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121 results for “Glycerol”
Regulation of Dye-decolorizing Peroxidases Gene Expression in Pleurotus ostreatus Grown on Glycerol as the Carbon Source
<p>This dataset contains the raw data and code necessary to reproduce the results of: Regulation of dye peroxidas gene expression in Pleurotus ostreatus grown on glycerol as the carbon source.</p> <p> </p> <p>These data are also available at github: <a href="https://github.com/JLuisCuamatzi/Pleurotus_ostreatus_CarbonSources">JLuisCuamatzi/Pleurotus_ostreatus_CarbonSources: Data and scripts to reproduce the analysis performed at Regulation of dye peroxidas gene expression in Pleurotus ostreatus grown on glycerol as the carbon source (github.com)</a></p>
Removal of glycerol from enzymatically produced 2-α-D-glucosylglycerol by discontinuous diafiltration
<p>We provide here the underlying data of the scientific publication "Removal of glycerol from enzymatically produced 2-α-D-glucosylglycerol by discontinuous diafiltration". Please find the abstract below:</p> <p>2-α-D-Glucosyl-glycerol (2-GG) is a natural glycoside that acts as a biological extremolyte. 2-<br> GG is a promising ingredient for cosmetic products. Industrially, 2-GG is synthesised from<br> sucrose in the presence of a high excess of glycerol using the enzyme sucrose<br> phosphorylase. For commercial applications, the surplus glycerol must be removed and an<br> efficient purification strategy is therefore demanded. Here, we demonstrate nanofiltration for<br> the separation of glycerol from 2-GG. Based on membrane screening performed at varied<br> pressure and temperature, we identified a 150 – 300 Da cut-off polyamide membrane for the<br> selective removal of glycerol at high permeate throughput. An enzymatically produced 2-GG<br> solution was purified with multicycle discontinuous diafiltration on the litre scale. The process<br> was operated at constant permeate flux (13 kg m² h-1) and at a temperature of 30 °C. A<br> complete removal of glycerol was achieved after six diafiltration cycles. This was in<br> accordance with the simulation results of a mass balance model. The purity of the product<br> solution increased to 80%, which is comparable with commercially products containing<br> functional carbohydrates. The achieved product recovery was around 84%.</p>
N2-sorption and catalytic results of doped Cu-catalysts for glycerol hydrogenolysis
<p>This repository includes experimental data associated with the following publication: A. Bouriakova et al., "Dopant induced stabilization of alumina supported copper – impact on the catalytic performance in the hydrogenolysis of glycerol to 1,2-propanediol", submitted to Catalysis Communications in April 2020</p> <p> </p> <p>In this work, five catalysts supported on γ-Al<sub>2</sub>O<sub>3</sub> were studied, i.e. one bare Cu and four doped-Cu catalysts. The catalysts were prepared via sequential impregnation. The nominal dopant loading was 1wt%, for the Cu a nominal value of 10wt% was selected:</p> <p>- 10wt% Cu/-Al2O3, indicated as Cu</p> <p>- 1wt%Ba-10wt% Cu/-Al2O3, indicated as Ba-Cu<br> - 1wt%Ce-10wt% Cu/-Al2O3, indicated as Ce-Cu<br> - 1wt%Cs-10wt% Cu/-Al2O3, indicated as Cs-Cu<br> - 1wt%La-10wt% Cu/-Al2O3, indicated as La-Cu</p> <p> </p> <p>The catalysts were investigated for 68 h for continuous flow pure glycerol hydrogenolysis in a trickle bed regime at 473 K, 7.5 MPa, 135 kg<sub>cat </sub>s mol<sup>-1</sup><sub>glycerol </sub>and 7 mol<sub>H2</sub> mol<sup>-1</sup><sub>glycerol</sub>, and compared for glycerol conversion, 1,2-propanediol selectivity and stability.</p> <p>The data file contains the glycerol conversion and selectivities over time for the studied catalysts, in combination with N<sub>2</sub>-sorption data of those catalysts. In addition, an overview of literature data on glycerol hydrogenolysis is given (Fig 1 in the manuscript).</p>
Glycerol Trinitrate Reductase XdpB from Agrobacterium sp. R89-1 (FMN-free form) - Diffraction Data
<p>Glycerol Trinitrate Reductase XdpB from Agrobacterium sp. R89-1 (FMN-free form) - Diffraction Data</p> <p>PDB code: 5EPD<br> http://www.rcsb.org/pdb/explore/explore.do?structureId=5EPD</p> <p>Funding:<br> The study was supported by grant No. 720414 of the Grant Agency of Charles University, the project RVO 61388971 to the Institute of Microbiology CAS, v. v. i. and 86 652 036 to the Institute of Biotechnology CAS, v. v. i. and to project<br> BIOCEV CZ.1.05/2.1.00/19.0390 from the ERDF and MEYS and in part by the Grant Agency of the Czech Technical University in Prague, grant No. SGS16/246/OHK4/3T/14.</p> <p> </p>
Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose
<p>We provide here the underlying data of the publication "Three‐level hybrid modeling for systematic optimization of biocatalytic synthesis: α‐glucosyl glycerol production by enzymatic trans‐glycosylation from sucrose". Please find the abstract below.</p> <p>Mechanism-based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic-empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase-catalyzed glycosylation of glycerol from sucrose, to synthesize the cosmetic ingredient α-glucosyl glycerol (GG). The empirical model part was developed to capture nonspecific effects of high sucrose concentrations (up to 1.5 M) on microscopic steps of the enzymatic trans-glycosylation mechanism. Based on verified predictions of the enzyme performance under initial rate conditions (Level 1), the hybrid model was expanded by microscopic terms of the reverse reaction to account for the full-time course of GG synthesis (Level 2). Lastly (Level 3), the application of the hybrid model for comprehensive window-of-operation analysis and constrained optimization of the GG production (~250 g/L) was demonstrated. Using two candidate sucrose phosphorylases (from <em>Leuconostoc mesenteroides</em> and <em>Bifidobacterium adolescentis</em>), we reveal the hybrid model as a powerful tool of “process decision making” to guide rational selection of the best-suited enzyme catalyst. Our study exemplifies a closing of the gap between enzyme kinetic models considered for mechanistic research and applicable in technologically relevant reaction conditions; and it highlights the important benefit thus realizable for biocatalytic process development.</p>
APPENDIX 3 in Fungal/bacterial syntrophy of glycerol utilization
APPENDIX 3. — Growth of Escherichia coli T.Escherich on M0 medium supplemented with glycerol. Escherichia coli DH5α was inoculated at position indicated by the blue dots (except at the one on the center where Podospora anserina (Rabenh.) Niessl would be inoculated when the plates contain the fungus, e.g. see Fig. 7) on the indicated media and the plates were incubated for 20 days at 27°C under constant illumination at which point photographs were taken. In these conditions, the growth of the E. coli colonies is readily visible after one week of incubation and starts to mask the blue dots, except on M0 that does not permit the growth of E. coli.
FIG. 7 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 7. — Production of mycelium and perithecia in the presence of Escherichia coli T.Escherich by Podospora anserina (Rabenh.) Niessl wild-type and mutant strains. The plates were inoculated at the same time at the center by a mat+/mat- heterokaryon of the fungus with the indicated genotypes, alone or with E. coli DH5α at the position indicated by the blue dots on the right and left of the center (visible on the M0 plates). The plates were incubated for 20 days at 27°C and constant illumination, after which they were photographed.Escherichia coli promoted the growth of the mycelium (clearly visible on plates containing 0.1 M glycerol) for all strains, and development of perithecia (clearly visible as small black dots on plates containing 0.05 M glycerol) in all strains except for the PaGut2Δ mutant.
FIG. 4 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 4. — Phenotype of the G3P mutants. Growth and fertility on M2 were evaluated eight days after inoculation of mat+/mat- heterokaryons of the indicated strains. For M2, the top plates are cultures on the M2 medium; the bottom ones are the cover of the same plates onto which ascospores seen as tiny black dots were expelled. Growth on M0, M0 + 0.05 M glycerol, and M0 + 0.1 M glycerol was evaluated after four days of growth.
FIG. 2 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 2. — Growth and fertility of Podospora anserina (Rabenh.) Niessl on glycerol media. The plates were inoculated at the center with a mat+/mat- heterokaryon and incubated for three weeks at 27°C in the presence of constant light, at which time pictures were taken. Arrowheads point toward ascospore-containing perithecia. See also Figure 4 for a dark field picture of the wild-type strain growing on M0 and M0 + 0.05 M glycerol. Top, Petri plate (Ø= 8 cm). Scale bars: 200 µm.
