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1,2-dilinolenoyl-3-(4-aminobutyryl)propane-1,2,3-triol →

1,2-dihexadecyl-sn-glycero-3-phosphocholine

Q: What are the key features of 1,2-dihexadecyl-sn-glycero-3-phosphocholine?

1,2-dihexadecyl-sn-glycero-3-phosphocholine is composed of two long-chain hexadecyl (cetyl) fatty acid chains esterified to the sn-1 and sn-2 positions of a glycerol backbone, with a phosphocholine head group at the sn-3 position. This unique structure gives it properties that are useful for mimicking the characteristics of biological membranes.

1,2-dihexadecyl-sn-glycero-3-phosphocholine is a synthetic glycerophosphocholine lipid commonly used as a model compound for investigating membrane structure and function.
It is composed of two long-chain hexadecyl (cetyl) fatty acid chains esterified to the sn-1 and sn-2 positions of a glycerol backbone, with a phosphocholine head group at the sn-3 position.
This lipd is useful for recreating the physicochemical properties of biological membranes in experimental systems, such as liposomes and bilayer mimetics.
Resesarch on 1,2-dihexadecyl-sn-glycero-3-phosphocholine can be optimized using PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy by helping researchers easily locate protocols from literature, pre-prints, and patents, and using AI-driven comparisons to identify the best protocols and products for their experiments.

Most cited protocols related to «1,2-dihexadecyl-sn-glycero-3-phosphocholine»

Polysome Immunoprecipitation and RNA Extraction

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Publication 2008

1,2-dihexadecyl-sn-glycero-3-phosphocholine Alabaster austin Brain Stem Buffers Cells Cerebellum Chloroform Cholinergic Agents Cold Temperature Cycloheximide Deoxyribonucleases Digestion Dithiothreitol Endoribonucleases Ethanol G-substrate Goat HEPES inhibitors Isopropyl Alcohol Lipids Magnesium Chloride Mice, Laboratory Mice, Transgenic Motor Neurons Nonidet P-40 Polyribosomes Protease Inhibitors Purkinje Cells Ribosomal RNA RNA, Messenger Sodium Acetate Sodium Chloride Striatum, Corpus Teflon Tissues trizol

NMR Structural Analysis of EGFR Transmembrane-Juxtamembrane Domain

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Publication 2013

1,2-dihexadecyl-sn-glycero-3-phosphocholine Amides Buffers Dimyristoylphosphatidylcholine Edetic Acid EGFR protein, human Inclusion Bodies Lipid A Lipids Proteins Sodium Azide Staphylococcal Protein A tris(2-carboxyethyl)phosphine Vertebral Column Vibration

Titration of Fas Transmembrane Domain

The Fas TMD was reconstituted in q = 0.7 bicelles and DHPC was progressively added to reduce the bicelle size. The detergent was taken from a concentrated stock solution (660 mM DHPC) made in the same buffer of the protein sample and it was added in small aliquots (few μL per step) to minimize possible dilution effects. To monitor the progress of the titration by NMR, a 2D ¹H-¹⁵N TROSY-HSQC spectrum was recorded at 600 MHz (Table S1) at each of the following q values: 0.7, 0.6, 0.5, 0.4 and 0.3. The chemical shift assignments of the human Fas TMD was taken from the Biological Magnetic Resonance Bank (BMRB)^{[20 (link)]}, entry 25930^{[1c (link)]}.

Piai A., Fu Q., Dev J, & Chou J.J. (2016). Optimal Bicelle q for Solution NMR Studies of Protein Transmembrane Partition. Chemistry (Weinheim an der Bergstrasse, Germany), 23(6), 1361-1367.

Publication 2016

1,2-dihexadecyl-sn-glycero-3-phosphocholine Biopharmaceuticals Buffers Detergents Homo sapiens Magnetic Resonance Proteins Technique, Dilution Titrimetry

Molecular Dynamics Simulations of Lipid Bilayers

Table 2 lists simulations of pure DHPC and pure DPPC bilayers presented in this study. Structural and mechanical properties calculated include form factors (

| F (q) |

), SDP,

A_{l}

, and

K_{A}

. Properties focusing on the interaction of water with the ether and ester linkages are: pair-correlation functions,

g (r)

, of the ether oxygen (in DHPC) and ester and carbonyl oxygens (in DPPC) with water; and z-profiles of the electrostatic potential (

