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21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one

21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one is a steroid compound with a 21-hydroxy group, a 9beta,10alpha configuration, and a 5,7-diene structure.
This molecule is of interest in research related to steroid biosynthesis and metabolism.
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Most cited protocols related to «21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one»

1
Constraint-Based Modeling of E. coli Metabolism
Cited 29 times
The constructed stoichiometric model of E. coli contains all presently known reactions in central carbon metabolism with 98 reactions and 60 metabolites (Supplementary Table I). To apply FBA, the reaction network was automatically translated into a stoichiometric matrix (Schilling and Palsson, 1998 (link)) by means of a parser program implemented in Matlab (MATLAB®, version 7.0.0.19920 (R14), The MathWorks Inc., Natick, MA). Assuming steady-state mass balances, the production and consumption of each of the m intracellular metabolites Mi is balanced to yield
with
S corresponds to the stoichiometric matrix (m × n) and ν (n × 1) to the array of n metabolic fluxes with νilb as lower and νiub as upper bounds, respectively. The above equations represent the conservation law of mass that is fundamental to constraint-based modeling. For all herein presented stoichiometric analyses, maximization of biomass yield is synonymous to the frequently used maximization of growth rate objective (Price et al, 2004 (link)). This is because stoichiometric models are sets of linear balance equations that are inherently dimensionless, hence maximization of the biomass reaction optimizes the amount of product (i.e., the yield) rather than a time-dependent rate of formation. The P-to-O ratio constraint was implemented by omitting the energy-coupling NADH dehydrogenase I (Nuo), cytochrome oxidase bo3 (Cyo) and/or cytochrome oxidase bd (Cyd) components of the respiratory chain. For a ratio of unity, Cyd and Nuo were set equal to zero. Under anaerobic conditions, electron flow is only possible via the NADH oxidases Nuo or NADH dehydrogenase II (Ndh) to fumarate reductase (Frd), hence coupled to succinate fermentation. For nitrate respiration, the terminal oxidase nitrate reductase (Nar) was used instead of Cyd or Cyo (Unden and Bongaerts, 1997 ).
For the genome-scale analysis we used two recently reconstructed models of E. coli metabolism (Edwards and Palsson, 2000b (link); Reed et al, 2003 (link)). In silico growth was simulated on glucose minimal medium for all six environmental conditions. ADP remained unbalanced, since otherwise formation of adenosine would be carbon-limited. For the proton-balanced model of Reed et al (2003) (link), severe alternate optima occurred in central carbon metabolism given an unlimited proton exchange flux between the cell and the medium and a P-to-O ratio of 2, that is the upper bound of the biologically feasible range of P-to-O ratios (Unden and Bongaerts, 1997 ). To prevent the unlimited production of ATP equivalents through the ATPS4r reaction under this condition, all external protons involved in the respiratory chain and the transhydrogenase reaction were balanced (specifically, we balanced the external protons around the reactions ATPS4r, TDH2, CYTBD, CYTBO3, NO3R1, NO3R2, NADH6, NADH7, NADH8). A P-to-O ratio of 2 was implemented by assuming both the transport of four protons through CYTBO3 and NADH6 across the membrane and the diffusion of four protons through ATPS4r for the formation of one ATP equivalent.
Schuetz R., Kuepfer L, & Sauer U. (2007). Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular Systems Biology, 3, 119.
