Biochemical and Structural Characterization of a novel thermophilic esterase EstD11 provide catalytic insights for the HSL family.

A novel esterase, EstD11, has been discovered in a hot spring metagenomic library. It is a thermophilic and thermostable esterase with an optimum temperature of 60°C. A detailed substrate preference analysis of EstD11 was done using a library of chromogenic ester substrate that revealed the broad substrate specificity of EstD11 with significant measurable activity against 16 substrates with varied chain length, steric hindrance, aromaticity and flexibility of the linker between the carboxyl and the alcohol moiety of the ester. The tridimensional structures of EstD11 and the inactive mutant have been determined at atomic resolutions. Structural and bioinformatic analysis, confirm that EstD11 belongs to the family IV, the hormone-sensitive lipase (HSL) family, from the α/ß-hydrolase superfamily. The canonical α/ß-hydrolase domain is completed by a cap domain, composed by two subdomains that can unmask of the active site to allow the substrate to enter. Eight crystallographic complexes were solved with different substrates and reaction products that allowed identification of the hot-spots in the active site underlying the specificity of the protein. Crystallization and/or incubation of EstD11 at high temperature provided unique information on cap dynamics and a first glimpse of enzymatic activity in vivo. Very interestingly, we have discovered a unique Met zipper lining the active site and the cap domains that could be essential in pivotal aspects as thermo-stability and substrate promiscuity in EstD11.

Anafilaxia a fluido seminal humano / Anaphylactic reaction to human seminal fluid

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La gestión de los intangibles en la percepción de los servicios sanitarios públicos / The management of intangibles on the perception of public health services

Conscientes de la importancia de la orientación de la gestión sanitaria hacia la satisfacción de sus usuarios, en este artículo pretendemos identificar cuáles son los aspectos que más influyen en la satisfacción de los españoles con la sanidad pública, y de qué manera lo hacen. De este modo, es posible orientar las políticas públicas sanitarias hacia la maximización de los objetivos de eficiencia y eficacia que, entendemos, no residen únicamente en mejorar el estado de salud de los ciudadanos, sino también en generarles confianza en el sistema, contribuyendo a legitimar las instituciones sanitarias, y con ellas el Estado del Bienestar que las sustenta. Para ello, construimos un modelo de explicación de la satisfacción ciudadana con el sistema sanitario público que testamos a través de modelos de ecuaciones estructurales

Aware of the importance of the orientation of health management towards the satisfaction of its users, this article aims to identify what are the aspects that most influence the satisfaction of the Spaniards with the public health system, and how they do so. With the identification of these elements, it is possible to guide the public policies in health towards the maximization of the objectives of efficiency and effectiveness which we understand, lie not only in improving the state of health of the citizens, but also on building confidence in the system, contributing to legitimize public health institutions and the Welfare State that sustains them. To do this, we build a model of explanation of the citizen satisfaction with the public healthcare system that we test through Structural Equation Models

Genome-scale modeling enables metabolic engineering of Saccharomyces cerevisiae for succinic acid production.

In this work, we describe the application of a genome-scale metabolic model and flux balance analysis for the prediction of succinic acid overproduction strategies in Saccharomyces cerevisiae. The top three single gene deletion strategies, Δmdh1, Δoac1, and Δdic1, were tested using knock-out strains cultivated anaerobically on glucose, coupled with physiological and DNA microarray characterization. While Δmdh1 and Δoac1 strains failed to produce succinate, Δdic1 produced 0.02 C-mol/C-mol glucose, in close agreement with model predictions (0.03 C-mol/C-mol glucose). Transcriptional profiling suggests that succinate formation is coupled to mitochondrial redox balancing, and more specifically, reductive TCA cycle activity. While far from industrial titers, this proof-of-concept suggests that in silico predictions coupled with experimental validation can be used to identify novel and non-intuitive metabolic engineering strategies.

Industrial systems biology of Saccharomyces cerevisiae enables novel succinic acid cell factory.

