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A-301

A-301 is a chemical compound of interest in various research fields, including pharmacology and biochemistry.
It is a synthetic derivative with potential applications in the development of new therapeutic agents.
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Most cited protocols related to «A-301»

1
ChIP-Seq Profiling of Transcriptional Regulators
Cited 55 times

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Publication 2013
A-301 Antibodies BRD4 protein, human CDK9 protein, human Chromatin Immunoprecipitation Sequencing DNA Chips histone H3 trimethyl Lys4 MED1 protein, human PLAGL1 protein, human RNA Polymerase II
2
Flexible Active Site Modeling of GrsA-PheA
Cited 20 times
Our structural model is the same as the one used in the original K* [25 (link)]. In our experiments, the structural model consists of nine active site residues (D235, A236, W239, T278, I299, A301, A322, I330, C331) of GrsA-PheA (PDB id: 1AMU) [5 (link)], a steric shell (30 residues with at least one atom within 8 Å from the substrate), the amino acid substrate, and the AMP cofactor. The steric shell facilitates the computation of the energy between the active site residues and neighboring regions of the protein (the residue-to-template energy) and constrains the movement of the active site residues to only sterically-allowable conformations relative to the body of the GrsA-PheA protein. All nine active site residues are modeled as flexible using rotamers and are subject to energy minimization. The steric shell includes residues 186Y, 188I, 190T, 210L, 213F, 214F, 230A, 234F, 237S, 238V, 240E, 243M, 279L, 300T, 302G, 303S, 320I, 321N, 323Y, 324G, 325P, 326T, 327E, 328T, 329T, 332A, 333T, 334T, 515N, and 517K. In 1AMU [5 (link)], and also in [25 (link)], residues 235D and 517K make H-bonds to the amino acid backbone of the ligand, thereby stabilizing the substrate in a productive orientation for catalysis. Flexible residues are represented by rotamers from the Richardsons’ rotamer library [28 (link)]. The energy function consists of the AMBER electrostatic, vdW, and dihedral energy terms [44 , 6 ], and the EEF1 pairwise solvation energy term [23 ]. A dielectric of 20 and a solvation energy scaling factor of 0.05 was used for the computational experiments. Each rotameric-based conformation is minimized using steepest-descent minimization (see Appendix C).
Georgiev I., Lilien R.H, & Donald B.R. (2008). The Minimized Dead-End Elimination Criterion and Its Application to Protein Redesign in a Hybrid Scoring and Search Algorithm for Computing Partition Functions over Molecular Ensembles. Journal of Computational Chemistry, 29(10), 1527-1542.
Publication 2008
1-naphthol-8-amino-3,6-disulfonic acid A-301 A-factor (Streptomyces) Amber Amino Acids Catalysis cDNA Library Electrostatics factor A Human Body Ligands Movement Peptide Elongation Factor 1 poly(2-hydroxyethyl acrylate) Protein Domain Proteins STEEP1 protein, human Vertebral Column
3
Evaluating Logistic Regression Models in Clustered Data
Cited 13 times
We generated a source population which included 100 centers. The number of patients per center was Poisson distributed, with a mean and variance varying per center according to the exponential function of a normal distribution (N(5.7, 0.3)). This resulted in a total of 30,556 patients and a median of 301 patients per center (range 155–552). The dichotomous outcome Y was predicted with 3 continuous (X1-X3) and 3 dichotomous variables (X4-X6). The three continuous predictors were independently drawn from a normal distribution, with a mean of 0 and standard deviations of 0.2, 0.4, and 1. The three dichotomous predictors were independently drawn from binomial distributions with incidences 0.2, 0.3, and 0.4. The regression coefficients of all predictors were 1. To introduce clustering of events, we generated a latent random effect from a normal distribution with mean 0 and variance 0.17. This corresponded to an intraclass correlation coefficient (ICC) of 5%, which was calculated as σ2u0/(σ2u0 + ((π ^ 2)/3). The σ2u0 equals the second level variance estimated with a random intercept logistic regression model [6 ]. Based on the six predictors and the latent random effect, the linear predictor lp was calculated for each patient. The linear predictor was normally distributed with mean −1.06 and standard deviation 1.41. The linear predictor was transformed to probabilities for the outcome using the formula P(Y) = 1/(1 + exp(−lp)). The outcome value Y (1 or 0) was then generated by comparing P(Y) with an independently generated variable u having a uniform distribution from 0 to 1. We used the rule Y = 1 if P(Y) ≤ u, and Y = 0 otherwise. The incidence of the outcome (P(Y = 1)) was 30% in all source populations, except for the situation with low number of events (incidence = 3%). Further, we varied several parameters in the source population as described above. We studied ICC values of 5%, 15% and 30%; Pearson correlation coefficient values between predictor X1 and the random intercept were 0.0 or 0.4.
Study samples were drawn according to the practice of data collection in a multicenter setting [20 (link),21 (link)]. We randomly drew study samples from the source population in two stages. First we sampled 20 centers, and then we sampled in total 1000 patients from the included centers (two-stage sampling). We also studied the performance in study samples with 5 or 50 centers (including respectively 100 and 1000 patients in total). Standard and random intercept logistic regression models were fitted in the study sample, and evaluated in that study sample (apparent performance) and in the whole source population (test performance). The whole process (sampling from source population, model development and evaluation) was repeated 100 times.
Calculations were performed with R version 2.11.1 [22 ]. We used the lmer function from the lme4 library to perform mixed effect regression analyses [23 ]. The lrm function of the Design package was used to fit the standard model and estimate overall performance measures [24 ].
Bouwmeester W., Twisk J.W., Kappen T.H., van Klei W.A., Moons K.G, & Vergouwe Y. (2013). Prediction models for clustered data: comparison of a random intercept and standard regression model. BMC Medical Research Methodology, 13, 19.
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Publication 2013
A-301 cDNA Library Patients
4
Ultrastructural Analysis of Wistar Rat Myocardium
Cited 11 times
Tissue samples from Wistar rat myocardium were obtained and processed for ultrastructural investigation as previously described [28 (link)].
Ten Wistar rats, having a body weight of 200–250 g, with free access to food and water, maintained in a temperature-controlled facility with a 12-hrs light/dark cycle were used for this study. All animal experiments have been carried out in accordance with the ethical Guidelines for Animal Experimentation and the study was approved by the Bioethics Committee of ‘Carol Davila’ University of Medicine Bucharest.
Ventricular and atrial myocardium was harvested under anaesthesia after perfusion-fixation (1.5% buffered glutaraldehyde) followed by immersion in 4% buffered glutaraldehyde. Tissue samples were cut into 1 mm three small fragments and fixed for 4 hrs in 4% glutaraldehyde in 0.1M cacodylate buffer, pH 7.4 at 4'B0C. The fragments were post-fixed for 1 hr in buffered 1% OsO4, dehydrated in an ethanol series and then processed for Epon 812 embedding at 60'B0C for 48 hrs.
One-micron-thick sections stained with 1% toluidine blue were examined for a precise orientation of the subsequent thin sections. The ultrathin sections were cut using an LKB ultramicrotome with a diamond knife and double stained with 1% uranyl acetate and Reynolds lead citrate.
Electron microscopy examination was performed with both a Philips CM 12 and a Philips 301 transmission electron microscope at 60 kV. The images were recorded with Morada 11 megapixel CCD camera and analysed with iTEM SYS software. Data are expressed as mean ‘B1 SD. Digitally colour images were obtained using Adobe Photoshop software.
Mandache E., Popescu L, & Gherghiceanu M. (2007). Myocardial interstitial Cajal-like cells (ICLC) and their nanostructural relationships with intercalated discs: shed vesicles as intermediates. Journal of Cellular and Molecular Medicine, 11(5), 1175-1184.
Publication 2007
A-301 Anesthesia Body Weight Cacodylate Citrate Diamond Electron Microscopy Epon 812 Ethanol Food Glutaral Heart Atrium Heart Ventricle Microtomy Myocardium Perfusion Pharmaceutical Preparations Process Assessment, Health Care Rats, Wistar Submersion Tissues Tolonium Chloride Transmission Electron Microscopy Ultramicrotomy uranyl acetate
5
ChIP-seq profiling of transcription regulators
Cited 8 times
The ChIP assays for BRD2, BRD3, BRD4, AR, RNA PolII, ERG and H3K27ac were performed using HighCell ChIP kit (Diagenode) according to manufacturer's protocol. The antibodies used for ChIP assay are AR_PG-21 (Millipore Cat. # 06-680) ; RNA Pol II (Abcam Cat. # ab5408); BRD2 (Bethyl Cat. # A302-583A) ; BRD3 (Bethyl Cat. # A302-368A) ; BRD4 (Bethyl Cat. # A301-985A); H3 (acetyl K27) (Abcam Cat. # ab4729) and IgG (Diagenode). For BRD2/3/4 ChIP-seq experiments with BET inhibitors, VCaP cells were treated with 500 nM JQ1 or I-BET762 for 12hrs. For AR signaling ChIP-seq experiments, VCaP cells were grown in charcoal-stripped serum containing media for 48hrs. followed by 6hrs. pre-treatment with vehicle or 500nM JQ1 or 10μM MDV3100 or 25μM Bicalutamide and then stimulated with 10nM DHT for 12hrs. For ERG ChIP-seq studies, VCaP cells were treated with 500nM JQ1 or vehicle for 12hrs. Next, cells were cross-linked for 10 min. with 1% formaldehyde. Cross-linking was terminated by the addition of 1/10 volume 1.25M glycine for 5 min. at room temperature followed by cell lysis and sonication (Bioruptor, Diagenode), resulting in an average chromatin fragment size of 200bp. Chromatin equivalent to 5×106 cells were used for ChIP using various antibodies. ChIP DNA was isolated (IPure Kit, Diagenode) from samples by incubation with the antibody at 4°C overnight followed by wash and reversal of cross-linking. The ChIP-seq sample preparation for sequencing was performed according to the manufacturer's instructions (Illumina). ChIP-enriched DNA samples (1-10 ng) were converted to blunt-ended fragments using T4 DNA polymerase, E. coli DNA polymerase I large fragment (Klenow polymerase) and T4 polynuleotide kinase (New England BioLabs, NEB). A single A-base was added to fragment ends by Klenow fragment (3′ to 5′ exo minus; NEB) followed by ligation of Illumina adaptors (Quick ligase, NEB). The adaptor-modified DNA fragments were enriched by PCR using the Illumina Barcode primers and Phusion DNA polymerase (NEB). PCR products were size selected using 3% NuSieve agarose gels (Lonza) followed by gel extraction using QIAEX II reagents (QIAGEN). Libraries were quantified with the Bioanalyzer 2100 (Agilent) and sequenced on the Illumina HiSeq 2000 Sequencer (100 nucleotide read length).
Asangani I.A., Dommeti V.L., Wang X., Malik R., Cieslik M., Yang R., Escara-Wilke J., Wilder-Romans K., Dhanireddy S., Engelke C., Iyer M.K., Jing X., Wu Y.M., Cao X., Qin Z.S., Wang S., Feng F.Y, & Chinnaiyan A.M. (2014). Therapeutic Targeting of BET Bromodomain Proteins in Castration-Resistant Prostate Cancer. Nature, 510(7504), 278-282.
Publication 2014
A-301 Antibodies bicalutamide BRD4 protein, human Cells Charcoal CHOP protocol Chromatin DNA-Directed DNA Polymerase DNA Chips DNA Polymerase I Escherichia coli Formaldehyde Glycine I-BET compound Immunoglobulins Immunoprecipitation, Chromatin inhibitors Ligase Ligation MDV 3100 Nucleotides NuSieve agarose Oligonucleotide Primers Phosphotransferases RNA Polymerase II Serum

Most recents protocols related to «A-301»

1
Ophthalmic Lens with Multifocal Optical Power
Not available on PMC !

Example 4

FIG. 8 illustrates an exemplary embodiment of the power profile of an optic zone for a lens. The example of FIG. 8 is directed to an ophthalmic lens comprising:

an optic zone comprising:

a primary area 301 having a primary optical power;

a central portion 311;

a first secondary area 302 within the central portion 311 having a first secondary optical power;

a first power transition area 304 having a first power transition from the primary area 301 to the first secondary area 302;

a peripheral portion 310;

a second secondary area 303 within the peripheral portion 310 having a second secondary optical power; and

a second power transition area 305 having a second power transition from the primary area 301 to the second secondary area 303;

wherein the primary optical power is selected according to a prescription for refractive correction, the first secondary optical power is more positive than the primary optical power and the second secondary optical power is more positive than the primary optical power;
wherein the first power transition comprises: at least a first step 306 in the first power transition area 304 in which the rate of change in power, from the first secondary optical power in the first secondary area 302 to the primary optical power in the primary area 301, changes at a first junction 313 between a first transition region 312 within the first power transition 304 and the first step 306 followed by a change in the rate of change in power at a second junction 314 between a second transition region 315 within the first power transition 304 and the first step 306, and
at least a second step 307 and a third step 308,
wherein the second step 307 lies within the second power transition area 305 in which the rate of change in power, from the second secondary optical power in the second secondary area 303 to the primary optical power in the primary area 301, changes at a third junction 318 between a third transition region 319 within the second power transition 305 and the second step 307 followed by a change in the rate of change in power at a fourth junction 317 between a fourth transition region 316 within the second power transition 305 and the second step 307, and the third step 308 lies within the second power transition area 305 in which the rate of change in power, from the second secondary optical power in the second secondary area 303 to the primary optical power in the primary area 301, changes at a fifth junction 321 between a fifth transition region 321 within the second power transition 305 and the third step 308 followed by a change in the rate of change in power at a sixth junction 320 between the third transition region 319 within the second power transition 305 and the third step 308.

In the exemplary embodiment of FIG. 8, the power of the primary area 301 is approximately −2 D and has a progression in optical power progressively increasing in positive power towards the periphery. Such peripheral progressive increase in power may result in effective or improved visual performance or vision performance in one or more aspects of visual performance or vision performance. For example, spherical aberration may be included in the primary area to correct, reduce or manipulate aberration of the eye and ophthalmic lens combined. Such an exemplary inclusion of spherical aberration may improve clarity of vision, contrast, contrast sensitivity, visual acuity, and overall quality of vision or combinations thereof.

In certain embodiments, the power of a primary area may be constant, substantially constant, progressively increasing, progressively decreasing, modulated (i.e. undulating along its power profile), possess an aberration profile (e.g. spherical aberration) or combinations thereof.

In the exemplary embodiment of FIG. 8, the powers of the first step 306, second step 307 and third step 308 are not constant within the steps.

In certain embodiments, the power profile within a step may be constant, or substantially constant, or progressively changing. In certain embodiments in which the power of a step is progressively changing, the change in power across the width of the step may be between 0 and 0.2 D, 0 and 0.15 D or 0 and 0.1 D. In certain embodiments in which two or more steps have progressively changing power profiles, the rate of change of the power profiles between the two or more steps may be equal or unequal.

In the exemplary embodiment of FIG. 8, the power profile along the first power transition 304 and the second power transition 305 are monotonic.

Monotonic means that where a power transition decreases from one area to another area (for example, between a first secondary area and a primary area), the power profile is either decreasing or constant or substantially decreasing or substantially constant along the power transition including steps within the power transition. Conversely, where a power transition increases from one area to another area (for example, from a primary area to a second secondary area), monotonic means the power profile is either increasing or constant or substantially increasing or substantially constant along the power transition including steps within the power transition. In certain embodiments, a power transition will have a monotonic power profile.

In the exemplary embodiment of FIG. 8, changes in the rate of change in optical power at junctions 313 and 314 that forms the first step 306 and changes in the rate of change in optical power at junctions 317 and 318 that forms the second step 307 are less rapid and/or more gradual.

In certain embodiments, a change in the rate of change in optical powers may be considered “gradual” when the change in rate of change occurs over a junction width of between 0.15 and 1 mm, 0.25 and 0.75 mm or 0.3 and 0.5 mm.

US11874529B2. Ophthalmic optical lens for vision correction having one or more areas of more positive power (2024-01-16). Brien Holden Vision Institute Limited [AU]. Inventors: Brien Anthony Holden [AU], Padmaja Rajagopal Sankaridurg [AU], Klaus Ehrmann [AU], Fabian Conrad [AU], Arthur Ho [AU].
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Patent 2024
A-301 Contrast Sensitivity Disease Progression Lens, Crystalline Ocular Refraction Visual Acuity
2
Assessing Oral Health Impacts of COVID-19
A questionnaire was constructed and validated through face and content validation process, furthermore, the reliability of the questionnaire was assessed through intra-class correlation and the internal consistency of items assessed was 0.92. The questionnaire was sent through email, and their responses were recorded. Forty-nine incomplete forms were excluded. A total of 301 completed questionnaires were received and included in the study.
The questionnaire consisted of two sections. The first included the demography of the patients including age, gender, occupation, level of education and smoking habits, systemic diseases, and finally if any medicines were being taken. The second section was focused on whether the patients experienced xerostomia symptoms or not, and how their quality of life regarding oral health was affected during active infection and after they recovered from it, using validated measurement tools. Xerostomia and the oral health impact of the coronavirus was measured using the Xerostomia Inventory Scale (XI) (Thomson et al., 1999 (link)) and Oral Health Impact Profile-14 (OHIP-14) scale (Slade, 1997 (link)), both being self-administered. Both the Xerostomia Inventory Scale (XI) and Oral Health Impact Profile-14 (OHIP-14) scale are adapted in the present study in accordance with there published license. The Xerostomia Inventory scale (XI) comprised of a total of 11 questions, with each question having five options as follows: 1 = Never, 2 = Hardly ever, 3 = Occasionally, 4 = Fairly often, and 5 = Very often. The Oral Health Impact Profile-14 scale comprised of a total of 14 questions related to seven dimensions: Functional limitation, physical pain, psychological discomfort, physical disability, psychological disability, social disability, and handicap. Each question in the OHIP-14 scale has five options as follows: 0 = Never, 1 = Rarely, 2 = Sometimes, 3 = Repeatedly, and 4 = Always. In both of the scales used, options in each question have its own numerical value which was added at the end to obtain a total score for each participant. For the Xerostomia Inventory scale (XI), the minimum score was 11 and the maximum was 55, with higher scores depicting greater severity in oral dryness. In the OHIP-14 scale, the minimum score was 0 with 56 being the maximum, and a score above 10 indicated poor Quality of Life (QoL).
The questionnaire was composed of two parts, the first part consisted of Xerostomia Inventory and Oral Health Impact Profile scales questions which the patients were asked to fill when they had active coronavirus infection. The second part of the questionnaire also had similar questions of the Xerostomia Inventory and Oral Health Impact Profile scales but these were addressing symptoms after recovery from coronavirus infection. The total score for each of the two scales in both parts of the questionnaire was added up. The total scores of OHIP and XI scales during active coronavirus infection were compared with scores of OHIP and XI scales when participants had recovered from it.
Saleem M.K., Lal A., Ahmed N., Abbasi M.S., Vohra F, & Abduljabbar T. (2023). Oral health related quality of life and the prevalence of ageusia and xerostomia in active and recovered COVID-19 Patients. PeerJ, 11, e14860.
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Publication 2023
A-301 Coronavirus Coronavirus Infections Disabled Persons Face Gender Infection Pain Patients Pharmaceutical Preparations Physical Examination Xerostomia
3
Western Blot Analysis of Oncogenic Signaling Pathways
The protein expression level of ERK, phosphorylated (p)-ERK, MEK, p-MEK, AKT, p-AKT-Ser473, p-AKT-Thr308, mTOR and p70-S6k was measured using western blot analysis in the MDA-MB-231, SUM149 and SUM159 cell lines, which were each treated with the IC50 of GO. All cells were lysed in RIPA lysis buffer (cat. no. P0013B; Beyotime Institute of Biotechnology) and then centrifuged at 15,702 x g for 15 min at 4˚C. Protein concentrations were determined using a BCA kit (Beyotime Institute of Biotechnology). A total of 20 µg protein was separated on 6-10% gels using SDS-PAGE and transferred to PVDF membranes (MilliporeSigma). The membranes were blocked for 1 h at 26˚C with 5% bovine serum albumin containing 0.1% Tween-20. Immunoblotting was performed using the following primary antibodies: ERK (cat. no. 13-6200, 1:1,000), p-ERK (cat. no. 44-680G; 1:500), MEK (cat. no. PA5-116802; 1:500), p-MEK (cat. no. 44-452, 1:1,000), AKT (cat. no. MA191204; 1:1,000), mTOR (cat. no. A301-144A-T; 1:1,000), p70-S6k (cat. no. MA5-36267; 1:1,000) (all Invitrogen; Thermo Fisher Scientific, Inc.) and Tubulin (cat. no. AF1216; 1:1,000; Beyotime Institute of Biotechnology) overnight at 4˚C. The membranes were then washed with 1% TBS-Tween-20 three times and incubated with the corresponding secondary antibodies (cat. no. A0208; goat anti-rabbit; 1:5,000; Beyotime Institute of Biotechnology) at 37˚C for 2 h. The membranes were washed again with TBS, and the proteins were visualized using an enhanced chemiluminescence assay kit (Beyotime Institute of Biotechnology). Images were captured using a Bio-Rad Chemodoc XRS+ system and the Image-lab software (Version 6.0; Bio-Rad Laboratories, Inc.). The test was repeated three times.
Rong P., Yanchu L., Nianchun G., Qi L, & Xianyong L. (2023). Glyoxal-induced disruption of tumor cell progression in breast cancer. Molecular and Clinical Oncology, 18(4), 26.
Publication 2023
A-301 Antibodies Buffers Cell Lines Cells Chemiluminescent Assays FRAP1 protein, human Gels Goat polyvinylidene fluoride Proteins Rabbits Radioimmunoprecipitation Assay Ribosomal Protein S6 Kinases, 70-kDa SDS-PAGE Serum Albumin, Bovine Staphylococcal Protein A Tissue, Membrane Tubulin Tween 20 Western Blot
4
Immunoprecipitation and Western Blotting of Proteins
Human embryonic kidney 293T cells (CRL-3216; American Type Culture Collection) were cultured in poly-L-lysine coated 6-cm plates (46 (link)). Upon reaching 25% confluency, cells were transiently transfected by the calcium phosphate coprecipitation method (47 (link)). Two days after transfection, cell extracts were prepared (48 (link)) and immunoprecipitations performed as previously described (49 (link)). Precipitated proteins were run on SDS polyacrylamide gels (50 (link)), transferred to polyvinylidene fluoride membrane (51 (link)), and then challenged with indicated antibodies (52 (link)). After incubation with corresponding secondary antibodies coupled to horseradish peroxidase (53 ), signals were revealed by chemiluminescence (54 (link)) and exposure to film (55 (link)). Likewise, endogenous proteins were coimmunoprecipitated from human HCT116 colorectal cancer cells, utilizing control rabbit IgG (Santa Cruz, sc-2027) or rabbit polyclonal anti-JMJD1A antibodies (Bethyl, A301-538A) for immunoprecipitation followed by Western blotting with anti-JMJD2A antibodies (Bethyl, A300-861A).
Sui Y., Jiang H., Kellogg C.M., Oh S, & Janknecht R. (2023). Promotion of colorectal cancer by transcription factor BHLHE40 involves upregulation of ADAM19 and KLF7. Frontiers in Oncology, 13, 1122238.
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Publication 2023
A-301 Anti-Antibodies Antibodies Calcium Phosphates Cell Extracts Cells Chemiluminescence Colorectal Carcinoma Embryo HCT116 Cells HEK293 Cells Homo sapiens Horseradish Peroxidase Immunoprecipitation Kidney Lysine Poly A polyacrylamide gels polyvinylidene fluoride Proteins Rabbits Tissue, Membrane Transfection
5
Overexpression of EHMT1 and EHMT2 in PEO1 Cells
PEO1 cells were maintained in RPMI+ 10% heat inactivated FBS (Avantor, Cat. #10803-034) + 100 μg/mL Penicillin/Streptomycin (Lonza, Cat. #09-757F). EHMT1 or EHMT2 overexpression cells were obtained by using lentivirus to infect cells with either TRE_EHMT1_hygromycin or TRE_EHMT2_hygromycin. To produce lentivirus, 500,000 Lenti-X 293T cells were seeded on 6-well plate. Twenty-four hours later, the cells were transfected with packaging plasmids (0.5μg pCMV-VSV-G, 1.1μg pD8.9), 0.85μg transfer plasmid, 7.35μg of PEI MAX (Polysciences Inc., Cat. #24765-1) and 10mM HEPES. Media was changed 24 hours later. Supernatant with virus was collected 48 and 72 hours post transfection. The supernatant was filtered using a 0.45μm PES Filter membrane (Whatman Uniflo, Cat. #9914-2504). Cells were then infected using 1mL of supernatant with 5μg/mL polybrene (EMD Millipore, Cat. #TR-1003-G). Media was changed 24 hours later. 48 hours post transduction, cells were selected using 200μg/ml Hygromycin B (Biosciences, Cat. #31282-04-9). Cells were selected until death of non-transduced cells. Once cells were selected, they were induced for EHMT1 or EHMT2 expression using Doxycycline (1μg/ml, TCI, Cat. #D4116) for four days and collected. Cells were immunoblotted for EHMT1 (Bethyl Laboratories, Cat. #A301-642A; dilution 1:500), EHMT2 (Cell Signaling, Cat. #3306; RRID:AB_2097647; dilution 1:1000), alpha-tubulin (Cell Signaling Cat. #3873; RRID:AB_1904178; dilution 1:3000), and H3K9me2 (Cell Signaling Cat. #4658; RRID:AB_10544405; diluted 1:1000).
Nguyen L.L., Watson Z.L., Ortega R., Woodruff E.R., Jordan K.R., Iwanaga R., Yamamoto T.M., Bailey C.A., Jeong A.D., Guntupalli S.R., Behbakht K., Gbaja V., Arnoult N., Chuong E.B, & Bitler B.G. (2023). Combinatory EHMT and PARP inhibition induces an interferon response and a CD8 T cell-dependent tumor regression in PARP inhibitor-resistant models. bioRxiv.
Publication Preprint 2023
A-301 alpha-Tubulin Cell Death Cells Doxycycline GIT1 protein, human HEK293 Cells HEPES hygromycin A Hygromycin B Lentivirus Penicillins Plasmids Polybrene Streptomycin Technique, Dilution Tissue, Membrane Transfection Virus

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More about "A-301"

A-301 is a versatile chemical compound with promising applications in various research fields, including pharmacology, biochemistry, and drug development.
This synthetic derivative has garnered significant attention due to its potential to serve as a building block for novel therapeutic agents.
Researchers delving into the study of A-301 can leverage PubCompare.ai, an innovative AI-powered platform, to optimize their research protocols.
This platform provides researchers with access to a vast repository of protocols from literature, preprints, and patents, while utilizing advanced AI-driven comparisons to identify the most effective protocols and products.
By streamlining the research process, PubCompare.ai empowers researchers to unlock new insights and accelerate their A-301 studies more efficiently.
This tool can be particularly useful for researchers working with related biomolecules, such as FLAG, β-actin, Anti-β-actin, Anti-FLAG, GAPDH, α-tubulin, Anti-FLAG M2, and Anti-BRD4, as it can help identify the best protocols and techniques for their specific research needs.
With the power of PubCompare.ai, researchers can navigate the complex landscape of A-301 research with ease, leveraging the platform's advanced AI capabilities to optimize their workflows and drive their studies forward more effectively.
Whether you're exploring the pharmacological properties of A-301 or investigating its biochemical applications, PubCompare.ai is an invaluable resource that can help you unlock new discoveries and push the boundaries of scientific understanding.