Publication 2007
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Adenosine Adjustment Disorders Carbon Cell Respiration Cells Diffusion Electrons Escherichia coli Fermentation Genome Glucose Metabolism NADH Dehydrogenase Complex 1 NADH dehydrogenase II NADH oxidase Nitrate Reductase Nitrates Oxidase, Cytochrome-c Oxidases Protons Protoplasm Respiratory Chain Succinate Succinate Dehydrogenase Tissue, Membrane Unden
2
Metabolic Network Flux Analysis
Cited 15 times
A metabolic network of m metabolites and n reactions is represented by an m×n stoichiometric matrix S, where each entry Sij contains the stoichiometric coefficient of metabolite i in reaction j. A flux vector is a tuple of reaction rates, , where is the rate of reaction i in the network. Reactions are grouped into reversible ones ( ) and irreversible ones ( ). For a reaction it holds that this and other imposed flux bounds, e.g., lower and upper bounds per reaction, are collectively denoted by (which defines a convex set). A flux vector is called feasible or a mode if it satisfies a set of steady-state mass-balance constraints that can be compactly expressed as: An elementary mode is a feasible flux vector with minimal support, that is, there is no other feasible flux vector with , where is the support (i.e., the set of nonzero entries) of
[19] , [22] (link). A reaction i is called blocked if it cannot be active under any mode, that is, there exists no mode such that (in practice , for some small positive threshold ε). A metabolic network model that contains no blocked reactions is called (flux) consistent[19] , [27] (link).
Vlassis N., Pacheco M.P, & Sauter T. (2014). Fast Reconstruction of Compact Context-Specific Metabolic Network Models. PLoS Computational Biology, 10(1), e1003424.
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Publication 2014
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Cloning Vectors Matrix-M Metabolic Networks
3
Flux Balance Analysis Validation of a Metabolic Model
Cited 14 times
Flux balance analysis (FBA) was used to simulate all experimental conditions to validate the iBsu1103 model. FBA defines the limits on the metabolic capabilities of a model organism under steady-state flux conditions by constraining the net production rate of every metabolite in the system to zero [36 (link)-39 (link)]. This quasi-steady-state constraint on the metabolic fluxes is described mathematically in Equation 1:

In Equation 1, N is the m × r matrix of the stoichiometric coefficients for the r reactions and m metabolites in the model, and v is the r × 1 vector of the steady-state fluxes through the r reactions in the model. Bounds are placed on the reaction fluxes depending on the reversibility of the reactions:


- (CDW = cell dry weight). When simulating a gene knockout, the bounds on the flux through all reactions associated exclusively with the gene being knocked out (or associated exclusively with a protein complex partially encoded by the gene being knocked out) were reset to zero. When simulating media conditions, only nutrients present in the media were allowed to have a net uptake by the cell. All other transportable nutrients were allowed only to be excreted by the cell. Details on conditions for all FBA simulations performed are provided in Table S8 in Additional data files 1 and 2.
Henry C.S., Zinner J.F., Cohoon M.P, & Stevens R.L. (2009). iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations. Genome Biology, 10(6), R69.
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Publication 2009
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Cells Cloning Vectors Gene Knockout Techniques Genes Matrix-M Nutrients Staphylococcal Protein A
4
Automated Metabolic Flux Analysis with OpenMebius
Cited 10 times
OpenMebius is implemented in MATLAB (MathWorks, Natick, MA, USA) for the Windows platform. The software consists of two parts: automated model construction and metabolic flux estimation by nonlinear optimization. Functions for processing raw mass spectrum data and the determination of confidence intervals are also included. OpenMebius is designed for conventional 13C-MFA and INST-13C-MFA using mass spectrometry data. Isotopic labeling enrichment of metabolites is described by a mass distribution vector (MDV) [25 (link)]:
MDVj=[m+0m+1⋮m+n] with  ∑i=0nm+i=1,
where MDVj is the vector of isotopic labeling enrichment of metabolite j. m + i indicates the relative abundance of a metabolite in which i carbons are labeled with 13C. To obtain the MDVj of the carbon skeleton, mass spectrum data are corrected for the presence of naturally occurring isotopes using the correction matrix [26 (link)].
In conventional 13C-MFA, a metabolic model M is an algebraic equation used to generate MDVjsim from the vector of metabolic flux (v) and the isotopic labeling pattern of a carbon source (xinp), as shown in (1).