Saccharomyces cerevisiae is the most well characterized eukaryote, the preferred microbial cell factory for the largest industrial biotechnology product (bioethanol), and a robust commerically compatible scaffold to be exploitted for diverse chemical production. Succinic acid is a highly sought after added-value chemical for which there is no native pre-disposition for production and accmulation in S. cerevisiae. The genome-scale metabolic network reconstruction of S. cerevisiae enabled in silico gene deletion predictions using an evolutionary programming method to couple biomass and succinate production. Glycine and serine, both essential amino acids required for biomass formation, are formed from both glycolytic and TCA cycle intermediates. Succinate formation results from the isocitrate lyase catalyzed conversion of isocitrate, and from the α-keto-glutarate dehydrogenase catalyzed conversion of α-keto-glutarate. Succinate is subsequently depleted by the succinate dehydrogenase complex. The metabolic engineering strategy identified included deletion of the primary succinate consuming reaction, Sdh3p, and interruption of glycolysis derived serine by deletion of 3-phosphoglycerate dehydrogenase, Ser3p/Ser33p. Pursuing these targets, a multi-gene deletion strain was constructed, and directed evolution with selection used to identify a succinate producing mutant. Physiological characterization coupled with integrated data analysis of transcriptome data in the metabolically engineered strain were used to identify 2(nd)-round metabolic engineering targets. The resulting strain represents a 30-fold improvement in succinate titer, and a 43-fold improvement in succinate yield on biomass, with only a 2.8-fold decrease in the specific growth rate compared to the reference strain. Intuitive genetic targets for either over-expression or interruption of succinate producing or consuming pathways, respectively, do not lead to increased succinate. Rather, we demonstrate how systems biology tools coupled with directed evolution and selection allows non-intuitive, rapid and substantial re-direction of carbon fluxes in S. cerevisiae, and hence show proof of concept that this is a potentially attractive cell factory for over-producing different platform chemicals.

Evolutionary engineering of Saccharomyces cerevisiae for efficient aerobic xylose consumption.

Industrial biotechnology aims to develop robust microbial cell factories, such as Saccharomyces cerevisiae, to produce an array of added value chemicals presently dominated by petrochemical processes. Xylose is the second most abundant monosaccharide after glucose and the most prevalent pentose sugar found in lignocelluloses. Significant research efforts have focused on the metabolic engineering of S. cerevisiae for fast and efficient xylose utilization. This study aims to metabolically engineer S. cerevisiae, such that it can consume xylose as the exclusive substrate while maximizing carbon flux to biomass production. Such a platform may then be enhanced with complementary metabolic engineering strategies that couple biomass production with high value-added chemical. Saccharomyces cerevisiae, expressing xylose reductase, xylitol dehydrogenase and xylulose kinase, from the native xylose-metabolizing yeast Pichia stipitis, was constructed, followed by a directed evolution strategy to improve xylose utilization rates. The resulting S. cerevisiae strain was capable of rapid growth and fast xylose consumption producing only biomass and negligible amount of byproducts. Transcriptional profiling of this strain was employed to further elucidate the observed physiology confirms a strongly up-regulated glyoxylate pathway enabling respiratory metabolism. The resulting strain is a desirable platform for the industrial production of biomass-related products using xylose as a sole carbon source.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications.

BACKGROUND: The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. RESULTS: In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. CONCLUSIONS: With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.

Novel micro-bioreactor high throughput technology for cell culture process development: Reproducibility and scalability assessment of fed-batch CHO cultures.

With increasing timeline pressures to get therapeutic and vaccine candidates into the clinic, resource intensive approaches such as the use of shake flasks and bench-top bioreactors may limit the design space for experimentation to yield highly productive processes. The need to conduct large numbers of experiments has resulted in the use of miniaturized high-throughput (HT) technology for process development. One such high-throughput system is the SimCell platform, a robotically driven, cell culture bioreactor system developed by BioProcessors Corp. This study describes the use of the SimCell micro-bioreactor technology for fed-batch cultivation of a GS-CHO transfectant expressing a model IgG4 monoclonal antibody. Cultivations were conducted in gas-permeable chambers based on a micro-fluidic design, with six micro-bioreactors (MBs) per micro-bioreactor array (MBA). Online, non-invasive measurement of total cell density, pH and dissolved oxygen (DO) was performed. One hundred fourteen parallel MBs (19 MBAs) were employed to examine process reproducibility and scalability at shake flask, 3- and 100-L bioreactor scales. The results of the study demonstrate that the SimCell platform operated under fed-batch conditions could support viable cell concentrations up to least 12 x 10(6) cells/mL. In addition, both intra-MB (MB to MB) as well as intra-MBA (MBA to MBA) culture performance was found to be highly reproducible. The intra-MB and -MBA variability was calculated for each measurement as the coefficient of variation defined as CV (%) = (standard deviation/mean) x 100. The % CV values for most intra-MB and intra-MBA measurements were generally under 10% and the intra-MBA values were slightly lower than those for intra-MB. Cell growth, process parameters, metabolic and protein titer profiles were also compared to those from shake flask, bench-top, and pilot scale bioreactor cultivations and found to be within +/-20% of the historical averages.

