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Gwan-Su Yi

Publications and source records attributed to Gwan-Su Yi.

4 recordsLinked to original sources
PubMed2005

Single-stranded DNA mimicry in the p53 transactivation domain interaction with replication protein A.

One of many protein-protein interactions modulated upon DNA damage is that of the single-stranded DNA-binding protein, replication protein A (RPA), with the p53 tumor suppressor. Here we report the crystal structure of RPA residues 1-120 (RPA70N) bound to the N-terminal transactivation domain of p53 (residues 37-57; p53N) and, by using NMR spectroscopy, characterize two mechanisms by which the RPA/p53 interaction can be modulated. RPA70N forms an oligonucleotide/oligosaccharide-binding fold, similar to that previously observed for the ssDNA-binding domains of RPA. In contrast, the N-terminal p53 transactivation domain is largely disordered in solution, but residues 37-57 fold into two amphipathic helices, H1 and H2, upon binding with RPA70N. The H2 helix of p53 structurally mimics the binding of ssDNA to the oligonucleotide/oligosaccharide-binding fold. NMR experiments confirmed that both ssDNA and an acidic peptide mimicking a phosphorylated form of RPA32N can independently compete the acidic p53N out of the binding site. Taken together, our data suggest a mechanism for DNA damage signaling that can explain a threshold response to DNA damage.

Amino Acid Sequence↗
PubMed2004

Identification and integrative analysis of 28 novel genes specifically expressed and developmentally regulated in murine spermatogenic cells.

Mammalian spermatogenesis is a highly ordered process that occurs in mitotic, meiotic, and postmeiotic phases. The unique mechanisms responsible for this tightly regulated developmental process suggest the presence of an intrinsic genetic program composed of spermatogenic cell-specific genes. In this study, we analyzed the mouse round spermatid UniGene library currently containing 2124 gene-oriented transcript clusters, predicting that 467 of them are testis-specific genes, and systematically identified 28 novel genes with evident testis-specific expression by in silico and in vitro approaches. We analyzed these genes by Northern blot hybridization and cDNA cloning, demonstrating the presence of additional transcript sequences in five genes and multiple transcript isoforms in six genes. Genomic analysis revealed lack of human orthologues for 10 genes, implying a relationship between these genes and male reproduction unique to mouse. We found that all of the novel genes are expressed in developmentally regulated and stage-specific patterns, suggesting that they are primary regulators of male germ cell development. Using computational bioinformatics tools, we found that 20 gene products are potentially involved in various processes during spermatogenesis or fertilization. Taken together, we predict that over 20% of the genes from the round spermatid library are testis-specific, have discovered the 28 authentic, novel genes with probable spermatogenic cell-specific expression by the integrative approach, and provide new and thorough information about the novel genes by various in vitro and in silico analyses. Thus, the study establishes on a comprehensive scale a new basis for studies to uncover molecular mechanisms underlying the reproductive process.

Animals↗
PubMed2004

Reuse of imputed data in microarray analysis increases imputation efficiency.

BACKGROUND: The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. RESULTS: We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. CONCLUSIONS: Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data.

Efficiency, Organizational↗