Characterization of the Biosynthesis, Processing and Kinetic Mechanism of Action of the Enzyme Deficient in Mucopolysaccharidosis IIIC

Heparin acetyl-CoA:alpha-glucosaminide N-acetyltransferase (N-acetyltransferase, EC 2.3.1.78) is an integral lysosomal membrane protein containing 11 transmembrane domains, encoded by the HGSNAT gene. Deficiencies of N-acetyltransferase lead to mucopolysaccharidosis IIIC. We demonstrate that contrary to a previous report, the N-acetyltransferase signal peptide is co-translationally cleaved and that this event is required for its intracellular transport to the lysosome. While we confirm that the N-acetyltransferase precursor polypeptide is processed in the lysosome into a small amino-terminal alpha- and a larger ß- chain, we further characterize this event by identifying the mature amino-terminus of each chain. We also demonstrate this processing step(s) is not, as previously reported, needed to produce a functional transferase, i.e., the precursor is active. We next optimize the biochemical assay procedure so that it remains linear as N-acetyltransferase is purified or protein-extracts containing N-acetyltransferase are diluted, by the inclusion of negatively charged lipids. We then use this assay to demonstrate that the purified single N-acetyltransferase protein is both necessary and sufficient to express transferase activity, and that N-acetyltransferase functions as a monomer. Finally, the kinetic mechanism of action of purified N-acetyltransferase was evaluated and found to be a random sequential mechanism involving the formation of a ternary complex with its two substrates; i.e., N-acetyltransferase does not operate through a ping-pong mechanism as previously reported. We confirm this conclusion by demonstrating experimentally that no acetylated enzyme intermediate is formed during the reaction

GÊNEROS DISCURSIVOS E ENSINO: UMA PROPOSTA DE APLICAÇÃO EM SALA DE AULA

Os gêneros discursivos são formas de agir e interagir discursivamente e são inerentes à comunicação humana. Neste artigo, nos propomos, a partir de um percurso teórico, discutir sobre o conceito de gênero discursivo com base nas reflexões de Bakhtin (2000) e Marcuschi (2003, 2005), considerando sua aplicabilidade no ensino como condição para assegurar à construção de conhecimentos fundamentais para as práticas sociais de linguagem. Para isso, refletimos sobre o gênero discursivo como atividade sociocomunicativa de interação social, produzido para as necessidades de comunicação, constituído de componentes sociais, históricos, culturais e cognitivos. Além disso, analisamos a sequência didática na perspectiva de Dolz e Schneuwly (2004) como possibilidade de auxiliar o ensino através dos gêneros.  Entendemos ser essencial, por essa razão, que as aulas de língua portuguesa centrem-se, nos diferentes níveis de ensino, nas dinâmicas sociais de interação por meio dos gêneros discursivos.       &nbsp

Imbalanced pattern completion vs. separation in cognitive disease: network simulations of synaptic pathologies predict a personalized therapeutics strategy

<p>Abstract</p> <p>Background</p> <p>Diverse Mouse genetic models of neurodevelopmental, neuropsychiatric, and neurodegenerative causes of impaired cognition exhibit at least four convergent points of synaptic malfunction: 1) Strength of long-term potentiation (LTP), 2) Strength of long-term depression (LTD), 3) Relative inhibition levels (Inhibition), and 4) Excitatory connectivity levels (Connectivity).</p> <p>Results</p> <p>To test the hypothesis that pathological increases or decreases in these synaptic properties could underlie imbalances at the level of basic neural network function, we explored each type of malfunction in a simulation of autoassociative memory. These network simulations revealed that one impact of impairments or excesses in each of these synaptic properties is to shift the trade-off between pattern separation and pattern completion performance during memory storage and recall. Each type of synaptic pathology either pushed the network balance towards intolerable error in pattern separation or intolerable error in pattern completion. Imbalances caused by pathological impairments or excesses in LTP, LTD, inhibition, or connectivity, could all be exacerbated, or rescued, by the simultaneous modulation of any of the other three synaptic properties.</p> <p>Conclusions</p> <p>Because appropriate modulation of any of the synaptic properties could help re-balance network function, regardless of the origins of the imbalance, we propose a new strategy of personalized cognitive therapeutics guided by assay of pattern completion vs. pattern separation function. Simulated examples and testable predictions of this theorized approach to cognitive therapeutics are presented.</p