ψ

), water potential of mean force (

{pmf}_{w}

), and density-weighted orientation of water dipole,

ρ (z) \cos θ (z)

.
Bilayer simulations contained a total of 80 lipids (40 per leaflet) and 30 waters per lipid. Initial coordinates were produced by the CHARMM-GUI^{28 (link)}Membrane Builder^{29 (link)–31 (link)} for DPPC. Necessary atom-type substitutions and deletions were made to produce the ether linkage of DHPC. “C36” denotes partial-charge assignments based on C36 charges for PEG and unchanged dihedral parameters (see Sec. S2 and Table S1). “C36e” denotes the new partial-charge and dihedral parameters found in Sec. 3.1. Simulations were run in NAMD using a 2-fs timestep and Langevin damping coefficient of 1/ps. Data were analyzed after equilibration, from 30 – 100 ns. Standard errors between replicates were calculated for

A_{l}

and scattering parameters, and from uncorrelated blocks for

K_{A}

. “NBFIX” parameters for carboxylate, ester, and phosphate oxygens³² were used in simulations with NaCl.
The scattering densities of functional groups from simulation were obtained with the software package SIMtoEXP.^{33 (link)} For ready comparison with the experimental SDP,^{12 (link)} three Gaussians were used to describe the volume probabilities of the lipid headgroup: one each for the glycerol and ether linkage (G1), the phosphate and CH₂CH₂N moiety (G2), and the trimethyl groups of the terminal choline (G3). For calculating the bilayer hydrocarbon thickness (2D_C), the total hydrocarbon region was represented by an error function. Matlab R2016a³⁴ was used to fit the Gaussian and error functions and obtain the difference between electron density maxima (D_HH). X-ray and neutron form factors were calculated using SIMtoEXP^{33 (link)} with a Fourier transform of the total densities.
Area compressibility moduli of DHPC and DPPC bilayers were calculated using fluctuations in area:³⁵

K_{A} = \frac{k_{B} T 〈 A_{l} 〉}{〈 σ_{A_{l}}^{2} 〉 n_{L}} .

Here,

〈 σ_{A_{l}}^{2} 〉

is the mean square fluctuation in area per lipid, n_L is the number of lipids per leaflet, and k_B is Boltzmann’s constant.
The electrostatic potential profile along the bilayer normal was calculated by integration of the Poisson equation:

ψ (z) = - \frac{4 π}{ϵ_{0}} \int_{0}^{z'} ρ_{c} (z'') d z''

where

ρ_{c}

is the total time-averaged charge density and

ϵ_{0}

is the permittivity of free space.

ψ

is greater at the center of the membrane than in bulk water. The total dipole potential drop across the membrane,

ΔΨ

, is here defined as the difference in the electrostatic potential of bulk water,

ψ (0)

, from that inside the membrane:³⁶

ΔΨ = ψ (z) - ψ (0) = - \frac{4 π}{ϵ_{0}} \int_{0}^{\infty} \int_{0}^{z'} ρ_{c} (z'') d z'' d z'

CHARMM^{20 (link)} was used to find the water density and dipole orientation with respect to position along the bilayer normal,

ρ (z) \cos θ (z)

The software package Visual Molecular Dynamics (VMD)^{37 (link)} was used to find the pair-correlation functions,

g (r)

, for ether oxygens of DHPC and carbonyl and ester oxygens of DPPC with water hydrogens, and to capture images of the bilayers. Peak positions and integrals of

g (r)

were computed with Matlab R2016a.³⁴

Leonard A.N., Pastor R.W, & Klauda J.B. (2018). Parameterization of the CHARMM All-Atom Force Field for Ether Lipids and Model Linear Ethers. The journal of physical chemistry. B, 122(26), 6744-6754.

