A novel bifunctional N-acetylglutamate synthase-kinase from Xanthomonas campestris that is closely related to mammalian N-acetylglutamate synthase

BACKGROUND: In microorganisms and plants, the first two reactions of arginine biosynthesis are catalyzed by N-acetylglutamate synthase (NAGS) and N-acetylglutamate kinase (NAGK). In mammals, NAGS produces an essential activator of carbamylphosphate synthetase I, the first enzyme of the urea cycle, and no functional NAGK homolog has been found. Unlike the other urea cycle enzymes, whose bacterial counterparts could be readily identified by their sequence conservation with arginine biosynthetic enzymes, mammalian NAGS gene was very divergent, making it the last urea cycle gene to be discovered. Limited sequence similarity between E. coli NAGS and fungal NAGK suggests that bacterial and eukaryotic NAGS, and fungal NAGK arose from the fusion of genes encoding an ancestral NAGK (argB) and an acetyltransferase. However, mammalian NAGS no longer retains any NAGK catalytic activity. RESULTS: We identified a novel bifunctional N-acetylglutamate synthase and kinase (NAGS-K) in the Xanthomonadales order of gamma-proteobacteria that appears to resemble this postulated primordial fusion protein. Phylogenetic analysis indicated that xanthomonad NAGS-K is more closely related to mammalian NAGS than to other bacterial NAGS. We cloned the NAGS-K gene from Xanthomonas campestis, and characterized the recombinant NAGS-K protein. Mammalian NAGS and its bacterial homolog have similar affinities for substrates acetyl coenzyme A and glutamate as well as for their allosteric regulator arginine. CONCLUSION: The close phylogenetic relationship and similar biochemical properties of xanthomonad NAGS-K and mammalian NAGS suggest that we have identified a close relative to the bacterial antecedent of mammalian NAGS and that the enzyme from X. campestris could become a good model for mammalian NAGS in structural, biochemical and biophysical studies

Effect of arginine on oligomerization and stability of N-acetylglutamate synthase.

N-acetylglutamate synthase (NAGS; E.C.2.3.1.1) catalyzes the formation of N-acetylglutamate (NAG) from acetyl coenzyme A and glutamate. In microorganisms and plants, NAG is the first intermediate of the L-arginine biosynthesis; in animals, NAG is an allosteric activator of carbamylphosphate synthetase I and III. In some bacteria bifunctional N-acetylglutamate synthase-kinase (NAGS-K) catalyzes the first two steps of L-arginine biosynthesis. L-arginine inhibits NAGS in bacteria, fungi, and plants and activates NAGS in mammals. L-arginine increased thermal stability of the NAGS-K from Maricaulis maris (MmNAGS-K) while it destabilized the NAGS-K from Xanthomonas campestris (XcNAGS-K). Analytical gel chromatography and ultracentrifugation indicated tetrameric structure of the MmMNAGS-K in the presence and absence of L-arginine and a tetramer-octamer equilibrium that shifted towards tetramers upon binding of L-arginine for the XcNAGS-K. Analytical gel chromatography of mouse NAGS (mNAGS) indicated either different oligomerization states that are in moderate to slow exchange with each other or deviation from the spherical shape of the mNAGS protein. The partition coefficient of the mNAGS increased in the presence of L-arginine suggesting smaller hydrodynamic radius due to change in either conformation or oligomerization. Different effects of L-arginine on oligomerization of NAGS may have implications for efforts to determine the three-dimensional structure of mammalian NAGS

A dual AAV system enables the Cas9-mediated correction of a metabolic liver disease in newborn mice