Since the metabolic flux is determined in cells at metabolic steady state, v follows the stoichiometric equation described by
Sv=0,
where S is the stoichiometric matrix. In OpenMebius, S is constructed from a metabolic network described in the “Rxns” column in a user-defined configuration worksheet (Figure 1(b)), taking into consideration the fluxes for biomass syntheses and product excretion. MDVjsim is calculated by the framework of elementary metabolite units (EMU) [27 (link)] using the carbon transition information described in the “carbon_transitions” column of the configuration worksheet (Figure 1(b)). In the framework, the carbon transition network is decomposed to cascade networks of EMUs depending on those carbon numbers. The cascade networks of the EMUs with sth carbon follow the EMU balance equation [27 (link)]:
As(v)Zs=Bs(v)Ys(xinp).
Here, each row in matrix Zs is MDV of corresponding EMU. The matrix Ys(xinp) includes EMUs of the carbon source or the smaller size EMUs. The element as(i, j) in row i and column j of matrices As(v) and the element bs(i, j) of matrix Bs(v) are described, respectively, as follows:
as(i,j) ={−sum  of  fluxes  consuming, ith  EMU  in  Zs,i=j,flux  to  ith  EMU  in  Zs  from  jth  EMU  in  Zs,i≠j,bs(i,j)=−flux  to  ith  EMU  in  Zs  from  jth  EMU  in  Ys.
In the case of INST-13C-MFA, the metabolic model M is expanded to describe a transition state of isotopic labeling (Figure 1(d)) by considering the dilution of isotopic labeling enrichment depending on the pool size of intermediates, as shown in (3), where X is a vector of the pool size of each metabolite that is constant under metabolic steady state. tk is the time of the kth sampling point. In this study, instead of a direct description of the metabolic model M, time-dependent changes in the isotopic labeling enrichment of metabolite j are described by the differential equation as follows:
dMDVj,t=tksimdt  =1Xj(∑i=1n(viinMDVi,t=tksim)−∑l=1m(vloutMDVj,t=tksim)),
where viin and vlout represent the fluxes of the ith inflow reaction and the lth outflow reaction of metabolite j, respectively. The model is automatically constructed by “ConstEMUnetwork.m.” Detailed rules to describe a user-defined metabolic pathway and carbon transition network are provided on the project home page (http://www-shimizu.ist.osaka-u.ac.jp/hp/en/software/OpenMebius.html). Euler's method is implemented to solve the ordinary differential equation (8) without adaptive step size control. Stiff equations can be resolved by carefully selecting the step size. The MDVj,t=tksim are standardized for each step to prevent divergence. Moreover, no specific libraries were used to implement the algorithm for solving differential equations.
Kajihata S., Furusawa C., Matsuda F, & Shimizu H. (2014). OpenMebius: An Open Source Software for Isotopically Nonstationary 13C-Based Metabolic Flux Analysis. BioMed Research International, 2014, 627014.
