O-glcnAc reprograms cellular energetics

Dysfunctional mitochondria and generation of reactive oxygen species (ROS) promote chronic diseases, which have spurred interest in the molecular mechanisms underlying these conditions. Previously, we have demonstrated that disruption of post-translational modification of proteins with ß-linked N-acetylglucosamine (O- glcnAcylation) via overexpression of the O-glcnAc–regulating enzymes O- glcnAc transferase (OGT) or O- glcnAcase (OGA) impairs mitochondrial function. Here, we report that sustained alterations in O- glcnAcylation either by pharmacological or genetic manipulation also alters metabolic function. Sustained O-glcnAc elevation in SH-SY5Y neuroblastoma cells increased OGA expression and reduced cellular respiration and ROS generation. Cells with elevated O-glcnAc levels had elongated mitochondria and increased mitochondrial membrane potential, and RNA-Seq in SH-SY5Y cells indicated transcriptome reprogramming and down regulation of the NRF2-mediated antioxidant response. Sustained O-glcnAcylation in mice brain and liver validated the metabolic phenotypes observed in the cells, and OGT knockdown in the liver elevated ROS levels, impaired respiration, and increased the NRF2 antioxidant response. Moreover, elevated O-glcnAc levels promoted weight loss and lowered respiration in mice and skewed the mice toward carbohydrate-dependent metabolism as determined by indirect calorimetry. In summary, sustained elevation in O-glcnAcylation coupled with increased OGA expression reprograms energy metabolism, a finding that has potential implications for the etiology, development, and management of metabolic diseases. Overall design: SY5Y cells were adapted to long term O-glcnAcase (OGA) inhibition using the specific OGA inhibitor Thiamet-G (tmg) or glucosamine treatment for 3 weeks. After adaptation to the growth conditions, cells were harvest and RNA isolated for Next Generation RNA sequencing. Briefly, cDNA library was prepared using Illumina TruSeq Stranded mRNA sample preparation kit (Illumina) as manufacturer's instruction. Total RNA was isolated using the same method as previously described and 800 ng of the total RNA per reaction was used to initiate the protocol. The quality of RNA sequencing results was first assessed using FastQC (0.11.2). RSEM (1.2.22) was utilized to align the reads to the human reference genome HG38 and to calculate gene expression values. EdgeR (3.14.0) was then used to normalize the expression values using the TMM-method (weighted trimmed mean of M-values), and for differential expression analyses. First, the negative binomial conditional common likelihood was maximized to estimate a common dispersion value across all genes (estimateCommonDisp). Next, tagwise dispersion values were estimated by an empirical Bayes method based on weighted conditional maximum likelihood (estimateTagwiseDisp). Finally, the differentially gene expression was calculated by computing genewise exact tests for differences in the means between two groups of negative-binomially distributed counts. Hierarchical clustering analysis was determined using Euclidean distance. The following R-packages were utilized for calculations and visualizations: plots and edgeR.

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Tan EP, McGreal SR, Graw S, Tessman R, Koppel SJ, Dhakal P, Zhang Z, Machacek M, Zachara NE, Koestler DC, Peterson KR, Thyfault JP, Swerdlow RH, Krishnamurthy P, DiTacchio L, Apte U, Slawson C