Young Bun Kim

Differentiation of Hdm2-mediated p53 ubiquitination and Hdm2 autoubiquitination activity by small molecular weight inhibitors

The oncoprotein hdm2 ubiquitinates p53, resulting in the rapid degradation of p53 through the ubiquitin (Ub)–proteasome pathway. Hdm2-mediated destabilization and inactivation of p53 are thought to play a critical role in a number of human cancers. We have used an in vitro enzyme assay, monitoring hdm2-catalyzed Ub transfer from preconjugated Ub-Ubc4 to p53, to identify small molecule inhibitors of this enzyme. Three chemically distinct types of inhibitors were identified this way, each with potency in the micromolar range. All three types of compounds display selective inhibition of hdm2 E3 ligase activity, with little or no effect on other Ub-using enzymes. Most strikingly, these compounds do not inhibit the autoubiquitination activity of hdm2. Steady-state analysis reveals that all three classes behave as simple reversible inhibitors of the enzyme and that they are noncompetitive with respect to both substrates, Ub-Ubc4 and p53. Studies of the effects of combinations of two inhibitory molecules on hdm2 activity indicate that the three types of compounds bind in a mutually exclusive fashion, suggesting a common binding site on hdm2 for all of these inhibitors. These compounds establish the feasibility of selectively blocking hdm2-mediated ubiquitination of p53 by small molecule inhibitors. Selective inhibitors of hdm2 E3 ligase activity could provide a novel mechanism for the development of new chemotherapeutics for the treatment of human cancers.

Genome differentiation of Drosophila melanogaster from a microclimate contrast in Evolution Canyon, Israel

Significance The microclimatic contrast between opposing slopes of “Evolution Canyon” (Mount Carmel, Israel) provides a natural laboratory for testing the effects of abiotic factors on biodiversity and population genetic differentiation in a geographical microscale. Drosophila melanogaster fruitflies originating from the opposite canyon slopes are subject to divergent selection leading to slope-specific adaptations, accompanied by incipient mating isolation, all in the face of pervasive demographic processes, including ongoing genetic exchange. We demonstrate that interslope genetic changes in this species accumulate in a number of chromosomal differentiation “islands” and that gene networks related to adaptive responses and reproductive processes are thus significantly affected. The opposite slopes of “Evolution Canyon” in Israel have served as a natural model system of adaptation to a microclimate contrast. Long-term studies of Drosophila melanogaster populations inhabiting the canyon have exhibited significant interslope divergence in thermal and drought stress resistance, candidate genes, mobile elements, habitat choice, mating discrimination, and wing-shape variation, all despite close physical proximity of the contrasting habitats, as well as substantial interslope migration. To examine patterns of genetic differentiation at the genome-wide level, we used high coverage sequencing of the flies’ genomes. A total of 572 genes were significantly different in allele frequency between the slopes, 106 out of which were associated with 74 significantly overrepresented gene ontology (GO) terms, particularly so with response to stimulus and developmental and reproductive processes, thus corroborating previous observations of interslope divergence in stress response, life history, and mating functions. There were at least 37 chromosomal “islands” of interslope divergence and low sequence polymorphism, plausible signatures of selective sweeps, more abundant in flies derived from one (north-facing) of the slopes. Positive correlation between local recombination rate and the level of nucleotide polymorphism was also found.

Comparative studies of active site–ligand interactions among various recombinant constructs of human β-amyloid precursor protein cleaving enzyme

Amyloid precursor protein (APP) cleaving enzyme (BACE) is the enzyme responsible for beta-site cleavage of APP, leading to the formation of the amyloid-beta peptide that is thought to be pathogenic in Alzheimers disease (AD). Hence, BACE is an attractive pharmacological target, and numerous research groups have begun searching for potent and selective inhibitors of this enzyme as a potential mechanism for therapeutic intervention in AD. The mature enzyme is composed of a globular catalytic domain that is N-linked glycosylated in mammalian cells, a single transmembrane helix that anchors the enzyme to an intracellular membrane, and a short C-terminal domain that extends outside the phospholipid bilayer of the membrane. Here we have compared the substrate and active site-directed inhibitor binding properties of several recombinant constructs of human BACE. The constructs studied here address the importance of catalytic domain glycosylation state, inclusion of domains other than the catalytic domain, and incorporation into a membrane bilayer on the interactions of the enzyme active site with peptidic ligands. We find no significant differences in ligand binding properties among these various constructs. These data demonstrate that the nonglycosylated, soluble catalytic domain of BACE faithfully reflects the ligand binding properties of the full-length mature enzyme in its natural membrane environment. Thus, the use of the nonglycosylated, soluble catalytic domain of BACE is appropriate for studies aimed at understanding the determinants of ligand recognition by the enzyme active site.