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Publication 2014
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Acclimatization Anabolism Carbon Carbon Isotopes Cells Cloning Vectors Emus Indium Isotopes Mass Spectrometry Metabolic Networks Skeleton Technique, Dilution
5
Dynamic Flux Balance Analysis for Microbial Ecosystems
Cited 10 times

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Publication 2014
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Cell Death Cells Cloning Vectors Comet Assay difluprednate Kinetics Metabolism Nutrients Protoplasm secretion Technique, Dilution

Most recents protocols related to «21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one»

1
Quantification of Peptidoglycan Metabolites in P. aeruginosa
P. aeruginosa ID40 parental and mutant strains were grown overnight in LB medium. The OD600 of overnight cultures (LB medium) was measured. 100 ml LB bacteria cultures with an initial OD600 of 0.05 per ml were grown for 6 h. Cells were harvested and OD600 measured. Bacteria were pelleted and resuspended in 20 ml 50 mM Tris-HCL buffer, pH 7.6 to a final concentration of OD600 = 5/ml (final OD600 = 100). Subsequently, bacteria were centrifuged at 3,000 g for 10 min. The supernatant was discarded and the pellet frozen at –80 °C. The next day, bacteria were resuspended in 400 µl water and the cultures boiled for 15 min at 95 °C. Cultures were cooled down to and centrifuged at room temperature at 16,000 g for 10 min. 200 µl of the supernatant were added to 800 µl ice-cold acetone (MS grade, Sigma 34850-2.5 L) to precipitate remaining proteins in the samples. Samples were then centrifuged at 4 °C at 16,000 × g for 10 min. The supernatant was transferred in a new tube and the cytosolic fraction was dried under vacuum for 2 h at 55 °C in a Speedvac (Eppendorf). Pellets were then stored at 4 °C. The dry cytosolic fractions were then dissolved in 50 µl Millipore water. 5 µl of the samples were subjected to LC-MS analysis with an UltiMate 3000 LC system (Dionex) coupled to an electrospray ionization-time of flight mass spectrometer (MicrO-TOF II; Bruker) that was operated in positive-ion mode in a mass range 180 m/z to 1,300 m/z. Metabolite separation was achieved with a Gemini C18 column (150 by 4.6 mm, 110 Å, 5 μm; Phenomenex) at 37 °C with a flow rate of 0.2 ml/min in accordance with a previously described 45 min gradient program40 (link) with small modifications: 5 min of washing with 100% buffer A (0.1% formic acid, 0.05% ammonium formate in water), followed by a linear gradient over 30 min to 40% buffer B (acetonitrile) and a 10 min column re-equilibration step with 100% buffer A. Peptidoglycan (PG) metabolites were shown in Data Analysis (Bruker) by extracted ion chromatograms (EICs) and the area under the curves of the respective EICs were calculated in Prism 8 (GraphPad). The theoretical m/z values of the PG metabolites investigated are 276.108 m/z for anhMurNAc, 479.187 m/z for GlcNAc-anhMurNAc, 648.272 m/z for anhMurNAc-3P, 851.352 m/z for GlcNAc-anhMurNAc-3P, 790.347 m/z for anhMurNAc-5P, 680.110 m/z for UDP-MurNAc, and 1194.349 (597.678 2+) for UDP-MurNAc 5 P.
Eggers O., Renschler F.A., Michalek L.A., Wackler N., Walter E., Smollich F., Klein K., Sonnabend M.S., Egle V., Angelov A., Engesser C., Borisova M., Mayer C., Schütz M, & Bohn E. (2023). YgfB increases β-lactam resistance in Pseudomonas aeruginosa by counteracting AlpA-mediated ampDh3 expression. Communications Biology, 6, 254.
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Publication 2023
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Acetone acetonitrile anhydro-N-acetylmuramic acid Bacteria Buffers Cells Cold Temperature Cytosol formic acid formic acid, ammonium salt Freezing Parent Pellets, Drug Peptidoglycan prisma Proteins Pseudomonas aeruginosa Strains Tromethamine Vacuum Z 300
2
Robust Metabolomics Data Processing
Metabolomics data were processed with an in-house pipeline for statistical analyses and plots were generated using a variety of custom Python code and R libraries including: heatmap, MetaboAnalystR, and manhattanly. Peak height intensities were extracted based on the established accurate mass and retention time for each metabolite as adapted from the Whitehead Institute and verified with authentic standards and/or high-resolution MS/MS manually curated against the NIST14MS/MS and METLIN spectral libraries. The theoretical m/z of the metabolite molecular ion was used with a±10 ppm mass tolerance window, and a±0.2 min peak apex retention time tolerance within the expected elution window (1–2 min). To account for sample-to-sample variance in the estimated cell counts, a sum-normalization step was carried out on a per-column (sample) basis. Detected metabolite intensities in a given sample were summed, and a percentage intensity was calculated for each metabolite (custom Rscript available from NYU Metabolomics Laboratory at https://med.nyu.edu/research/scientific-cores-shared-resources/metabolomics-laboratory, contact the Laboratory Director Dr. Drew Jones at: drew.jones@nyulangone.org). The median mass accuracy vs the theoretical m/z for the library was −0.7 ppm (n=90 detected metabolites). Median retention time range (time between earliest and latest eluting sample for a given metabolite) was 0.23 min (30 min LCMS method). A signal-to-noise ratio (S/N) of 3 X was used compared to blank controls throughout the sequence to report detection, with a floor of 10,000 (arbitrary units). Labeled amino acid internal standards in each sample were used to assess instrument performance (median CV=5%).