FIG. 1 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 1. — Scheme of glycerol import and catabolism in Podospora anserina (Rabenh.) Niessl as transposed from pathways known in Saccharomyces cerevisiae Meyen ex E.C.Hansen, Schizosaccharomyces pombe Lindner, and Aspergillus nidulans G.Winter.
FIG. 6 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 6. — Production of perithecia by Podospora anserina (Rabenh.) Niessl in the presence of Paenibacillus validus (Nakamura) Ash et al. and Escherichia coli T.Escherich. The presence of large amounts of mature perithecia (the black dots) are seen around and farther away from the bacterial colony on media containing 0.02 M glycerol. The fungus formed a dense mycelium on a plate containing 0.1 glycerol and the bacteria. Note that at 0.1 M glycerol, P. anserina remained sterile even in the presence of bacteria. Scale bars: 250 µm.
FIG. 3 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 3. — Toxicity of glycerol. M2 and M0 Plates containing the indicated concentration of glycerol (gly) and sorbitol (sorb) were inoculated at the center with a mat+/mat- heterokaryon and incubated for two weeks at 27°C in the presence of constant light, at which time pictures were taken. Perithecia are visible as small black dots. On the M2 medium, the presence of 0.1 M of glycerol inhibited growth and abolished fertility; higher concentrations of glycerol resulted in no growth. On the contrary, Podospora anserina (Rabenh.) Niessl grew and was fertile even in M2 medium containing the highest concentration of sorbitol. Podospora anserina was also fertile on medium containing sorbitol as sole carbon source at all concentrations, while it produced no visible perithecia on a medium with the same glycerol concentrations after two weeks.
FIG. 5. — The PaGut1 in Fungal/bacterial syntrophy of glycerol utilization
FIG. 5. — The PaGut1Δ and PaGUT2Δ mutants are sterile on glycerol media. Small but mature perithecia can be seen at the periphery of the colony when the wild type is incubated for 20 days on media containing 0.02 M glycerol, while they cannot be seen in the PaGut1Δ, PaGUT2Δ, and PaGut1Δ PaGUT2Δ mutants. Bottom, enlargement of representative fruiting bodies. Scale bars: 250 µm.
APPENDIX 2 in Fungal/bacterial syntrophy of glycerol utilization
APPENDIX 2. — Phylogenetic analysis of NDGD. The tree was rooted with the divergent protein encoded by Pa_7_11100. The AN7193 protein defined as a NDGD is in red and the Podospora anserina (Rabenh.) Niessl proteins are in blue.
APPENDIX 1 in Fungal/bacterial syntrophy of glycerol utilization
APPENDIX 1. — Southern blot validation of the deletions. For each gene, on the left, schematic representation of the wild-type and deleted locus. Enzymes and probes (in yellow with *) used for Southern blots are indicated; on the right, corresponding autoradiograms. In both cases, the two tested candidates had the correct replacement; WT = wild type
Data for the paper: The Role of Glycerol in Manufacturing Freeze-Dried Chitosan and Cellulose Foams for Mechanically Stable Scaffolds in Skin Tissue Engineering
<p>The Dataset contains all the data, described in the article "<strong>The Role of Glycerol in Manufacturing Freeze-Dried Chitosan </strong><br><strong>and Cellulose Foams for Mechanically Stable Scaffolds in Skin Tissue Engineering</strong>."</p> <p><strong><em>Abstract</em></strong><br>Various strategies have extensively explored enhancing the physical and biological properties of chitosan and cellulose scaffolds for skin tissue engineering. This study presents a straightforward method involving the addition of glycerol into highly porous structures of two polysaccharide complexes: chitosan/carboxymethyl cellulose (Chit/CMC) and chitosan/oxidized cellulose (Chit/OC); during a one-step freeze-drying process. Adding glycerol, especially to Chit/CMC, significantly increased stability, prevented degradation, and improved mechanical strength by nearly 50%. Importantly, after 21 days of incubation in enzymatic medium Chit/CMC scaffold has almost completely decomposed, while foams reinforced with glycerol exhibited only 40% mass loss. It is possible due to differences in multivalent cations and polymer chain contraction, resulting in varied hydrogen bonding <br>and, consequently, distinct physicochemical outcomes. Additionally, the scaffolds with glycerol improved the cellular activities resulting in over 40% higher proliferation of fibroblast after 21 days of incubation. It was achieved by imparting water resistance to the highly absorbent material and aiding in achieving a balance between hydrophilic and hydrophobic properties. This study clearly indicates the possible elimination of additional crosslinkers and multiple fabrication steps that can reduce the cost of scaffold production for skin tissue engineering applications while tailoring mechanical strength and degradation.