Divergence of Drosophila melanogaster repeatomes in response to a sharp microclimate contrast in Evolution Canyon, Israel

Significance Repeatome, or the ensemble of all repeat sequences, with its enormous variability and internal epigenetic dynamics, emerges as a critical source of potentially adaptive changes and evolutionary novelties. This conclusion is exemplified here by the cosmopolitan Drosophila melanogaster from a sharp ecological contrast in North Israel. Flies derived from the opposing sides of this long-studied microsite exhibit a significant difference in the contents and distribution of mobile elements, as well as microsatellite allele frequencies, corresponding well with earlier reported phenotypic patterns of stress resistance and assortative mating in the system. Repeat sequences, especially mobile elements, make up large portions of most eukaryotic genomes and provide enormous, albeit commonly underappreciated, evolutionary potential. We analyzed repeatomes of Drosophila melanogaster that have been diverging in response to a microclimate contrast in Evolution Canyon (Mount Carmel, Israel), a natural evolutionary laboratory with two abutting slopes at an average distance of only 200 m, which pose a constant ecological challenge to their local biotas. Flies inhabiting the colder and more humid north-facing slope carried about 6% more transposable elements than those from the hot and dry south-facing slope, in parallel to a suite of other genetic and phenotypic differences between the two populations. Nearly 50% of all mobile element insertions were slope unique, with many of them disrupting coding sequences of genes critical for cognition, olfaction, and thermotolerance, consistent with the observed patterns of thermotolerance differences and assortative mating.

Unsupervised Gene Selection For High Dimensional Data

In this paper, we present a new hybrid approach for unsupervised gene selection. Hybrid approaches try to utilize different evaluation criteria of the filter approaches and wrapper approaches in different search stages. Our method thus uses a two-step approach to identify informative genes. The first step retrieves gene subsets with original physical meaning based on their capacities to reproduce sample projections on principle components by applying the least-square-estimation based evaluation. The second step then searches for the best gene subsets that maximize clustering performance. When applied to a gene expression dataset of leukemia, the method identified a small set of genes whose expression is highly predictive

Biomarker Selection for Predicting Alzheimer Disease Using High-Resolution MALDI-TOF Data

High-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in analyzing MALDI-TOF data is the large number of mass/charge peaks (a.k.a. features, data points). With such a huge number of data points for a single sample, efficient feature selection is critical for unequivocal protein pattern discovery. In this paper, we propose a feature selection method and a new biclassification algorithm based on error-correcting output coding (ECOC) in multiclass problems. Our scheme is applied to the analysis of alzheimers disease (AD) data. To validate the performance of the proposed algorithm, experiments are performed in comparison with other methods. We show that our proposed framework outperforms not only the standard ECOC framework but also other algorithms.

Functional Proteomic Pattern Identification under Low Dose Ionizing Radiation

The goal of this study is to explore and to understand the dynamic responses of signaling pathways to low dose ionizing radiation (IR). Low dose radiation (10 cGy or lower) affects several signaling pathways including DNA repair, survival, cell cycle, cell growth, and cell death. To detect the possibly regulatory protein/kinase functions, an emerging reverse-phase protein microarray (RPPM) in conjunction with quantum dots nano-crystal technology is used as a quantitative detection system. The dynamic responses are observed under different time points and radiation doses. To quantitatively determine the responsive protein/kinases and to discover the network motifs, we present a Discriminative Network Pattern Identification System (DiNPIS). Instead of simply identifying proteins contributing to the pathways, this methodology takes into consideration of protein dependencies which are represented as Strong Jumping Emerging Patterns (SJEP). Furthermore, infrequent patterns though occurred will be considered irrelevant. The whole framework consists of three steps: protein selection, protein pattern identification, and pattern annotation. Computational results of analyzing ATM (ataxia-telangiectasia mutated) cells treated with six different IR doses up to 72 hours are presented.

A New Semi-Supervised Subspace Clustering Algorithm on Fitting Mixture Models

We propose a new subspace clustering algorithm (EPSCMIX), which is based on the feature saliency measure that is obtained by using both the Emerging Patterns algorithm and the EM algorithm, for the analysis of microarray data. For the model selection, it employs a novel agglomerative step together with MDL criterion. And, we present the result of comparative experiments between AIC, MDL and minimum message length (MML) used to determine a criterion for our algorithm. The robustness of using emerging patterns based on mixture models, as well as using the Gaussian mixture model for subspace clustering, was demonstrated on both synthetic and real data sets. In experiments, it also certified that a new agglomerative method that merges mostly correlated components with MDL consistently worked better than the one that removes weak weight components.

A new maximum-relevance criterion for significant gene selection

Gene (feature) selection has been an active research area in microarray analysis. Max-Relevance is one of the criteria which has been broadly used to find features largely correlated to the target class. However, most approximation methods for Max-Relevance do not consider joint effect of features on the target class. We propose a new Max-Relevance criterion which combines the collective impact of the most expressive features in Emerging Patterns (EPs) and some popular independent criteria such as t-test and symmetrical uncertainty. The main benefit of this criterion is that by capturing the joint effect of features using EPs algorithm, it finds the most discriminative features in a broader scope. Experiment results clearly demonstrate that our feature sets improve the class prediction comparing to other feature selections.

A New Hybrid Approach for Unsupervised Gene Selection

In recent years, unsupervised gene (feature) selection has become an integral part of microarray analysis because of the large number of genes and complexity in biological systems. Principal components analysis (PCA) is one of the approaches which has been applied, even though principal components (PCs) have no clear physical meanings. In this paper, we present a PCA based feature selection within a wrapper framework called PFSBEM (hybrid PCA based feature selection and boost-expectation-maximization clustering). PFSBEM uses a two-step approach to select features. The first step retrieves feature subsets with original physical meaning based on their capacities to reproduce sample projections on PCs. The second step then searches for the best feature subsets that maximize clustering performance. Experiment results clearly show that our feature sets improve the class prediction with respect to the chosen performance criteria