Emrich S.M., Yoast R.E., Zhang X., Fike A.J., Wang Y.H., Bricker K.N., Tao A.Y., Xin P., Walter V., Johnson M.T., Pathak T., Straub A.C., Feske S., Rahman Z.S, & Trebak M. (2023). Orai3 and Orai1 mediate CRAC channel function and metabolic reprogramming in B cells. eLife, 12, e84708.
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Publication 2023
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Amino Acids cDNA Library Immune Tolerance Laser Capture Microdissection Python Retention (Psychology) Tandem Mass Spectrometry
3
Metabolomic Profiling of Dietary Supplement Effects
Original data collected by the UPLC-QTOF-MS system were processed by Profile Analysis 2.1 (Bruker) for the recognition, calibration, alignment, and normalization of peaks, and then converted into TXT format. To acquire more complete metabolic profiles, the obtained data in the positive and negative modes were merged and imported into SIMCA-P 14.0 (Umetrics, Stockholm, Sweden) for multivariate statistical analysis.
Principal component analysis (PCA) is an unsupervised method of pattern recognition. PCA can reveal visually the natural grouping of samples (Xu et al., 2017 (link); Hu et al., 2020 (link)). Each point represents a sample in a PCA score plot, which is convenient for the removal of outliers (Zhou et al., 2017 (link)). Whereas, orthogonal projections to latent structures discriminant analysis (OPLS-DA) is carried out for supervised regression modeling and identifies potential differential biomarkers between groups (Wang, T. et al., 2019 (link)). The values of R2Y and Q2 are key indices to evaluate the fitting quality and predictability of OPLS-DA models. The values of Q2 were larger than 0.5 and the difference between R2Y and Q2 was less than 0.3, suggesting superior quality of our models. (Wang, X. et al., 2020 (link)).
Variable importance in projection (VIP) is implemented to measure the contribution to sample classification (Yuan, Z. et al., 2020 (link)). Coupled with VIP values (VIP >3) and the Student’s t-test (p < 0.05), the m/z of significantly altered metabolites was filtered preliminarily. According to the retention time, an accurate molecular weight was determined through DataAnalysis 4.4 software (Bruker). And next, biomarker (error <5 ppm) were identified through the Human Metabolome Database (www.hmdb.ca) and Metaboanalyst (www.metaboanalyst.ca/) based on their retention time, accurate molecular weights and MS/MS fragments of ions (Zhou et al., 2019 (link); Li et al., 2022 (link)). Hierarchical clustering analysis (HCA) of biomarkers was carried out and corresponding heatmaps were acquired using Mev 4.8.0 (MeV, United States). Finally, a network diagram of perturbed metabolic pathways in the PE model involving intervention by DS, DS-Pol, DS-Oli, DS-FG, DS-FA, or DS-FO was created using the Kyoto Encyclopedia of Genes and Genomes (KEGG; www.kegg.jp/), MetaboAnalyst, and MBRole (http://csbg.cnb.csic.es/mbrole2/) databases.
Zhang J., Zhou N., Wang Y., Liu T., Cao Y., Feng W, & Zheng X. (2023). Protective effects of Descurainia sophia seeds extract and its fractions on pulmonary edema by untargeted urine and serum metabolomics strategy. Frontiers in Pharmacology, 14, 1080962.
