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Nudix hydrolase NUDT19 regulates mitochondrial function and ATP production in murine hepatocytes
(2022)
Changes in intracellular CoA levels are known to contribute to the development of non-alcoholic fatty liver disease (NAFLD) in type 2 diabetes (T2D) in human and rodents. However, the underlying genetic basis is still poorly understood.
Due to their diverse susceptibility towards metabolic diseases, mouse inbred strains have been proven to serve as powerful tools for the identification of novel genetic factors that underlie the patho-physiology of NAFLD and diabetes. Transcriptome analysis of mouse liver samples revealed the nucleoside diphosphate linked moiety X-type motif Nudt19 as novel candidate gene responsible for NAFLD and T2D development. Knockdown (KD) of Nudt19 increased mitochondrial and glycolytic ATP production rates in Hepa 1-6 cells by 41% and 10%, respectively.
The enforced utilization of glutamine or fatty acids as energy substrate reduced uncoupled respiration by 41% and 47%, respectively, in non-target (NT) siRNA transfected cells.
This reduction was prevented upon Nudt19 KD. Furthermore, incubation with palmitate or oleate respectively increased mitochondrial ATP production by 31% and 20%, and uncoupled respiration by 23% and 30% in Nudt19 KD cells, but not in NT cells.
The enhanced fatty acid oxidation in Nudt19 KD cells was accompanied by a 1.3-fold increased abundance of Pdk4.
This study is the first to describe Nudt19 as regulator of hepatic lipid metabolism and potential mediator of NAFLD and T2D development.
Chronic psychosocial stress adversely affects human morbidity and is a risk factor for inflammatory disorders, liver diseases, obesity, metabolic syndrome, and major depressive disorder (MDD). In recent studies, we found an association of MDD with an increase of acid sphingomyelinase (ASM) activity. Thus, we asked whether chronic psychosocial stress as a detrimental factor contributing to the emergence of MDD would also affect ASM activity and sphingolipid (SL) metabolism. To induce chronic psychosocial stress in male mice we employed the chronic subordinate colony housing (CSC) paradigm and compared them to non-stressed single housed control (SHC) mice. We determined Asm activity in liver and serum, hepatic SL concentrations as well as hepatic mRNA expression of genes involved in SL metabolism. We found that hepatic Asm activity was increased by 28% (P = 0.006) and secretory Asm activity by 47% (P = 0.002) in stressed mice. C16:0-Cer was increased by 40% (P = 0.008). Gene expression analysis further revealed an increased expression of tumor necrosis factor (TNF)-alpha (P = 0.009) and of several genes involved in SL metabolism (Cers5, P = 0.028; Cers6, P = 0.045; Gba, P = 0.049; Gba2, P = 0.030; Ormdl2, P = 0.034; Smpdl3B; P = 0.013). Our data thus provides first evidence that chronic psychosocial stress, at least in mice, induces alterations in SL metabolism, which in turn might be involved in mediating the adverse health effects of chronic psychosocial stress and peripheral changes occurring in mood disorders.
Acting during phase II metabolism, sulfotransferases (SULTs) serve detoxification by transforming a broad spectrum of compounds from pharmaceutical, nutritional, or environmental sources into more easily excretable metabolites. However, SULT activity has also been shown to promote formation of reactive metabolites that may have genotoxic effects. SULT subtype 1E1 (SULT1E1) was identified as a key player in estrogen homeostasis, which is involved in many physiological processes and the pathogenesis of breast and endometrial cancer. The development of an in silico prediction model for SULT1E1 ligands would therefore support the development of metabolically inert drugs and help to assess health risks related to hormonal imbalances. Here, we report on a novel approach to develop a model that enables prediction of substrates and inhibitors of SULT1E1. Molecular dynamics simulations were performed to investigate enzyme flexibility and sample protein conformations. Pharmacophores were developed that served as a cornerstone of the model, and machine learning techniques were applied for prediction refinement. The prediction model was used to screen the DrugBank (a database of experimental and approved drugs): 28% of the predicted hits were reported in literature as ligands of SULT1E1. From the remaining hits, a selection of nine molecules was subjected to biochemical assay validation and experimental results were in accordance with the in silico prediction of SULT1E1 inhibitors and substrates, thus affirming our prediction hypotheses.