Assessment of genetic mutations in the XRCC2 coding region by high resolution melting curve analysis and the risk of differentiated thyroid carcinoma in Iran

Homologous recombination (HR) is the major pathway for repairing double strand breaks (DSBs) in eukaryotes and XRCC2 is an essential component of the HR repair machinery. To evaluate the potential role of mutations in gene repair by HR in individuals susceptible to differentiated thyroid carcinoma (DTC) we used high resolution melting (HRM) analysis, a recently introduced method for detecting mutations, to examine the entire XRCC2 coding region in an Iranian population. HRM analysis was used to screen for mutations in three XRCC2 coding regions in 50 patients and 50 controls. There was no variation in the HRM curves obtained from the analysis of exons 1 and 2 in the case and control groups. In exon 3, an Arg(188)His polymorphism (rs3218536) was detected as a new melting curve group (OR: 1.46; 95%CI: 0.432-4.969; p = 0.38) compared with the normal melting curve. We also found a new Ser(150)Arg polymorphism in exon 3 of the control group. These findings suggest that genetic variations in the XRCC2 coding region have no potential effects on susceptibility to DTC. However, further studies with larger populations are required to confirm this conclusion.     

Taxol induces caspase-10-dependent apoptosis

Taxol (paclitaxel) is known to inhibit cell growth and trigger significant apoptosis in various cancer cells. Although taxol induces apoptosis of cancer cells, its exact mechanism of action is not yet known. In this study we investigated death receptors, FAS-associated death domain protein (FADD), the activation of caspases-10 and -8 as well as the downstream caspases, and reactive oxygen species (ROS) in taxol-induced apoptosis in the CCRF-HSB-2 human lymphoblastic leukemia cell line. Pretreating the cells with neutralizing antibodies to Fas, tumor necrosis factor (TNF)-alpha receptor 1, or TNF-related apoptosis-inducing ligand receptors (DR4 and DR5) did not affect taxol-induced apoptosis, but transfection of the cells with a dominant negative FADD plasmid resulted in inhibition of taxol-induced apoptosis, revealing that taxol induces apoptosis independently of these death receptors but dependently on FADD. Furthermore, the drug induced activation of caspases-10, -8, -6, and -3, cleaved Bcl-2, Bid, poly(ADP-ribose) polymerase, and lamin B, and down-regulated cellular levels of FLICE-like inhibitory protein (FLIP) and X-chromosome-linked inhibitor of apoptosis protein (XIAP). However, despite the release of cytochrome c from the mitochondria in taxol-treated cells, caspase-9 was not activated. Inhibitors of caspases-8, -6, or -3 partially inhibited taxol-induced apoptosis, whereas the caspase-10 inhibitor totally abrogated this process. Taxol-induced apoptosis was also associated with decreased mitochondrial membrane potential (Deltapsim) and a significant increase in ROS generation. However, increased ROS production was not directly involved in taxol-triggered apoptosis. Therefore, these results demonstrate for the first time that taxol induces FADD-dependent apoptosis primarily through activation of caspase-10 but independently of death receptors.     

Isocyanide-based consecutive Bargellini/Ugi reactions: an efficient method for the synthesis of pseudo-peptides containing three amide bonds

Isocyanide-based consecutive Bargellini/Ugi multicomponent reactions as a combinatorial strategy have been developed for the synthesis of new class of pseudo-peptides. Via Bargellini reaction 3-carboxamido-isobutyric acids are prepared using acetone, chloroform, sodium hydroxide, and isocyanides. Then, using Ugi multicomponent reaction strategy, pseudo-peptides containing three amide bonds are synthesized using the Bargellini reaction product, aldehydes, amines, and isocyanides. This is an efficient and eco-friendly approach for easy access to wide variety of structurally diverse, drug-like pseudo-peptides from cheap and readily available precursors in high yields.

     

Detection of the acrolein-derived cyclic DNA adduct by a quantitative 32P-postlabeling/solid-phase extraction/HPLC method: blocking its artifact formation with glutathione

Acrolein (Acr), a hazardous air pollutant, reacts readily with deoxyguanosine (dG) in DNA to produce cyclic 1, N2-propanodeoxyguanosine adducts (Acr-dG). Studies demonstrate that these adducts are detected in vivo and may play a role in mutagenesis and carcinogenesis. In the study described here, a quantitative 32P-postlabeling/solid-phase extraction/HPLC method was developed by optimizing the solid-phase extraction and the 32P-postlabeling conditions for analysis of Acr-dG in DNA samples with a detection limit of 0.1 fmol. It was found that Acr-dG can form as an artifact during the assay. Evidence obtained from mass spectrometry indicates that the Acr in water used in the assay is a likely source of artifact formation of Acr-dG. The formation of Acr-dG as an artifact can be effectively blocked by adding glutathione (GSH) to the DNA sample to be analyzed. In addition, Acr-dG was detected as a contaminant in the commercial dG and dT 3'-monophosphate samples. Finally, this method was used to detect Acr-dG in calf thymus and human colon HT29 cell DNA with an excellent linear quantitative relationship.     

Efficient Modeling and Active Learning Discovery of Biological Responses

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random.