An Efficient Single Phase Method for the Extraction of Plasma Lipids

Lipidomic approaches are now widely used to investigate the relationship between lipid metabolism, health and disease. Large-scale lipidomics studies typically aim to quantify hundreds to thousands of lipid molecular species in a large number of samples. Consequently, high throughput methodology that can efficiently extract a wide range of lipids from biological samples is required. Current methods often rely on extraction in chloroform:methanol with or without two phase partitioning or other solvents, which are often incompatible with liquid chromatography electrospray ionization-tandem mass spectrometry (LC ESI-MS/MS). Here, we present a fast, simple extraction method that is suitable for high throughput LC ESI-MS/MS. Plasma (10 μL) was mixed with 100 μL 1-butanol:methanol (1:1 v/v) containing internal standards resulting in efficient extraction of all major lipid classes (including sterols, glycerolipids, glycerophospholipids and sphingolipids). Lipids were quantified using positive-ion mode LC ESI-MS/MS. The method showed high recovery (>90%) and reproducibility (%CV < 20%). It showed a strong correlation of all lipid measures with an established chloroform:methanol extraction method (R2 = 0.976). This method uses non-halogenated solvents, requires no drying or reconstitution steps and is suitable for large-scale LC ESI-MS/MS-based lipidomic analyses in research and clinical laboratories

Plasma Dihydroceramide Species Associate with Waist Circumference in Mexican American Families

OBJECTIVE: Waist circumference (WC), the clinical marker of central obesity, is gaining popularity as a screening tool for type 2 diabetes (T2D). While there is epidemiologic evidence favoring the WC-T2D association, its biological substantiation is generally weak. Our objective was to determine the independent association of plasma lipid repertoire with WC. METHODS: Samples and data from the San Antonio Family Heart Study of 1208 Mexican Americans from 42 extended families were used. Association of plasma lipidomic profiles with the cross-sectionally assessed WC was determined. Plasma lipidomic profiling entailed liquid chromatography with mass spectrometry. Statistical analyses included multivariable polygenic regression models and bivariate trait analyses using the SOLAR software. RESULTS: After adjusting for age and sex interactions, body mass index, homeostasis model of assessment-insulin resistance, total cholesterol, triglycerides, high density lipoproteins and use of lipid lowering drugs, dihydroceramides as a class were associated with WC. Dihydroceramide species 18:0, 20:0, 22:0, and 24:1 were significantly associated and genetically correlated with WC. Two sphingomyelin species (31:1 and 41:1) were also associated with WC. CONCLUSIONS: Plasma dihydroceramide levels independently associate with WC. Thus, high resolution plasma lipidomic studies can provide further credence to the biological underpinnings of the association of WC with T2D

Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts

BACKGROUND: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. METHODS: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia--the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. RESULTS: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76%. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. CONCLUSIONS: Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D

Plasma lipidomic profiles improve upon traditional risk factors for the prediction of cardiovascular events in type 2 diabetes

Background- Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk. Methods- Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionisation-tandem mass spectrometry on a case-cohort (n=3,779) subset from the ADVANCE (Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation) trial. The-case cohort was 61% male with a mean age of 67. All participants had type 2 diabetes mellitus with one or more additional cardiovascular risk factors and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (non-fatal myocardial infarction, non-fatal stroke and cardiovascular death) and cardiovascular death during a five year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized using the Akaike information criteria. C-statistics and net reclassification indices (NRI) were calculated within a five-fold cross validation framework. Results- Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C-statistic from 0.680 (95% CI, 0.678-0.682) to 0.700 (95% CI, 0.698-0.702, p&lt;0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219-0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species to the base model, showing an increase in the C-statistic from 0.740 (95% CI, 0.738-0.742) to 0.760 (95% CI, 0.757-0.762, p&lt;0.0001) and a continuous NRI of 0.328 (95%CI, 0.317-0.339). The results were validated in a subcohort with type 2 diabetes (n=511) from the LIPID (The Long-Term Intervention with Pravastatin in Ischaemic Disease) trial. Conclusion- The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes.</p

Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus

Background: Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes mellitus or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk. Methods: Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionization–tandem mass spectrometry on a case-cohort (n=3779) subset from the ADVANCE trial (Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation). The case-cohort was 61% male with a mean age of 67 years. All participants had type 2 diabetes mellitus with ≥1 additional cardiovascular risk factors, and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized with the Akaike information criteria. C statistics and NRIs were calculated within a 5-fold cross-validation framework. Results: Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters, and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C statistic from 0.680 (95% confidence interval [CI], 0.678–0.682) to 0.700 (95% CI, 0.698–0.702; P<0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219–0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species into the base model, showing an increase in the C statistic from 0.740 (95% CI, 0.738–0.742) to 0.760 (95% CI, 0.757–0.762; P<0.0001) and a continuous net reclassification index of 0.328 (95% CI, 0.317–0.339). The results were validated in a subcohort with type 2 diabetes mellitus (n=511) from the LIPID trial (Long-Term Intervention With Pravastatin in Ischemic Disease). Conclusions: The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes mellitus