Biomedical subjects

Yang Seok Kim

Publications and source records attributed to Yang Seok Kim.

3 recordsLinked to original sources

PubMed2006

Systematic analysis of cDNA microarray-based CGH.

cDNA microarray-based CGH (Microarray-CGH) is a useful technique for detecting genomic aberrations with a high resolution. However, the criteria for determining a genomic alteration have not been determined. We evaluated the genome-wide measurement of copy number of each gene in normal gastric and placenta tissues with both sex-matched, direct and sex-mismatched, indirect designs using 17K cDNA microarray. The results revealed the range of genomic copy number of normal tissues to be +/-0.3 of the log(2) ratio (gain >0.3, loss <-0.3) in the autosomal genes with direct and indirect designs. The copy number at a gene level from the X chromosomal genes using the direct and indirect sex-mismatched designs was +/-0.68 of the log(2) ratio (amplification >0.68, deletion <-0.68). In summary, the suggested method can be used as a guideline for analysis of genomic aberration using a Microarray-CGH in both direct and indirect designs.

Chromosomes, Human↗

PubMed2005

Attenuation of telomerase activity by hammerhead ribozyme targeting human telomerase RNA induces growth retardation and apoptosis in human breast tumor cells.

Ribozyme possesses specific endoribonuclease activity and catalyzes the hydrolysis of specific phosphodiester bonds, which results in the cleavage of target RNA sequences. Here, we evaluated the ability of hammerhead ribozymes targeting human telomerase RNA (hTR) to inhibit the catalytic activity of telomerase and the proliferation of cancer cells. Hammerhead ribozymes were designed against 7 NUX sequences located in open loops of the hTR secondary structure. We verified the ribozyme specificity by in vitro cleavage assay by using a synthetic RNA substrate. Subsequently, we introduced ribozyme expression vector into human breast tumor MCF-7 cells and assessed the biologic effects of ribozyme. Hammerhead ribozyme R1 targeting the template region of hTR efficiently cleaved hTR in vitro, and stable transfectants of this ribozyme induced the degradation of target hTR RNA and attenuated telomerase activity in MCF-7 cells. Moreover, the ribozyme R1 transfectant displayed a significant telomere shortening and a lower proliferation rate than parental cells. Clones with reduced proliferation capacity showed enlarged senescence-like shapes or highly differentiated dendritic morphologies of apoptosis. In conclusion, the inhibition of telomerase activity by hammerhead ribozyme targeting the template region of the hTR presents a promising strategy for inhibiting the growth of human breast cancer cells.

Apoptosis↗

PubMed2004

A graph-theoretic modeling on GO space for biological interpretation of gene clusters.

MOTIVATION: With the advent of DNA microarray technologies, the parallel quantification of genome-wide transcriptions has been a great opportunity to systematically understand the complicated biological phenomena. Amidst the enthusiastic investigations into the intricate gene expression data, clustering methods have been the useful tools to uncover the meaningful patterns hidden in those data. The mathematical techniques, however, entirely based on the numerical expression data, do not show biologically relevant information on the clustering results. RESULTS: We present a novel methodology for biological interpretation of gene clusters. Our graph theoretic algorithm extracts common biological attributes of the genes within a cluster or a group of interest through the modified structure of gene ontology (GO) called GO tree. After genes are annotated with GO terms, the hierarchical nature of GO terms is used to find the representative biological meanings of the gene clusters. In addition, the biological significance of gene clusters can be assessed quantitatively by defining a distance function on the GO tree. Our approach has a complementary meaning to many statistical clustering techniques; we can see clustering problems from a different viewpoint by use of biological ontology. We applied this algorithm to the well-known data set and successfully obtained the biological features of the gene clusters with the quantitative biological assessment of clustering quality through GO Biological Process.

Algorithms↗