Many genetic liver diseases present in newborns with repeated, often lethal, metabolic crises. Gene therapy using non-integrating viruses such as AAV is not optimal in this setting because the non-integrating genome is lost as developing hepatocytes proliferate1,2. We reasoned that newborn liver may be an ideal setting for AAV-mediated gene correction using CRISPR/Cas9. Here we intravenously infuse two AAVs, one expressing Cas9 and the other expressing a guide RNA and the donor DNA, into newborn mice with a partial deficiency in the urea cycle disorder enzyme, ornithine transcarbamylase (OTC). This resulted in reversion of the mutation in 10% (6.7% – 20.1%) of hepatocytes and increased survival in mice challenged with a high-protein diet, which exacerbates disease. Gene correction in adult OTC-deficient mice was lower and accompanied by larger deletions that ablated residual expression from the endogenous OTC gene, leading to diminished protein tolerance and lethal hyperammonemia on a chow diet

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU

BACKGROUND: The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change. OBJECTIVES: To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions. POPULATION: There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing. MODEL: A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression. RESULTS: Discrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed clinical expectations about the trajectories of death and survivors. CONCLUSIONS: The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients

Clinical Instability Is a Sign of Severity of Illness: A Cohort Study

OBJECTIVES: Test the hypothesis that within patient clinical instability measured by deterioration and improvement in mortality risk over 3-, 6-, 9-, and 12-hour time intervals is indicative of increasing severity of illness. DESIGN: Analysis of electronic health data from January 1, 2018, to February 29, 2020. SETTING: PICU and cardiac ICU at an academic children\u27s hospital. PATIENTS: All PICU patients. Data included descriptive information, outcome, and independent variables used in the Criticality Index-Mortality. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 8,399 admissions with 312 deaths (3.7%). Mortality risk determined every three hours using the Criticality Index-Mortality, a machine learning algorithm calibrated to this hospital. Since the sample sizes were sufficiently large to expect statical differences, we also used two measures of effect size, the proportion of time deaths had greater instability than survivors, and the rank-biserial correlation, to assess the magnitude of the effect and complement our hypothesis tests. Within patient changes were compared for survivors and deaths. All comparisons of survivors versus deaths were less than 0.001. For all time intervals, two measures of effect size indicated that the differences between deaths and survivors were not clinically important. However, the within-patient maximum risk increase (clinical deterioration) and maximum risk decrease (clinical improvement) were both substantially greater in deaths than survivors for all time intervals. For deaths, the maximum risk increase ranged from 11.1% to 16.1% and the maximum decrease ranged from -7.3% to -10.0%, while the median maximum increases and decreases for survivors were all less than ± 0.1%. Both measures of effect size indicated moderate to high clinical importance. The within-patient volatility was greater than 4.5-fold greater in deaths than survivors during the first ICU day, plateauing at ICU days 4-5 at 2.5 greater volatility. CONCLUSIONS: Episodic clinical instability measured with mortality risk is a reliable sign of increasing severity of illness. Mortality risk changes during four time intervals demonstrated deaths have greater maximum and within-patient clinical instability than survivors. This observation confirms the clinical teaching that clinical instability is a sign of severity of illness

N-acetylglutamate synthase: structure, function and defects

N-acetylglutamate (NAG) is a unique enzyme cofactor, essential for liver ureagenesis in mammals while it is the first committed substrate for de novo arginine biosynthesis in microorganisms and plants. The enzyme that produces NAG from glutamate and CoA, NAG synthase (NAGS), is allosterically inhibited by arginine in microorganisms and plants and activated in mammals. This transition of the allosteric effect occurred when tetrapods moved from sea to land. The first mammalian NAGS gene (from mouse) was cloned in 2002 and revealed significant differences from the NAGS ortholog in microorganisms. Almost all NAGS genes possess a C-terminus transferase domain in which the catalytic activity resides and an N-terminus kinase domain where arginine binds. The three-dimensional structure of NAGS shows two distinctly folded domains. The kinase domain binds arginine while the acetyltransferase domain contains the catalytic site. NAGS deficiency in humans leads to hyperammonemia and can be primary, due to mutations in the NAGS gene or secondary due to other mitochondrial aberrations that interfere with the normal function of the same enzyme. For either condition, N-carbamylglutamate (NCG), a stable functional analog of NAG, was found to either restore or improve the deficient urea cycle function