Structural chemoproteomics and drug discovery

Our laboratories have developed several technologies to accelerate drug discovery process on the basis of structural chemoproteomics. They include SPS technology for the efficient determination of protein structures, SCP technology for the rapid lead generation and SDF technology for the productive lead optimization. Using these technologies, we could determine many 3D structures of target proteins bound with biologically active chemicals including the structure of phosphodiesterase 5/Viagra complex and obtain highly potent compounds in animal models of obesity, diabetes, cancer and inflammation. In this paper, we will discuss concepts and applications of structural chemoproteomics for drug discovery.     

A column method for determination of DNA cytosine-C5-methyltransferase activity

DNA methylation at the 5th position of cytosine has been found to be correlated with tumorigenesis. An inhibitor of DNA methylase could, therefore, be used as an anticancer drug. However, only a few inhibitory compounds have been discovered due to the limitations for assaying the DNA methylation. In this study, we describe a modification of DNA cytosine-C5-methyltransferase assay system utilizing [(3)H]-labeled S-adenosyl-methionine (SAM) and Sephadex G-25 column. Pre-treatment of either lambda DNA or the promoter region of human telomerase (hTERT) with HaeIII methylase greatly reduced the digestion of the DNAs with the corresponding restriction enzyme HaeIII endonuclease (over 100-fold), and the result was further confirmed by agarose gel electrophoresis. Application of this column method to another modification/restriction system, EcoRI methylase/endonuclease, gave rise to the similar results. Our data suggest that the newly developed column method could be effective for rapid screening of large number of cytosine methylase inhibitors and could also be applicable to other DNA methylases.     

Equivalence of multibreed animal models and hierarchical Bayes analysis for maternally influenced traits

Background

It has been argued that multibreed animal models should include a heterogeneous covariance structure. However, the estimation of the (co)variance components is not an easy task, because these parameters can not be factored out from the inverse of the additive genetic covariance matrix. An alternative model, based on the decomposition of the genetic covariance matrix by source of variability, provides a much simpler formulation. In this study, we formalize the equivalence between this alternative model and the one derived from the quantitative genetic theory. Further, we extend the model to include maternal effects and, in order to estimate the (co)variance components, we describe a hierarchical Bayes implementation. Finally, we implement the model to weaning weight data from an Angus × Hereford crossbred experiment.

Methods

Our argument is based on redefining the vectors of breeding values by breed origin such that they do not include individuals with null contributions. Next, we define matrices that retrieve the null-row and the null-column pattern and, by means of appropriate algebraic operations, we demonstrate the equivalence. The extension to include maternal effects and the estimation of the (co)variance components through the hierarchical Bayes analysis are then straightforward. A FORTRAN 90 Gibbs sampler was specifically programmed and executed to estimate the (co)variance components of the Angus × Hereford population.

Results

In general, genetic (co)variance components showed marginal posterior densities with a high degree of symmetry, except for the segregation components. Angus and Hereford breeds contributed with 50.26% and 41.73% of the total direct additive variance, and with 23.59% and 59.65% of the total maternal additive variance. In turn, the contribution of the segregation variance was not significant in either case, which suggests that the allelic frequencies in the two parental breeds were similar.

Conclusion

The multibreed maternal animal model introduced in this study simplifies the problem of estimating (co)variance components in the framework of a hierarchical Bayes analysis. Using this approach, we obtained for the first time estimates of the full set of genetic (co)variance components. It would be interesting to assess the performance of the procedure with field data, especially when interbreed information is limited.     

The antifungal drug voriconazole is an efficient inhibitor of brain cholesterol 24S-hydroxylase in vitro and in vivo

Cholesterol 24S-hydroxylase (CYP46A1) is of key importance for cholesterol homeostasis in the brain. This enzyme seems to be resistant toward most regulatory factors and at present no drug effects on its activity have been described. The crystal structures of the substrate-free and substrate-bound CYP46A1 were recently determined (Mast et al., Crystal structures of substrate-bound and substrate-free cytochrome P450 46A1, the principal cholesterol hydroxylase in the brain. Proc. Natl. Acad. Sci. USA. 2008. 105: 9546–9551). These structural studies suggested that ligands other than sterols can bind to CYP46A1. We show here that the antifungal drug voriconazole binds to the enzyme in vitro and inhibits CYP46A1-mediated cholesterol 24-hydroxylation with a Ki of 11 nM. Mice treated with daily intraperitoneal injections of voriconazole for 5 days had high levels of voriconazole in the brain and significantly reduced brain levels of 24S-hydroxycholesterol. The levels of squalene, lathosterol, and HMG-CoA reductase mRNA were reduced in the brain of the voriconazole-treated animals as well, indicating a reduced cholesterol synthesis. Most of this effect may be due to a reduced utilization of cholesterol by CYP46A1. One of the side-effects of voriconazole is visual disturbances. Because CYP46A1 is also expressed in the neural retina, we discuss the possibility that the inhibition of CYP46A1 by voriconazole contributes to these visual disturbances.     

High glucose increase cell cycle regulatory proteins level of mouse embryonic stem cells via PI3-K/Akt and MAPKs signal pathways

This study examined the effects of high glucose on cell proliferation and its related signal pathways using mouse embryonic stem (ES) cells. Here, we showed that high glucose level significantly increased [3H]thymidine incorporation, BrdU incorporation, the number of cells, [3H]leucine, and [3H]proline incorporation in a time-( >3 hr) and dose-(> 25 mM) dependent manner. Moreover, high glucose level increased the cellular reactive oxygen species (ROS), Akt, and mitogen-activated protein kinases (MAPKs) phosphorylation. Subsequently, these signaling molecules involved in high glucose-induced increase of [3H]thymidine incorporation. High glucose level also increased cyclin D1, cyclin E, cyclin-dependent kinase (CDK) 2, and CDK 4 protein levels, which is cell cycle regulatory proteins acting in G1-S phase of cell cycle. Inhibition of phosphatidylinositol 3-kinase (PI3-K) (LY 294002: PI3-kinase inhibitor, 10(-6) M), Akt (Akt inhibitor, 10(-5) M), and p44/42 MAPKs (PD 98059: MEK inhibitor, 10(-5) M) decreased these proteins. High glucose level phosphorylated the RB protein, which was decreased by inhibition of PI3-K and Akt. In conclusion, high glucose level stimulates mouse ES cell proliferation via the PI3-K/Akt and MAPKs pathways.