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IDH1 and IDH2 Mutations as Novel Therapeutic Targets in Acute Myeloid Leukemia (AML): Current Perspectives
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2020
Angelo Paci, Mael Heiblig, Christophe Willekens, Sophie Broutin, Mehdi Touat, Virginie Penard-Lacronique, Stéphane de Bottona
AG-221 (enasidenib) is a specific slow tight binder of the IDH2 R140Q-mutant enzyme (Fig. 5.2). It targets selectively mutant/wild-type heterodimers and mutant homodimers over IDH2-wild-type homodimers, IDH1-wild-type homodimers and IDH1 R132H-mutant enzymes (Yen et al., 2017). AG-221 allosterically stabilizes the open homodimer conformation, preventing the conformational change required for catalysis. Importantly, AG-221 displayed higher potency against R140Q versus R172K (Yen et al., 2017). Therefore, AG-221 induced 99% reduction in intracellular 2HG in R140Q at low concentrations (1 μM), whereas R172K required high concentrations (5 μM) to achieve 99% reduction relative to vehicle-treated controls (Yen et al., 2017). AG-221 induced cellular differentiation in primary human IDH2-mutant AML cells treated ex vivo26 and in patient-derived xenograft (PDX) mouse models (Kats et al., 2017; Yen et al., 2017). Furthermore, these differentiated cells were functional; IDH2 R140Q-mutant neutrophils obtained from primary IDH2 R140Q-mutant blasts treated ex vivo with enasidenib demonstrated intact phagocytic activity, and exhibited granules colocalizing with lactoferrin, a canonical marker of secondary and tertiary granules of mature neutrophils (Yen et al., 2017; Amatangelo et al., 2017). AG-221 also provided a statistically significant survival benefit in an aggressive IDH2 R140Q-mutant AML xenograft mouse model (Yen et al., 2017).
Huntington’s Disease and Stem Cells
Published in Deepak A. Lamba, Patient-Specific Stem Cells, 2017
Karen Ring, Robert O’Brien, Ningzhe Zhang, Lisa M. Ellerby
One important element in these approaches is the development of a method for replacing the mHTT allele with the wild-type allele. In genetic studies, a corrected cell line allows for comparison of HD cells with an isogenic control background, ensuring that differences are due to the mHTT mutation rather than unrelated genetic differences between control and case cell lines. In the case of transplantation, it is generally considered important to replace lost tissue with cells that do not contain the disease causing mutation and thus are not subject to mHTT-mediated toxicity. Genetic correction of mHTT iPSCs has been achieved using traditional homologous recombination approaches, which utilize donor plasmids containing large regions of homology with the HTT exon 1 locus coupled with selection in order to identify putative recombined iPSC colonies (Figure 6.6) (46). This method relies on random events that result in damage to the genome to activate DNA repair machinery and homologous recombination. Using this approach, HR is very inefficient, with ~1 in 100 antibiotic resistant colonies identified after selection showing true homologous recombination; the remaining colonies are usually present due to random integration of the resistance cassette into the genome. It is estimated that of 1 × 106 cells electroporated, 2 are found to be recombinants (efficiency is thus 1:106) (46).
Microscopy Experiments
Published in Raimund J. Ober, E. Sally Ward, Jerry Chao, Quantitative Bioimaging, 2020
Raimund J. Ober, E. Sally Ward, Jerry Chao
In choosing the two excitation wavelengths to use, it is important to ensure that the resulting fluorescence intensity ratios provide a good indicator of the pH value. A commonly employed approach is to select the first excitation wavelength to be one at which the fluorescence emission is very sensitive to pH changes, and the second excitation wavelength to be one at which the fluorescence emission is minimally sensitive to pH changes. In this way, fluorescence intensity ratios are obtained that can be used to effectively discern pH values over a useful range. In the case of fluorescein, we see from Fig. 11.12(a) that we can choose 490 nm to be the first wavelength and 440 nm to be the second wavelength based on the significant and minimal sensitivity, respectively, of the fluorescence emission to the pH at these excitation wavelengths. In Fig. 11.12(b), a calibration curve is shown for fluorescein that gives the relationship between the pH and the fluorescence intensity ratio obtained using the 490-nm and 440-nm excitation wavelengths (Note 11.5). It is worth noting that unlike fluorescein, there are fluorophores, such as the wild-type GFP mutant ratiometric pHluorin, whose excitation spectrum is characterized by high pH sensitivity in two different wavelength ranges, in such a way that as the pH increases, the fluorescence intensity increases upon excitation with light in one wavelength range, but decreases upon excitation with light in the other wavelength range. With such fluorophores, the two excitation wavelengths are chosen near the peaks of the two wavelength ranges (Note 11.6).
Analysis of coronavirus envelope protein with cellular automata model
Published in International Journal of Parallel, Emergent and Distributed Systems, 2022
Raju Hazari, Parimal Pal Chaudhuri
For the CAML model, signal graph analytics enable extraction of meaningful information from a graph relevant for mutational study of protein. Wild type AA chain refers to the one that can be observed in nature without any mutation. The chain with a mutation is termed as Mutant. CL signal graphs for wild type AA chain are shown in Figures 5–7. In view of common backbone, the base line of the CL signal graph has the CL value 3. Signal graph analytics proceeds on identifying mcl (Maximum Cycle Length) and Nmcl (Next to mcl) signals. The mcl signal is the one that covers a number of cells with maximum cycle length value. More than one mcl signals may exist in a CL graph. A signal with CL value less than mcl but greater than y is referred to as Nmcl, where number of signals with CL values (y+1) and y is greater than 3. Such an evaluation of Nmcl signals enables avoidance of noisy signals below the CL value (y+1) for signal graph analytics. The value of the parameter y will depend on the analysis of low valued signals in a CL signal graph.