Biomedical subjects

Myong-Hee Sung

Publications and source records attributed to Myong-Hee Sung.

7 recordsLinked to original sources

PubMed2006

Wnt pathway mutations selected by optimal beta-catenin signaling for tumorigenesis.

Mutations in components of the Wnt/beta-catenin pathway are observed to be the earliest initiating event for most colorectal tumors. The majority of the mutations occur in the tumor suppressor adenomatous polyposis coli (APC), even though there are other genes that are capable of modulating the pathway activity. Moreover, the specific APC mutations associated in colon cancer indicate the possibility that the tumor selects for certain truncated forms of APC that partially retain its function, namely, inhibition of beta-catenin. We estimated the effects of various mutations in APC and other known mutations using a recent mathematical model of the Wnt pathway that was constructed to represent the conserved core molecular events. We provide evidence that APC mutations are selected not based on the maximal level of beta-catenin but rather based on distinct state of activity that appears to be optimal for the tissue-specific tumorigenesis. This optimal level is determined by balancing beta-catenin signaling and the induction of Axin2 that acts as a potent negative feedback. The predominant pattern of APC mutations may provide synergistic oncogenic effects that promote colorectal tumorigenesis: the optimal signaling for cell survival and renewal, disrupted cell adhesion, chromosomal instability, and altered asymmetric division of stem cells.

Adenomatous Polyposis Coli↗

PubMed2005

In silico identification of the key components and steps in IFN-gamma induced JAK-STAT signaling pathway.

Systems biology efforts are increasingly adopting quantitative, mechanistic modeling to study cellular signal transduction pathways and other networks. However, it is uncertain whether the particular set of kinetic parameter values of the model closely approximates the corresponding biological system. We propose that the parameters be assigned statistical distributions that reflect the degree of uncertainty for a comprehensive simulation analysis. From this analysis, we globally identify the key components and steps in signal transduction networks at a systems level. We investigated a recent mathematical model of interferon gamma induced Janus kinase-signal transducers and activators of transcription (JAK-STAT) signaling pathway by applying multi-parametric sensitivity analysis that is based on simultaneous variation of the parameter values. We find that suppressor of cytokine signaling-1, nuclear phosphatases, cytoplasmic STAT1, and the corresponding reaction steps are sensitive perturbation points of this pathway.

Computational Biology↗

PubMed2004

Expansion and functional relevance of high-avidity myelin-specific CD4+ T cells in multiple sclerosis.

Multiple sclerosis (MS) is an autoimmune disease in which myelin-specific T cells are believed to play a crucial pathogenic role. Nevertheless, so far it has been extremely difficult to demonstrate differences in T cell reactivity to myelin Ag between MS patients and controls. We believe that by using unphysiologically high Ag concentrations previous studies have missed a highly relevant aspect of autoimmune responses, i.e., T cells recognizing Ag with high functional avidity. Therefore, we focused on the characterization of high-avidity myelin-specific CD4+ T cells in a large cohort of MS patients and controls that was matched demographically and with respect to expression of MHC class II alleles. We demonstrated that their frequency is significantly higher in MS patients while the numbers of control T cells specific for influenza hemagglutinin are virtually identical between the two cohorts; that high-avidity T cells are enriched for previously in vivo-activated cells and are significantly skewed toward a proinflammatory phenotype. Moreover, the immunodominant epitopes that were most discriminatory between MS patients and controls differed from those described previously and were clearly biased toward epitopes with lower predicted binding affinities to HLA-DR molecules, pointing at the importance of thymic selection for the generation of the autoimmune T cell repertoire. Correlations between selected immunological parameters and magnetic resonance imaging markers indicate that the specificity and function of these cells influences phenotypic disease expression. These data have important implications for autoimmunity research and should be considered in the development of Ag-specific therapies in MS.

Adult↗

PubMed2004

Candidate epitope identification using peptide property models: application to cancer immunotherapy.