</p> <p><strong>Figure 2.</strong> Morphology. SEM micrographs of the internal structure of the freeze-dried scaffolds. Results of porosity analysis. The methodology and data are described in the README file in the folder.</p> <p><strong>Figure 3.</strong> Mechanical test results. Representative stress-strain curves from the tensile test of all freeze-dried scaffolds, where (A)<br>– measurement performed in dry conditions, (B) – measurement performed in wet conditions. All are described in the README file in the folder.</p> <p><strong>Figure 4. </strong>Swelling behavior of all scaffolds. B – Two representative vials with a visual demonstration of swelling, samples marked with circles: Chit/CMC sample submerged in the PBS (blue circle) and Chit/CMC/Glyc sample floating on the surface (green circle). The arrows lead to photos of scaffolds taken from vials directly after swelling. C – Gel fraction analysis in aqueous solution after 24 <br>6 h. D – Time after which the water droplet is absorbed into the scaffold. E – Photographs of water droplet shape changes on Chit/CMC and Chit/CMC/Glyc scaffolds over time. All are described in the README file in the folder.</p> <p><strong>Figure 5</strong>. Fourier Transform Infrared Spectroscopy (ATR-FTIR) analysis results. Details are in the README file in the folder.</p> <p><strong>Figure 6.</strong> The FTIR spectra of eluates from degraded scaffolds collected on a microscopic glass slide. Details are in the README file in the folder.</p> <p><strong>Figure 7.</strong> The degradation studies of all scaffolds over 21 days of experiments in A – enzymatic medium. B – cell culture medium. Details are in the README file in the folder.</p> <p><strong>Figure 9. </strong>Cell experiments and toxicity analysis. Cytotoxicity of eluates taken from degraded scaffolds. B – Direct fibroblast seeding on scaffolds during 14 days of culture period. C – Direct fibroblast seeding on scaffolds during 14 days of culture period without control to better see the effect of glycerol. Details are in the README file in the folder.</p>
Kinetic Insights into Glycerol Electrooxidation on Nickel: Current-Dependent Product Distribution and Reaction Mechanism
<p>## FILE DESCRIPTION<br>--------------<br>### Figure 1<br>- Fig1a.txt : Cyclic voltammetry of a Ni-based electrode in 0.1 M LiOH and 50 mM glycerol, measured at 5 mV s^-1. <br>- Fig1b.txt : IS spectra obtained at 1.50 V vs RHE in 0.1 M LiOH and 0.1 M LiOH + 50 mM glycerol.<br>- Fig1c.txt : Chronopotentiometric curves at 1 mA cm^-2, 3 mA cm^-2, 5 mA cm^-2 during 2 hours in 0.1 M LiOH + 50 mM glycerol.</p> <p>### Figure 2<br>- Fig2a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1 mA cm^-2.<br>- Fig2b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 3 mA cm^-2.<br>- Fig2c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 5 mA cm^-2.<br>- Fig2d.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 10 mA cm^-2.<br>- Fig2e.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 1 mA cm^-2.<br>- Fig2f.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 3 mA cm^-2.<br>- Fig2g.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 5 mA cm^-2.<br>- Fig2h.txt : Concentration of reaction products vs. time plots during glycerol oxidation (50 mM) in 0.1 M LiOH at 10 mA cm^-2.</p> <p>### Figure 3<br>- Fig3a.txt : Rate constant comparison at varying current densities applied for the formation of formate, glycolate, glycerate, tartronate and oxalate with its error bar.</p> <p>### Figure 4<br>- Fig4a.txt : Differential optical density (m∆O.D) taken at the maximum absorption peak as a function of potential applied in a solution of 0.1 M LiOH and LiOH 0.1 M + 50 mM glycerol in different regions (capacitive, NiOOH formation and GEOR and OER).<br>- Fig4b.txt : Rate law plot for glycerol and LiOH considering current density as a function of the normalized differential absorption (m∆O.D).