Publication 2018

1,2-dihexadecyl-sn-glycero-3-phosphocholine Choline Dietary Fiber Electrons Electrostatics Esters Ethers Gene Deletion Glycerin Hydrocarbons Hydrogen Lipid A Lipids Membrane Potentials Molecular Dynamics Oxygen Phosphates Radiography Sodium Chloride Tissue, Membrane

Thermodynamics of Bcl-XL Interactions with BH3 Peptides

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Publication 2011

1,2-dihexadecyl-sn-glycero-3-phosphocholine 2-Mercaptoethanol Bax protein (53-86) Buffers Calorimetry Dimyristoylphosphatidylcholine Edetic Acid Entropy Ligands Molar Peptides Proteins Sodium Chloride sodium phosphate Syringes Technique, Dilution Titrimetry

Most recents protocols related to «1,2-dihexadecyl-sn-glycero-3-phosphocholine»

Regulator Perspectives on Communicating Safety Concerns

For the first study aim, the outcomes were the regulators’ opinions regarding the need to update the SmPC and to send a DHPC, and the determinant was the level of concern. For the second study aim, the outcome was the concern regarding the safety issues, and the determinants were the attributes of the safety issue, the demographic and professional characteristics, and the regulators’ attitudes (Fig. 1).Fig. 1

Overview of the study aims, outcomes, and determinants. ¹Aim A, to explore to what extent regulators’ opinions regarding the need to communicate through updating the SmPC or sending a DHPC is influenced by regulators’ concern about the safety issue. ²Aim B, to assess whether regulators’ concerns are influenced by certain characteristics of the safety issue, demographic and professional characteristics of the regulators, and regulators’ attitudes. SmPC summary of product characteristics, DHPC direct healthcare professional communication

Roldan Munoz S., Postmus D., de Vries S.T., Gross-Martirosyan L., Bahri P., Hillege H, & Mol P.G. (2023). What Factors Make EU Regulators Want to Communicate Drug Safety Issues Related to SGLT2 Inhibitors? An Online Survey Study. Drug Safety, 46(3), 243-255.

Publication 2023

1,2-dihexadecyl-sn-glycero-3-phosphocholine Health Care Professionals Safety

Regulators' Attitudes and Safety Communication

We used descriptive statistics for the analysis of the demographic and professional characteristics, the regulators’ attitudes, the benefit-risk evaluation, and the level of concern per scenario, as well as for the calculation of the proportion of participants who considered it necessary to communicate the risk per scenario. Only those participants who completed at least one question regarding their level of concern towards the safety issue, their opinion on the need to update the SmPC, or the need to send a DHPC were included in the study.
To determine the influence of the level of concern on the need to update the SmPC or to send a DHPC, we fitted generalised linear mixed-effects models (GLMMs) with a binomial distribution and a logit link function. In these models, the level of concern was included as a fixed effect and by-subject random intercepts and slopes for the level of concern were included as random effects. Results regarding the effect of the level of concern on the need to communicate are presented as the odds ratios of updating the SmPC or sending a DHPC for a 10 percentage-point increase in the level of concern and graphically by plotting the estimated population-level probabilities of updating the SmPC or sending a DHPC against the level of concern.
To assess the effects of the attributes of the safety issue, demographic and professional characteristics, and regulators’ attitudes on the level of concern, we fitted multiple linear mixed effects models. We began by fitting a crude model in which the attributes of the safety issue were included as the only fixed effects and by-subject random intercepts and slopes for the attributes of the safety issue were included as the random effects. Subsequently, we fitted separate follow-up models in which, while maintaining the fixed and random effects of the crude model, we added the other determinants (i.e., demographic and professional characteristics and regulators’ attitudes) one by one as fixed effects. We tested for the determinants’ main effect as well as all possible two-way interactions between each determinant and the attributes of the safety issue. We performed backward elimination to stepwise remove all non-significant interaction terms. Results are presented as estimated marginal means (also known as least-squares means), which reflect the predicted outcome for each level of a factor averaged over all possible combinations of the levels of the other factors in the model. They were unstratified for the crude model and stratified by demographic and professional characteristics, and by regulators’ attitudes for the follow-up models. The groups of categorical variables were pre-defined by definition (e.g., women vs. men), and groups of the continuous variables were created using the score of the variable at the 25th, 50th and 75th percentiles. Further details regarding the estimated marginal means as well as the regression coefficients of each model are presented in ESM 2.
Because the sampling scheme per country could have resulted in data clusters per country, we generated a multi-level model with observations grouped by individuals nested in country. These results showed no indication of clusters (ESM 2); therefore, no adjustments for the sampling scheme were made in the statistical analysis.
The analyses were performed in R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria; URL https://www.R-project.org/) with the packages lme4, emmeans and lmerTest [32 –34 ]. Statistical significance was indicated by p-values less than 0.05. Figures were generated in R and Microsoft Excel^® 2010 (Microsoft Corp., Redmond, WA, USA).

