Noncoding sequence variants define a novel regulatory element in the first intron of the N-acetylglutamate synthase gene

N-acetylglutamate synthase deficiency is an autosomal recessive urea cycle disorder caused either by decreased expression of the NAGS gene or defective NAGS enzyme resulting in decreased production of N-acetylglutamate (NAG), an allosteric activator of carbamylphosphate synthetase 1 (CPS1). NAGSD is the only urea cycle disorder that can be effectively treated with a single drug, N-carbamylglutamate (NCG), a stable NAG analog, which activates CPS1 to restore ureagenesis. We describe three patients with NAGSD due to four novel noncoding sequence variants in the NAGS regulatory regions. All three patients had hyperammonemia that resolved upon treatment with NCG. Sequence variants NM_153006.2:c.427-222G>A and NM_153006.2:c.427-218A>C reside in the 547 bp-long first intron of NAGS and define a novel NAGS regulatory element that binds retinoic X receptor α. Sequence variants NC_000017.10:g.42078967A>T (NM_153006.2:c.-3065A>T) and NC_000017.10:g.42078934C>T (NM_153006.2:c.-3098C>T) reside in the NAGS enhancer, within known HNF1 and predicted glucocorticoid receptor binding sites, respectively. Reporter gene assays in HepG2 and HuH-7 cells demonstrated that all four substitutions could result in reduced expression of NAGS. These findings show that analyzing noncoding regions of NAGS and other urea cycle genes can reveal molecular causes of disease and identify novel regulators of ureagenesis.

AMP-activated protein kinase signaling regulated expression of urea cycle enzymes in response to changes in dietary protein intake

Abundance of urea cycle enzymes in the liver is regulated by dietary protein intake. Although urea cycle enzyme levels rise in response to a high-protein (HP) diet, signaling networks that sense dietary protein intake and trigger changes in expression of urea cycle genes have not been identified. The aim of this study was to identify signaling pathway(s) that respond to changes in protein intake and regulate expression of urea cycle genes in mice and human hepatocytes. Mice were adapted to either HP or low-protein diets followed by isolation of liver protein and mRNA and integrated analysis of the proteomic and transcriptomic data. HP diet led to increased expression of mRNA and enzymes in amino acid degradation pathways and decreased expression of mRNA and enzymes in carbohydrate and fat metabolism, which implicated adenosine monophosphate-activated protein kinase (AMPK) as a possible regulator. Primary human hepatocytes, treated with 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) an activator of AMPK, were used to test whether AMPK regulates expression of urea cycle genes. The abundance of carbamoylphosphate synthetase 1 and ornithine transcarbamylase mRNA increased in hepatocytes treated with AICAR, which supports a role for AMPK signaling in regulation of the urea cycle. Because AMPK is either a target of drugs used to treat type-2 diabetes, these drugs might increase the expression of urea cycle enzymes in patients with partial urea cycle disorders, which could be the basis of a new therapeutic approach.

Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information.