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Publication 2023
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Biological Markers Genes Genome Homo sapiens Ions Metabolic Networks Metabolic Profile Metabolome Retention (Psychology) Student Tandem Mass Spectrometry
4
Protein-Ligand Interaction Analysis
The characterization of the mechanism of binding interactions and their types, including hydrogen bonds, alkyl, π-alkyl, halogen, and the Van der Waals interactions formed between the protein of L. mexicana and the ligands (major metabolites of the fraction MA-24F of M. alceifolia), were determined and analyzed by Discovery Studio and LigPlot + v1.4.5 using default parameters [34 (link)].
Cervantes-Ceballos L., Mercado-Camargo J., del Olmo-Fernández E., Serrano-García M.L., Robledo S.M, & Gómez-Estrada H. (2023). Antileishmanial Activity and In Silico Molecular Docking Studies of Malachra alceifolia Jacq. Fractions against Leishmania mexicana Amastigotes. Tropical Medicine and Infectious Disease, 8(2), 115.
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Publication 2023
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Halogens Hydrogen Bonds L Forms Ligands Proteins
5
Quantification of Metformin, Baxdrostat, and Metabolite
Plasma and urine samples were analyzed to measure concentrations of metformin, baxdrostat, and its primary metabolite, CIN-107-M, using validated liquid chromatography tandem mass spectrometry (LC-MS/MS) methods.
The quantifiable range for metformin in plasma was 0.5–500 ng/mL, using metformin-d6 as the internal standard. Plasma samples were extracted by protein precipitation with methanol, followed by analysis by LC-MS/MS with electrospray ionization in positive mode [ESI(+)] and multiple reaction monitoring (MRM). Reversed-phase chromatographic separation with a mobile phase gradient of 15–90% was utilized, and total run time was approximately 5 min. Precursor to product ion transitions for metformin and internal standard were 130.1–71.1 and 135.4–77.0, respectively. Similarly, the quantifiable ranges for metformin in urine were 1.00–500 ng/mL (low range) and 2.00–1250 ng/mL (high range), with similar extraction and LC-MS/MS conditions. Between-day precision (coefficient of variation [CV]%) values for metformin in plasma and urine were within 5% and 10%, respectively.
The quantifiable ranges for both baxdrostat and its metabolite CIN-107-M in plasma were 0.05–50 ng/mL (low range) and 5.00–2500 ng/mL (high range), using baxdrostat-d5 and CIN-107-M-d3, respectively, as the internal standards. Plasma samples were extracted by protein precipitation with methanol, followed by analysis by LC-MS/MS with ESI(+) and MRM. Reversed-phase chromatographic separation with a mobile phase gradient of 20–90% was utilized, and total run time was approximately 5 min. Precursors to product transition for baxdrostat and its internal standard were 364.2–291.2 and 369.2–291.2, respectively. The transitions for CIN-107-M and its internal standard were 309.2–291.2 and 313.2–295.2, respectively. Between-day precision (CV%) values for baxdrostat and CIN-107-M in plasma were within 3% and 4%, respectively.
Freeman M.W., Bond M., Murphy B., Hui J, & Isaacsohn J. (2023). Results From a Randomized, Open-Label, Crossover Study Evaluating the Effect of the Aldosterone Synthase Inhibitor Baxdrostat on the Pharmacokinetics of Metformin in Healthy Human Subjects. American Journal of Cardiovascular Drugs, 23(3), 277-286.
Publication 2023
21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one Chromatography Chromatography, Reverse-Phase Liquid Chromatography Metformin Methanol Plasma Proteins Spectrometry, Mass, Electrospray Ionization Tandem Mass Spectrometry Urine

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More about "21-hydroxy-9beta,10alpha-pregna-5,7-diene-3-ol-20-one"

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