabolite libraries.</p

Change of Anthropometric and Metabolic Parameters in improved and non-improved groups.

<p>*Patients who had glycated hemoglobin percentage less than 7.0% without glucose-lowering agent in 3 months after metabolic surgery.</p>1<p>OGTT indicates oral glucose tolerance test at 30, 60, 90, or 120 min after 75 g glucose intake.</p>2<p>paired t-test.</p>3<p>Not available.</p><p>Change of Anthropometric and Metabolic Parameters in improved and non-improved groups.</p