Peptides derived from pathogens or tumors are selectively presented by the major histocompatibility complex proteins (MHC) to the T lymphocytes. Antigenic peptide-MHC complexes on the cell surface are specifically recognized by T cells and, in conjunction with co-factor interactions, can activate the T cells to initiate the necessary immune response against the target cells. Peptides that are capable of binding to multiple MHC molecules are potential T cell epitopes for diverse human populations that may be useful in vaccine design. Bioinformatical approaches to predict MHC binding peptides can facilitate the resource-consuming effort of T cell epitope identification. We describe a new method for predicting MHC binding based on peptide property models constructed using biophysical parameters of the constituent amino acids and a training set of known binders. The models can be applied to development of anti-tumor vaccines by scanning proteins over-expressed in cancer cells for peptides that bind to a variety of MHC molecules. The complete algorithm is described and illustrated in the context of identifying candidate T cell epitopes for melanomas and breast cancers. We analyzed MART-1, S-100, MBP, and CD63 for melanoma and p53, MUC1, cyclin B1, HER-2/neu, and CEA for breast cancer. In general, proteins over-expressed in cancer cells may be identified using DNA microarray expression profiling. Comparisons of model predictions with available experimental data were assessed. The candidate epitopes identified by such a computational approach must be evaluated experimentally but the approach can provide an efficient and focused strategy for anti-cancer immunotherapy development.

Animals↗

PubMed2004

Genomewide conserved epitope profiles of HIV-1 predicted by biophysical properties of MHC binding peptides.

We propose a new method for predicting MHC binding of peptides using biophysical parameters of the constituent amino acids. Unlike conventional matrix-based methods, our method does not assume independent binding of the individual side chains and uses a model that simultaneously represents all the residues. The model discovers the quantified 9-mer "property model" within the longer peptides that are most common among binders. Prediction for a new peptide is based on its statistical "distance" from the extracted peptide property model. MHC-specific peptide property models were constructed from compiled binder/nonbinder data using this method. We report the results of cross-validation of the prediction method and comparison with other methods. The comparison suggests that our method performs substantially better for some MHC class II molecules and equally well for other MHC types. To demonstrate large-scale utility, 30 HIV-1 reference genomes covering diverse subtypes were analyzed. Regions that are likely to bind MHC (A2, DR1, or DR4) and that are conserved across the HIV-1 subtypes were identified. These "epitope profiles" of the diverse HIV-1 strains can also be visually presented to facilitate discovery of conserved patterns naturally occurring in the viral genomes. As an essential step in designing vaccines, the revealed patterns may provide valuable information in identifying the immunologically important regions.

Computer Simulation↗

PubMed2004

In silico simulation of inhibitor drug effects on nuclear factor-kappaB pathway dynamics.

NF-kappaB is a transcription factor family that activates numerous genes that are related to cell survival, apoptosis, and cell migration. Its persistent activity is associated with tumor formation, growth, metastasis, and drug resistance in many cancer types, including lymphoma, colon cancer, and breast cancer. Current therapeutic efforts for inhibiting this central "switch" include using small molecules to block a selected target in this pathway. Recognizing the regulatory network structure of the NF-kappaB pathway, we examine in silico the effects of inhibitors targeting various network components, using a kinetic model of the pathway. By simulating the corresponding perturbed system dynamics, we show the resulting time course of inhibition has distinct target-specific profiles. In particular, greater oscillatory potential exists for inhibition of upstream events than for direct inhibition of NF-kappaB, at low drug concentrations. This phenomenon is observed also when we examine the dynamic effects of the recently approved proteasome inhibitor, bortezomib (PS-341), and compare it with other inhibitors, taking its pharmacokinetics into consideration. Such kinetic analyses of the "drugged" molecular system will facilitate optimal drug target selection and the development of treatment protocols for a molecularly targeted therapy.

Computer Simulation↗

PubMed2002

T-cell epitope prediction with combinatorial peptide libraries.

T cell receptors (TCR) recognize antigenic peptides in complex with the major histocompatibility complex (MHC) molecules and this trimolecular interaction initiates antigen-specific signaling pathways in the responding T lymphocytes. For the study of autoimmune diseases and vaccine development, it is important to identify peptides (epitopes) that can stimulate a given TCR. The use of combinatorial peptide libraries has recently been introduced as a powerful tool for this purpose. A combinatorial library of n-mer peptides is a set of complex mixtures each characterized by one position fixed to be a specified amino acid and all other positions randomized. A given TCR can be fingerprinted by screening a variety of combinatorial libraries using a proliferation assay. Here, we present statistical models for elucidating the recognition profile of a TCR using combinatorial library proliferation assay data and known MHC binding data.

Combinatorial Chemistry Techniques↗