Industrial systems biology.

The chemical industry is currently undergoing a dramatic change driven by demand for developing more sustainable processes for the production of fuels, chemicals, and materials. In biotechnological processes different microorganisms can be exploited, and the large diversity of metabolic reactions represents a rich repository for the design of chemical conversion processes that lead to efficient production of desirable products. However, often microorganisms that produce a desirable product, either naturally or because they have been engineered through insertion of heterologous pathways, have low yields and productivities, and in order to establish an economically viable process it is necessary to improve the performance of the microorganism. Here metabolic engineering is the enabling technology. Through metabolic engineering the metabolic landscape of the microorganism is engineered such that there is an efficient conversion of the raw material, typically glucose, to the product of interest. This process may involve both insertion of new enzymes activities, deletion of existing enzyme activities, but often also deregulation of existing regulatory structures operating in the cell. In order to rapidly identify the optimal metabolic engineering strategy the industry is to an increasing extent looking into the use of tools from systems biology. This involves both x-ome technologies such as transcriptome, proteome, metabolome, and fluxome analysis, and advanced mathematical modeling tools such as genome-scale metabolic modeling. Here we look into the history of these different techniques and review how they find application in industrial biotechnology, which will lead to what we here define as industrial systems biology.

Fueling industrial biotechnology growth with bioethanol.

Industrial biotechnology is the conversion of biomass via biocatalysis, microbial fermentation, or cell culture to produce chemicals, materials, and/or energy. Industrial biotechnology processes aim to be cost-competitive, environmentally favorable, and self-sustaining compared to their petrochemical equivalents. Common to all processes for the production of energy, commodity, added value, or fine chemicals is that raw materials comprise the most significant cost fraction, particularly as operating efficiencies increase through practice and improving technologies. Today, crude petroleum represents the dominant raw material for the energy and chemical sectors worldwide. Within the last 5 years petroleum prices, stability, and supply have increased, decreased, and been threatened, respectively, driving a renewed interest across academic, government, and corporate centers to utilize biomass as an alternative raw material. Specifically, bio-based ethanol as an alternative biofuel has emerged as the single largest biotechnology commodity, with close to 46 billion L produced worldwide in 2005. Bioethanol is a leading example of how systems biology tools have significantly enhanced metabolic engineering, inverse metabolic engineering, and protein and enzyme engineering strategies. This enhancement stems from method development for measurement, analysis, and data integration of functional genomics, including the transcriptome, proteome, metabolome, and fluxome. This review will show that future industrial biotechnology process development will benefit tremendously from the precedent set by bioethanol - that enabling technologies (e.g., systems biology tools) coupled with favorable economic and socio-political driving forces do yield profitable, sustainable, and environmentally responsible processes. Biofuel will continue to be the keystone of any industrial biotechnology-based economy whereby biorefineries leverage common raw materials and unit operations to integrate diverse processes to produce demand-driven product portfolios.

Determination of monoclonal antibody production in cell culture using novel microfluidic and traditional assays.

This study compares microfluidic technology (Protein 200 LabChip Assay kit, Agilent 2100 Bioanalyzer, referred to here as Protein 200) to the traditional approach for protein analysis, one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), for the sizing and quantification of immunoglobulin G (IgG) in hybridoma cell cultures. Internal references differ between each method: purified IgG was used alone in SDS-PAGE while myosin (the upper marker) was added to each sample in Protein 200. The IgG used here were produced in cultures propagated in either a serum-free or a serum-containing medium. With serum-containing samples, there was a significant difference in the IgG concentrations (p < 0.05) between SDS-PAGE and Protein 200. The concentration determined by SDS-PAGE was significantly higher (> 30%) than by Protein 200 or by high-pressure liquid chromatography (HPLC) because the large amounts of serum albumin in the samples affect the accuracy of SDS-PAGE. Protein 200 can determine size similarly to SDS-PAGE in serum-free samples (standard error of the mean, SEM, < 1%, 95% confidence < +/-1%), unlike in serum-containing samples. The Protein 200 assay was more effective than the traditional one-dimensional SDS-PAGE in determining concentration and size of IgG in cell culture samples and it provided a miniaturized and convenient platform for rapid analysis.