Project description:Over the past decades, protein O-GlcNAcylation has been found to play a fundamental role in cell cycle control, metabolism, transcriptional regulation, and cellular signaling. Nevertheless, quantitative approaches to determine in vivo GlcNAc dynamics at a large-scale are still not readily available. Here, we have developed an approach to isotopically label O-GlcNAc modifications on proteins by producing 13C-labeled UDP-GlcNAc from 13C6-glucose via the hexosamine biosynthetic pathway. This metabolic labeling was combined with quantitative mass spectrometry-based proteomics to determine site-specific protein O-GlcNAcylation turnover rates. First, an efficient enrichment method for O-GlcNAc peptides was developed with the use of phenylboronic acid solid-phase extraction and anhydrous DMSO. The near stoichiometry reaction between the diol of GlcNAc and boronic acid dramatically improved the enrichment efficiency. Additionally, our kinetic model for turnover rates integrates both metabolomic and proteomic data, which increase the accuracy of the turnover rate estimation. Other advantages of this metabolic labeling method include in vivo application, direct labeling of the O-GlcNAc sites and higher confidence for site identification. Concentrating only on nuclear localized GlcNAc modified proteins, we are able to identify 159 O-GlcNAc sites on 74 proteins and determine turnover rates of 24 O-GlcNAc peptides from 21 proteins extracted from HeLa nuclei. In general, we found O-GlcNAcylation turnover rates are slower than those published for phosphorylation or acetylation. Nevertheless, the rates widely varied depending on both the protein and the residue modified. We believe this methodology can be broadly applied to reveal turnovers/dynamics of protein O-GlcNAcylation from different biological states and will provide more information on the significance of site-specific O-GlcNAcylation, enabling us to study the temporal dynamics of this critical modification in a site-specific manner for the first time.

Project description:Body mass index (BMI) is a complex disease risk factor known to be influenced by genes acting via both metabolic pathways and appetite regulation. In this study, we aimed to gain insight into the phenotypic consequences of BMI-associated genetic variants, which may be mediated by their expression in different tissues. First, we harnessed meta-analyzed gene expression datasets derived from subcutaneous adipose (n = 1257) and brain (n = 1194) tissue to identify 86 and 140 loci, respectively, which provided evidence of genetic colocalization with BMI. These two sets of tissue-partitioned loci had differential effects with respect to waist-to-hip ratio, suggesting that the way they influence fat distribution might vary despite their having very similar average magnitudes of effect on BMI itself (adipose = 0.0148 and brain = 0.0149 standard deviation change in BMI per effect allele). For instance, BMI-associated variants colocalized with TBX15 expression in adipose tissue (posterior probability [PPA] = 0.97), but not when we used TBX15 expression data derived from brain tissue (PPA = 0.04) This gene putatively influences BMI via its role in skeletal development. Conversely, there were loci where BMI-associated variants provided evidence of colocalization with gene expression in brain tissue (e.g., NEGR1, PPA = 0.93), but not when we used data derived from adipose tissue, suggesting that these genes might be more likely to influence BMI via energy balance. Leveraging these tissue-partitioned variant sets through a multivariable Mendelian randomization framework provided strong evidence that the brain-tissue-derived variants are predominantly responsible for driving the genetically predicted effects of BMI on cardiovascular-disease endpoints (e.g., coronary artery disease: odds ratio = 1.05, 95% confidence interval = 1.04-1.07, p = 4.67 × 10-14). In contrast, our analyses suggested that the adipose tissue variants might predominantly be responsible for the underlying relationship between BMI and measures of cardiac function, such as left ventricular stroke volume (beta = 0.21, 95% confidence interval = 0.09-0.32, p = 6.43 × 10-4).

Project description:Over the past decades, protein O-GlcNAcylation has been found to play a fundamental role in cell cycle control, metabolism, transcriptional regulation, and cellular signaling. Nevertheless, quantitative approaches to determine in vivo GlcNAc dynamics at a large-scale are still not readily available. Here, we have developed an approach to isotopically label O-GlcNAc modifications on proteins by producing 13C-labeled UDP-GlcNAc from 13C6-glucose via the hexosamine biosynthetic pathway. This metabolic labeling was combined with quantitative mass spectrometry-based proteomics to determine site-specific protein O-GlcNAcylation turnover rates. First, an efficient enrichment method for O-GlcNAc peptides was developed with the use of phenylboronic acid solid-phase extraction and anhydrous DMSO. The near stoichiometry reaction between the diol of GlcNAc and boronic acid dramatically improved the enrichment efficiency. Additionally, our kinetic model for turnover rates integrates both metabolomic and proteomic data, which increase the accuracy of the turnover rate estimation. Other advantages of this metabolic labeling method include in vivo application, direct labeling of the O-GlcNAc sites and higher confidence for site identification. Concentrating only on nuclear localized GlcNAc modified proteins, we are able to identify 159 O-GlcNAc sites on 74 proteins and determine turnover rates of 24 O-GlcNAc peptides from 21 proteins extracted from HeLa nuclei. In general, we found O-GlcNAcylation turnover rates are slower than those published for phosphorylation or acetylation. Nevertheless, the rates widely varied depending on both the protein and the residue modified. We believe this methodology can be broadly applied to reveal turnovers/dynamics of protein O-GlcNAcylation from different biological states and will provide more information on the significance of site-specific O-GlcNAcylation, enabling us to study the temporal dynamics of this critical modification in a site-specific manner for the first time.

Dataset Information

Genetic recoding to dissect the roles of site-specific protein O-GlcNAcylation.

Publications

Genetic recoding to dissect the roles of site-specific protein O-GlcNAcylation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets