All of the primary sequence files are deposited in CGHub (
Methylation
This chemical modification can regulate gene expression, alter protein function, and influence various cellular processes.
Methylation plays a crucial role in biological systems, including epigenetic regulation, cellular signaling, and disease pathogenesis.
Researchers studying methylation can utilize PubCompare.ai to optimize their research process by locating the most relevant protocols from literature, pre-prints, and patents using AI-driven comparisons.
This can enhance reproducibility and accuracy by identifying the best protocols and products, while simplifying the research process with PubCompare.ai's intuitive tools and data-driven insihgts.
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Most cited protocols related to «Methylation»
All of the primary sequence files are deposited in CGHub (
Most recents protocols related to «Methylation»
Example 8
Characterization of Absorption, Distribution, Metabolism, and Excretion of Oral [14C]Vorasidenib with Concomitant Intravenous Microdose Administration of [13C315N3]Vorasidenib in Humans
Metabolite profiling and identification of vorasidenib (AG-881) was performed in plasma, urine, and fecal samples collected from five healthy subjects after a single 50-mg (100 μCi) oral dose of [14C]AG-881 and concomitant intravenous microdose of [13C3 15N3]AG-881.
Plasma samples collected at selected time points from 0 through 336 hour postdose were pooled across subjects to generate 0—to 72 and 96-336-hour area under the concentration-time curve (AUC)-representative samples. Urine and feces samples were pooled by subject to generate individual urine and fecal pools. Plasma, urine, and feces samples were extracted, as appropriate, the extracts were profiled using high performance liquid chromatography (HPLC), and metabolites were identified by liquid chromatography-mass spectrometry (LC-MS and/or LC-MS/MS) analysis and by comparison of retention time with reference standards, when available.
Due to low radioactivity in samples, plasma metabolite profiling was performed by using accelerator mass spectrometry (AMS). In plasma, AG-881 was accounted for 66.24 and 29.47% of the total radioactivity in the pooled AUC0-72 h and AUC96-336 h plasma, respectively. The most abundant radioactive peak (P7; M458) represented 0.10 and 43.92% of total radioactivity for pooled AUC0-72 and AUC96-336 h plasma, respectively. All other radioactive peaks accounted for less than 6% of the total plasma radioactivity and were not identified.
The majority of the radioactivity recovered in feces was associated with unchanged AG-881 (55.5% of the dose), while no AG-881 was detected in urine. In comparison, metabolites in excreta accounted for approximately 18% of dose in feces and for approximately 4% of dose in urine. M515, M460-1, M499, M516/M460-2, and M472/M476 were the most abundant metabolites in feces, and each accounted for approximately 2 to 5% of the radioactive dose, while M266 was the most abundant metabolite identified in urine and accounted for a mean of 2.54% of the dose. The remaining radioactive components in urine and feces each accounted for <1% of the dose.
Overall, the data presented indicate [14C]AG-881 underwent moderate metabolism after a single oral dose of 50-mg (100 μCi) and was eliminated in humans via a combination of metabolism and excretion of unchanged parent. AG-881 metabolism involved the oxidation and conjugation with glutathione (GSH) by displacement of the chlorine at the chloropyridine moiety. Subsequent biotransformation of GSH intermediates resulted in elimination of both glutamic acid and glycine to form the cysteinyl conjugates (M515 and M499). The cysteinyl conjugates were further converted by a series of biotransformation reactions such as oxidation, S-dealkylation, S-methylation, S-oxidation, S-acetylation and N-dealkylation resulting in the formation multiple metabolites.
A summary of the metabolites observed is included in Table 2
Example 7
5′-capping of polynucleotides may be completed concomitantly during the in vitro-transcription reaction using the following chemical RNA cap analogs to generate the 5′-guanosine cap structure according to manufacturer protocols: 3′-O-Me-m7G(5′)ppp(5′) G [the ARCA cap]; G(5′)ppp(5′)A; G(5′)ppp(5′)G; m7G(5′)ppp(5′)A; m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). 5′-capping of modified RNA may be completed post-transcriptionally using a Vaccinia Virus Capping Enzyme to generate the “Cap 0” structure: m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). Cap 1 structure may be generated using both Vaccinia Virus Capping Enzyme and a 2′-O methyl-transferase to generate: m7G(5′)ppp(5′)G-2′-O-methyl. Cap 2 structure may be generated from the Cap 1 structure followed by the 2′-O-methylation of the 5′-antepenultimate nucleotide using a 2′-O methyl-transferase. Cap 3 structure may be generated from the Cap 2 structure followed by the 2′-O-methylation of the 5′-preantepenultimate nucleotide using a 2′-O methyl-transferase. Enzymes are preferably derived from a recombinant source.