Publication 2023

1,2-dihexadecyl-sn-glycero-3-phosphocholine factor A Safety Woman

DNA Methylation Modulation in Aged Rats

Aged rats received a subcutaneous injection of saline (0.9% NaCl) or methionine (200 mg/kg; Sigma-Aldrich, St. Louis, Missouri, USA) twice a day (12-h interval) for 30 days. This dosage was previously shown to induce global DNA hypermethylation [23 (link)]. For the RG108 experiment, bilaterally cannulated young rats (ACSF-SHAM: n=12; ACSF-DBS: n=12; RG108-SHAM: n=14; RG108-DBS: n=12) were infused with either RG108 (Axon Medchem) or artificial cerebral spinal fluid (ACSF) for 30 days at a dosage previously shown to reduce DNA methylation [24 (link)]. RG108 in 20% DMSO was diluted to 200 ng/µL in ACSF. Drugs (2 µL) were infused into the dHPC using a Hamilton syringe connected to the internal cannula through polyethylene tubing (Protech international, Texas, USA) at a rate of 0.5 µL/min over 4 min. The internal cannula was left inside the guide cannula for an additional 3 min to ensure proper diffusion of the infused drug.

Poon C.H., Liu Y., Pak S., Zhao R.C., Aquili L., Tipoe G.L., Leung G.K., Chan Y.S., Yang S., Fung M.L., Wu E.X, & Lim L.W. (2023). Prelimbic Cortical Stimulation with L-methionine Enhances Cognition through Hippocampal DNA Methylation and Neuroplasticity Mechanisms. Aging and Disease, 14(1), 112-135.

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Publication 2023

1,2-dihexadecyl-sn-glycero-3-phosphocholine Axon Cannula Cerebrospinal Fluid Diffusion DNA Methylation Methionine Normal Saline Pharmaceutical Preparations Polyethylene Rattus RG108 Saline Solution Subcutaneous Injections Sulfoxide, Dimethyl Syringes

Quantification of Gene Expression in Aged Hippocampus

This assay was performed as previously described [6 (link), 8 (link), 29 (link), 30 (link)]. Animals were decapitated on day 30 after receiving 1-h electrical stimulation. The dHPC was dissected and total RNA was extracted using TRIzol reagent (Cat# TR118, Molecular Research Center Inc., Ohio, USA). RNA concentration and purity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA) through calculating the ratio of optical density at wavelengths between 260nm and 280nm. 7µl of RNA (600-800 ng in total) was used for cDNA synthesis using a PrimeScript™ RT reagent kit with gDNA eraser (Cat# RR047B, Takara Bio USA, California, USA) according to the instructions from manufacturer. Briefly, the RNA mixture was incubated with gDNA eraser and eraser buffer at 42°C for 2 min, followed by incubation in reverse transcription buffer, enzyme and primer mix at 37°C for 15 min, and finally with incubation at 85°C for 5 s to produce the final cDNA synthesis mixture. Efficiency of primers was validated using the diluted cDNA series measured between 90-110%. Real-time PCR was performed on a Roche LightCycler 480 II (Roche Diagnostics). Relative gene expressions were analyzed using the 2^-DDCT method relative to the expression of SAL-SHAM group (for aged animal experiments) or ACSF-SHAM group (for young animal experiments) [29 (link), 30 (link)] (Table 1).

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Publication 2023

1,2-dihexadecyl-sn-glycero-3-phosphocholine Anabolism Animals Biological Assay Buffers Diagnosis DNA, Complementary Enzymes Gene Expression Oligonucleotide Primers Real-Time Polymerase Chain Reaction Reverse Transcription Stimulations, Electric trizol Vision

