Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder caused by loss-of-function mutations in PKD1 or PKD2. Increased glycolysis is a prominent feature of the disease, but how it impacts on other metabolic pathways is unknown. Here, we present an analysis of mouse Pkd1 mutant cells and kidneys to investigate the metabolic reprogramming of this pathology. We show that loss of Pkd1 leads to profound metabolic changes that affect glycolysis, mitochondrial metabolism, and fatty acid synthesis (FAS). We find that Pkd1-mutant cells preferentially use glutamine to fuel the TCA cycle and to sustain FAS. Interfering with either glutamine uptake or FAS retards cell growth and survival. We also find that glutamine is diverted to asparagine via asparagine synthetase (ASNS). Transcriptional profiling of PKD1-mutant human kidneys confirmed these alterations. We find that silencing of Asns is lethal in Pkd1-mutant cells when combined with glucose deprivation, suggesting therapeutic approaches for ADPKD.
SummaryPrimary hyperoxaluria type I (PH1) is an autosomal-recessive inborn error of liver metabolism caused by alanine:glyoxylate aminotransferase (AGT) deficiency. In silico modeling of liver metabolism in PH1 recapitulated accumulation of known biomarkers as well as alteration of histidine and histamine levels, which we confirmed in vitro, in vivo, and in PH1 patients. AGT-deficient mice showed decreased vascular permeability, a readout of in vivo histamine activity. Histamine reduction is most likely caused by increased catabolism of the histamine precursor histidine, triggered by rerouting of alanine flux from AGT to the glutamic-pyruvate transaminase (GPT, also known as the alanine-transaminase ALT). Alanine administration reduces histamine levels in wild-type mice, while overexpression of GPT in PH1 mice increases plasma histidine, normalizes histamine levels, restores vascular permeability, and decreases urinary oxalate levels. Our work demonstrates that genome-scale metabolic models are clinically relevant and can link genotype to phenotype in metabolic disorders.
The Intravenous Glucose Tolerance Test is an experimental procedure used to study the glucose-insulin endocrine regulatory system. An open problem is to construct a model representing simultaneously the entire regulative mechanism. In the past three decades, several models have appeared, but they have not escaped criticisms and drawbacks. In this paper, the authors apply the Metabolic P systems theory for developing new physiologically based models of the glucose-insulin system, which can be applied to the IVGTT. Ten data-sets obtained from literature were considered and an MP model was found for each, which fits the data and explains the regulations of the dynamics. Finally, each model is analysed to define a common pattern which explains, in general, the action of the glucose-insulin control system.
Renal cell carcinomas (RCCs) are common cancers diagnosed in more than 350,000 people each year worldwide. Several pathways are de-regulated in RCCs, including mTORC1. However, how mTOR drives tumorigenesis in this context is unknown. The lack of faithful animal models has limited progress in understanding and targeting RCCs. Here, we generated a mouse model harboring the kidney-specific inactivation of Tsc1. These animals develop cysts that evolve into papillae, cystadenomas, and papillary carcinomas. Global profiling confirmed several metabolic derangements previously attributed to mTORC1. Notably, Tsc1 inactivation results in the accumulation of fumarate and in mTOR-dependent downregulation of the TCA cycle enzyme fumarate hydratase (FH). The re-expression of FH in cellular systems lacking Tsc1 partially rescued renal epithelial transformation. Importantly, the mTORC1-FH axis is likely conserved in human RCC specimens. We reveal a role of mTORC1 in renal tumorigenesis, which depends on the oncometabolite fumarate.
Inborn errors of metabolism (IEM) are genetic diseases caused by mutations in enzymes or transporters affecting specific metabolic reactions that cause a block in the physiological metabolic fluxes. Therapeutic treatment can be achieved either by decreasing the metabolic flux upstream of the block or by increasing the flux downstream of the block. The identification of upstream and downstream fluxes however is not trivial, since metabolic reactions are intertwined in a complex network. To overcome this problem, we propose an innovative computational workflow to model the alteration of metabolism caused by IEM and predict the metabolites and reactions that are affected by the mutation. Our workflow exploits a recent genome-scale metabolic network model of hepatocyte metabolism to identify metabolites accumulating in hepatocytes due to single gene mutations in IEM via an innovative ''differential flux analysis.'' We simulated 38 IEMs in the liver, and in about half of the cases, our workflow correctly identified the metabolites known to accumulate in the blood and urine of IEM patients.
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