When transfected into mammalian cells, the modified mRNAs have a stability of between 12-18 hours or more than 18 hours, e.g., 24, 36, 48, 60, 72 or greater than 72 hours.
Example 6
5′-capping of polynucleotides can be completed concomitantly during the in vitro-transcription reaction using the following chemical RNA cap analogs to generate the 5′-guanosine cap structure according to manufacturer protocols: 3′-O-Me-m7G(5′)ppp(5′) G [the ARCA cap];G(5′)ppp(5′)A; G(5′)ppp(5′)G; m7G(5′)ppp(5′)A; m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). 5′-capping of modified RNA can be completed post-transcriptionally using a Vaccinia Virus Capping Enzyme to generate the “Cap 0” structure: m7G(5′)ppp(5′)G (New England BioLabs, Ipswich, MA). Cap 1 structure can be generated using both Vaccinia Virus Capping Enzyme and a 2′-O methyl-transferase to generate: m7G(5′)ppp(5′)G-2′-O-methyl. Cap 2 structure can be generated from the Cap 1 structure followed by the 2′-O-methylation of the 5′-antepenultimate nucleotide using a 2′-O methyl-transferase. Cap 3 structure can be generated from the Cap 2 structure followed by the 2′-O-methylation of the 5′-preantepenultimate nucleotide using a 2′-O methyl-transferase. Enzymes can be derived from a recombinant source.
When transfected into mammalian cells, the modified mRNAs can have a stability of between 12-18 hours or more than 18 hours, e.g., 24, 36, 48, 60, 72 or greater than 72 hours.
In all melanoma cell lines, for each modality (promoter hypomethylation, promoter hypermethylation, gene amplification, and gene deletion) and for each gene, we built an ANOVA model using TF targeting as the response variable across all melanoma cell lines while the status of that gene (either promoter methylation or copy number status) was the explanatory variable. For example, in modeling promoter hypermethylation, we chose positive instances to represent hypermethylated promoters and negative instances for nonmethylated promoters along with an additional factor correcting for the cell lineage. Similarly, for copy number variation analysis, we chose positive instance to represent amplified genes and negative instances for nonamplified genes while correcting for cell lineage. We only computed the associations if they had at least three positive instances of the explanatory variable (for example, promoter hypomethylation in at least three cell lines).
To predict drug response using TF targeting, we conducted a linear regression with elastic net [45 (link)] regularization as implemented in the Python package sklearn v1.1.3 using an equal weight of 0.5 for L1 and L2 penalties using regorafenib cell viability assays in melanoma cell lines as a response variable and the targeting scores of 1,132 TFs (Table S
Finally, to model EMT in melanoma, we used MONSTER on two LIONESS networks of melanoma cancer cell lines, one representing a primary tumor (Depmap ID: ACH-000580) as the initial state and the other a metastasis cell line (Depmap ID: ACH-001569) as the end state. We modified the original implementation of MONSTER that implements its own network reconstruction procedure to take any input network, such as LIONESS networks. MONSTER identifies differentially involved TFs in the transition by shuffling the columns of the initial and final state adjacency matrices 1000 times to build a null distribution, which is then used to compute a standardized differential TF involvement score by scaling the obtained scores by those of the null distribution.
Top products related to «Methylation»
More about "Methylation"
This chemical modification can regulate gene expression, alter protein function, and influence various cellular processes.
Methylation is integral to epigenetic regulation, cellular signaling, and disease pathogenesis.
Researchers studying methylation can leverage powerful tools like the EZ DNA Methylation Kit, EZ DNA Methylation-Gold Kit, and EpiTect Bisulfite Kit to analyze DNA methylation patterns.
The Infinium HumanMethylation450 BeadChip and Infinium MethylationEPIC BeadChip provide comprehensive genome-wide methylation analysis, while the QIAamp DNA Mini Kit and DNeasy Blood & Tissue Kit enable efficient DNA extraction and purification.
Downstream techniques such as PyroMark Q24 and the PyroMark PCR Kit can be used to quantify and analyze methylation levels.
These advanced tools and kits, when combined with the AI-powered insights of PubCompare.ai, can help researchers optimize their methylation research process, enhance reproducibility and accuracy, and simplify their workflow.
PubCompare.ai empowers researchers to locate the most relevant protocols from literature, preprints, and patents using AI-driven comparisons.
This can lead to the identification of the best protocols and products, ultimately improving the quality and efficiency of methylation research.
Experince the power of AI-assisted methylation research optimization today and take your research to new heights.