Biomedical subjects

Kwang H Lee

Publications and source records attributed to Kwang H Lee.

11 recordsLinked to original sources

PubMed2007

Brain transplantation of immortalized human neural stem cells promotes functional recovery in mouse intracerebral hemorrhage stroke model.

We have generated stable, immortalized cell lines of human NSCs from primary human fetal telencephalon cultures via a retroviral vector encoding v-myc. HB1.F3, one of the human NSC lines, expresses a normal human karyotype of 46, XX, and nestin, a cell type-specific marker for NSCs. F3 has the ability to proliferate continuously and differentiate into cells of neuronal and glial lineage. The HB1.F3 human NSC line was used for cell therapy in a mouse model of intracerebral hemorrhage (ICH) stroke. Experimental ICH was induced in adult mice by intrastriatal administration of bacterial collagenase; 1 week after surgery, the rats were randomly divided into two groups so as to receive intracerebrally either human NSCs labeled with beta-galactosidase (n = 31) or phosphate-buffered saline (PBS) (n = 30). Transplanted NSCs were detected by 5-bromo-4-chloro-3-indolyl-beta-d-galactoside histochemistry or double labeling with beta-galactosidase (beta-gal) and mitogen-activated protein (MAP)2, neurofilaments (both for neurons), or glial fibrillary acidic protein (GFAP) (for astrocytes). Behavior of the animals was evaluated for period up to 8 weeks using modified Rotarod tests and a limb placing test. Transplanted human NSCs were identified in the perihematomal areas and differentiated into neurons (beta-gal/MAP2(+) and beta-gal/NF(+)) or astrocytes (beta-gal/GFAP(+)). The NSC-transplanted group showed markedly improved functional performance on the Rotarod test and limb placing after 2-8 weeks compared with the control PBS group (p < .001). These results indicate that the stable immortalized human NSCs are a valuable source of cells for cell replacement and gene transfer for the treatment of ICH and other human neurological disorders. Disclosure of potential conflicts of interest is found at the end of this article.

Animals↗

PubMed2006

Specificity of molecular interactions in transient protein-protein interaction interfaces.

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions.

Animals↗

PubMed2006

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications.

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene-patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene-patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at http://www.patome.org/; the information is updated bimonthly.

Amino Acid Sequence↗

PubMed2006

Towards clustering of incomplete microarray data without the use of imputation.

MOTIVATION: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. RESULTS: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.

Algorithms↗

PubMed2006

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets.

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003).

Algorithms↗

PubMed2006

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge.

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.

Algorithms↗

PubMed2006

Interactions between the plasma membrane and the antimicrobial peptide HP (2-20) and its analogues derived from Helicobacter pylori.

HP (2-20), a 19-residue peptide derived from the N-terminus of Helicobacter pylori ribosomal protein L1, has antimicrobial activity but is not cytotoxic to human erythrocytes. We synthesized several peptide analogues to investigate the effects of substitutions on structure and antimicrobial activity. Replacement of Gln16 and Asp18 with tryptophan [anal-3 (analogue-3)] caused a dramatic increase in lytic activities against bacteria and fungi. By contrast, a decrease in amphiphilicity caused by replacement of Phe5 or Leu11 with serine was accompanied by a reduction in antimicrobial activity. Analysis of the tertiary structures of the peptides in SDS micelles by NMR spectroscopy revealed that they have a well-defined a-helical structure. Among the analogues, anal-3 has the longest a-helix, from Val4 to Trp18. The enhanced hydrophobicity and increased a-helicity results in enhanced antimicrobial activity in anal-3 without an increase in haemolytic activity. Fluorescence experiments proved that the bacterial-cell selectivity of the anal-3 peptide is due to its high binding affinity for negatively charged phospholipids in bacterial cells. Results showing the effect of spin-labels on the NMR spectra indicated that the side chains in the hydrophobic phase of the amphiphilic a-helix are buried on the surface of the micelle and the tryptophan indole ring is anchored in the membrane surface. Because anal-3 shows high selectivity towards bacterial and fungal cells, it may provide an avenue for the development of new antibiotics.

Amino Acid Sequence↗

PubMed2005

Simulated microgravity produces attenuated baroreflex-mediated pressor, chronotropic, and inotropic responses in mice.

Whether myocardial contractile impairment contributes to orthostatic intolerance (OI) is controversial. Accordingly, we used transient bilateral carotid occlusion (TBCO) to compare the in vivo pressor, chronotropic, and inotropic responses (parts 1 and 2) to open-loop selective carotid baroreceptor unloading in anesthetized mice. In part 3, in vitro myocyte responses to isoproterenol in mice exposed to hindlimb unweighting (HLU) for approximately 2 wk were determined. Heart rate (HR) and mean arterial pressure (MAP) responses to TBCO were measured. In control mice, TBCO increased HR (15 +/- 2 beats/min, P < 0.05) and MAP (17 +/- 2 mmHg, P < 0.05). These responses were markedly potentiated in denervated control (DC) mice, in which the aortic depressor nerve and sympathetic trunk were sectioned before measurement. Baroreflex responses to TBCO were eliminated by blockade with hexamethonium bromide (10 microg/kg). In HLU (denervated) mice, HR and MAP responses were reduced approximately 70% compared with DC mice. In part 2, myocardial contractile responses to TBCO were measured with a left ventricular micromanometer-conductance catheter. TBCO in DC mice increased the slope of the end-systolic pressure-volume relation (end-systolic elastance) by 86 +/- 13%. This inotropic response was attenuated (14 +/- 10%, P < 0.005) after HLU. In part 3, contractile responses to isoproterenol were impaired in myocytes isolated from HLU mice. In conclusion, selective carotid baroreceptor unloading stimulates HR, blood pressure, and myocardial contractility, and HLU attenuates each response. These findings have important implications for the management of OI in astronauts, the elderly, and individuals subjected to prolonged bed rest.

Animals↗

PubMed2005

Detecting clusters of different geometrical shapes in microarray gene expression data.

MOTIVATION: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. RESULTS: We present the Gustafson-Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://dragon.kaist.ac.kr/gk.

Algorithms↗

PubMed2004

Architecture of basic building blocks in protein and domain structural interaction networks.

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.

Animals↗

PubMed2004

Fuzzy branching temporal logic.

Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.

Journal Article↗