Biomedical subjects

S June Oh

Publications and source records attributed to S June Oh.

3 recordsLinked to original sources

PubMed2006

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

BACKGROUND: To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. RESULTS: To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. CONCLUSION: By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.

Archaeal Proteins↗

PubMed2005

Identification and functional characterization of novel CYP2J2 variants: G312R variant causes loss of enzyme catalytic activity.

CYP2J2 plays important roles in the metabolism of therapeutic drugs, such as astemizole and ebastine, as well as endogenous fatty acids. This study aimed to identify CYP2J2 genetic variants in Koreans and to characterize their functional consequences. From direct sequencing of the CYP2J2 gene, 12 genetic variations, including the two novel nonsynonymous mutations G312R and P351L, were identified from 93 Korean subjects. The two novel CYP2J2 variants were co-expressed with NADPH-cytochrome P450 reductase in Sf9 cells and their catalytic activities were quantified. The recombinant CYP2J2 G312R variant showed almost complete loss of enzymatic activity, as determined by CYP2J2-catalysed astemizole O-demethylation and ebastine hydroxylation. The CYP2J2 P351L variant showed enzymatic activities that were comparable with the wild-type CYP2J2. The reduced CO spectra of the recombinant CYP2J2 proteins suggested no CO binding to the heme in CYP2J2 G312R. In addition, molecular modelling of the three-dimensional structure consistently predicted that there might be spatial hindrance between heme and the bulky side chain of the R312 residue in CYP2J2 G312R variant. The CYP2J2 G312R variant was not found in 192 Chinese, 99 African-Americans, 100 Caucasians and 159 Vietnamese subjects. Two of the 192 Chinese subjects (0.52%) were heterozygous for CYP2J2 P351L. Twelve CYP2J2 variants, including two novel nonsynonymous variants, were identified in a Korean population. The G312R variant is the first nonfunctional CYP2J2 allele to be identified, and is expected to influence the disposition of its substrate therapeutics, as well as endogenous compounds.

Alleles↗

PubMed2005

Bayesian network learning with feature abstraction for gene-drug dependency analysis.

Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset (http://discover.nci.nih.gov) consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.

Algorithms↗