</p> <p>### Figure S3<br>- FigS3.txt : X-ray diffraction (XRD) analysis </p> <p><br>### Figure S4<br>- FigS4a.txt : Cyclic Voltammetries in different electrolytes.</p> <p>### Figure S5<br>- FigS5a.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.53 V vs RHE.<br>- FigS5b.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.62 V vs RHE.<br>- FigS5c.txt : Faradaic Efficiencies for Glycerol oxidation electrolysis at 1.78 V vs RHE.</p> <p>### Figure S6<br>- FigS6a.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 1 mA cm^-2.<br>- FigS6b.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 3 mA cm^-2.<br>- FigS6c.txt : pH measurement near to the surface of the electrode and in the bulk of the solution every five minutes at 5 mA cm^-2.</p> <p>### Figure S7<br>- FigS7a.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH.<br>- FigS7b.txt : Chronoamperometry measurement without glycerol.<br>- FigS7c.txt : UV-Vis absorbance spectra as a function of applied potentials in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8d.txt : Chronoamperometry measurement with glycerol.</p> <p>### Figure S8<br>- FigS8a.txt : Differential UV-Vis spectra of pre-catalytic (species formed in capacitive and NiOOH formation region) in 0.1 M LiOH.<br>- FigS8b.txt : Differential UV-Vis spectra of catalytic species (formed in OER and GEOR region) in 0.1 M LiOH.<br>- FigS8c.txt : Differential UV-Vis spectra of pre-catalytic species in 0.1 M LiOH + 50 mM glycerol. <br>- FigS8d.txt : Differential UV-Vis spectra of catalytic species in 0.1 M LiOH + 50 mM glycerol.<br>- FigS8e.txt : Steady state J-V curve with the onset for OER (Oxygen Evolution Reaction) and GEOR (Glycerol Electrooxidation Reaction) indicated.</p> <p>### Figure S9<br>- FigS9.txt : Rate law plot for glycerol and LiOH considering current density (j) as a function of the normalized differential absorption (m∆O.D).</p>
Dataset related to article "On the extension of the use of a standard operating procedure for nicotine, glycerol and propylene glycol analysis in e-liquids using mass spectrometry"
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Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of non-equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using the periodic perturbation technique [1]. </p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains three sets of simulations, with different perturbation force parameters ("Em*"), where configuration files necessary to reproduce molecular simulations simulations are provided. Maps of density and velocity field are in "Em*"->"Flow".</p> <p>A small self-contained Python script to fit the velocity fields to a periodic cosine perturbation is provided (fit-periodic.py). Alternatively, viscosity can be obtained from energy outputs by running:</p> <pre><code>gmx energy -f ener.edr</code></pre> <p>and selecting "1/Viscosity". Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p>
Shear viscosity coefficient of acqueous glycerol from equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using linear response theory (Einstein relations) [1]. The post-processing of simulation results is inspired by the method of Zhang et al. [2].</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol, being "000" pure water (0%) and "100" pure glycerol (100%). Zip folders contain</p> <ul> <li>Expected value and integral of the square of the off-diagonal components of the pressure gradient ("EnergyOutputs")</li> <li>Initial configurations used to start the ensemble of replicas from which viscosity is computed ("InitConfReplicas")</li> <li>Output, state and topology of replica simulations ("MdrunOutputs")</li> </ul> <p>A self-contained Python script (compute-visco.py) for the computation of viscosity from the output of an ensemble of simulations is provided. Integrals used to quantify viscosity via Einstein's relation can be obtained from energy output files (.edr) by running:</p> <pre><code>gmx energy -f ener.edr -evisco -eviscoi -vis</code></pre> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p> <p>[2] Y. Zhang et al., Reliable Viscosity Calculation from Equilibrium Molecular Dynamics Simulations: A Time Decomposition Method, J. Chem. Theory Comput. 2015, 11, 3537−3546, <a href="https://doi.org/10.1021/acs.jctc.5b00351">https://doi.org/10.1021/acs.jctc.5b00351</a></p>
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