Antibody-Mediated Capture of GFP-Tagged Proteins

TRAP was performed as previously described (Doyle et al., 2008 (link); Heiman et al., 2008 (link)). For each reaction, 300 μl of Streptavidin MyOne T1 Dynabeads (Invitrogen #65601) were washed 5x with 1x PBS and incubated with 120 μg biotinylated protein L (Pierce #29997, reconstituted at 1 μg/μl in PBS) in 1x PBS in a total volume of 1 ml for 35 minutes at RT, using end-over-end rotation. The beads were then washed 5 times with 3% IgG, Protease-free BSA (JacksonImmuno #001-000-162) in 1x PBS and subsequently incubated with 50 μg each of 19C8 and 19F7 anti-GFP monoclonal antibodies (Memorial Sloan-Kettering Monoclonal Antibody Facility) in 500 μl of 0.15 M KCl TRAP Wash Buffer (10 mM HEPES-KOH pH 7.4, 5 mM MgCl₂, 150 mM KCl, and 1% NP-40, supplemented with 100 μg/ml cycloheximide (Millipore Sigma #C7698-1g) in methanol, 0.5 mM DTT (Thermo Fisher Scientific #R0861), and 20 U/ml RNasin (Fisher Scientific #PR-N2515) just before use), for 30 min using end-over-end rotation. After antibody binding, the beads were washed 3 times with 0.15 M KCl TRAP Wash Buffer, resuspended in 0.15 M KCl TRAP Wash Buffer, and each reaction was supplemented with 30 mM DHPC (Avanti #850306P).

Mätlik K., Govek E.E., Paul M.R., Allis C.D, & Hatten M.E. (2023). Histone bivalency regulates the timing of cerebellar granule cell development. bioRxiv.

Publication Preprint 2023

1,2-dihexadecyl-sn-glycero-3-phosphocholine Anti-Antibodies Buffers Cycloheximide HEPES Immunoglobulins Magnesium Chloride Methanol Monoclonal Antibodies Nonidet P-40 Peptide Hydrolases Proteins Streptavidin

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1 2 dimyristoyl sn glycero 3 phosphocholine by Avanti Polar Lipids

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1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) is a phospholipid compound used in various laboratory applications. It is a synthetic lipid that can be utilized as a surfactant or a structural component in model membrane systems. DHPC has a defined chemical structure and physical properties that make it suitable for specific experimental purposes.

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What is the purpose of 1,2-dihexadecyl-sn-glycero-3-phosphocholine?

What are the key features of 1,2-dihexadecyl-sn-glycero-3-phosphocholine?

How can researchers optimize their work with 1,2-dihexadecyl-sn-glycero-3-phosphocholine?

Researchers can optimize their work with 1,2-dihexadecyl-sn-glycero-3-phosphocholine by using PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy. PubCompare.ai allows you to screen protocol literature more efficiently, leverage AI to pinpoint critical insights, and identify the most effective protocols related to 1,2-dihexadecyl-sn-glycero-3-phosphocholine for your specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibiltiy and accuracy.

Are there any common usage challenges with 1,2-dihexadecyl-sn-glycero-3-phosphocholine?

One potential challenge with using 1,2-dihexadecyl-sn-glycero-3-phosphocholine is ensuring consistent and reproducible results, as the properties of the lipid can be sensitive to factors like preparation methods and experimental conditions. By using PubCompare.ai to identify the most effective protocols, researchers can overcome this challenge and improve the reliabiltiy of their findings.

More about "1,2-dihexadecyl-sn-glycero-3-phosphocholine"

1,2-dihexadecyl-sn-glycero-3-phosphocholine, also known as DHPC or dipalmitoylphosphatidylcholine (DPPC), is a synthetic glycerophosphocholine lipid commonly used as a model compound for investigating membrane structure and function.
It is composed of two long-chain hexadecyl (cetyl) fatty acid chains esterified to the sn-1 and sn-2 positions of a glycerol backbone, with a phosphocholine head group at the sn-3 position.
This lipid is useful for recreating the physicochemical properties of biological membranes in experimental systems, such as liposomes and bilayer mimetics.
Research on DHPC can be optimized using PubCompare.ai, an AI-driven platform that enhances reproducibility and accuracy by helping researchers easily locate protocols from literature, pre-prints, and patents, and using AI-driven comparisons to identify the best protocols and products for their experiments.
PubCompare.ai is a valuable tool for researchers working with other related lipids, such as 1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC).
To further enhance your DHPC research, consider using other related compounds, such as ammonium acetate, 1,2-diheptanoyl-sn-glycero-3-phosphocholine (DHPC), HPLC-grade pyridine, 06:0 PC (DHPC), phenylisothiocyanate (PITC), and Dynabeads MyOne Streptavidin T1.
These compounds can be useful for purification, analysis, and other experimental techniques.
By leveraging the insights and tools provided by PubCompare.ai, you can optimize your DHPC research and ensure the highest levels of reproducibility and accuracy.