Biomedical subjects

Daehee Hwang

Publications and source records attributed to Daehee Hwang.

13 recordsLinked to original sources

PubMed2006

Expression and functional profiling reveal distinct gene classes involved in fatty acid metabolism.

Cells respond to fatty acid exposure by metabolic reorganization and proliferation of peroxisomes. Described here is the development and application of a genome-wide screen to identify nonessential yeast genes necessary for efficient metabolism of myristic and oleic acids. Comparison of the resultant fitness data set with an integrated data set of genes transcriptionally responsive to fatty acids revealed very little overlap between the data sets. Furthermore, the fitness data set enriched for genes involved in peroxisome biogenesis and other processes related to cell morphology, whereas the expression data set enriched for genes related to metabolism. These data suggest that in response to fatty acid exposure, transcriptional control is biased towards metabolic reorganization, and structural changes tend to be controlled post-transcriptionally. They also suggest that fatty acid responsive metabolic networks are more robust than those related to cell structure. Statistical analyses of these and other global data sets suggest that the utilization of distinct control mechanisms for the execution of morphological versus metabolic responses is widespread.

Fatty Acids↗

PubMed2005

A data integration methodology for systems biology.

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.

Informatics↗

PubMed2005

A data integration methodology for systems biology: experimental verification.

The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets.

Chromatin Immunoprecipitation↗

PubMed2005

Transcriptional therapy with the histone deacetylase inhibitor trichostatin A ameliorates experimental autoimmune encephalomyelitis.

We demonstrate that the histone deacetylase (HDAC) inhibitor drug trichostatin A (TSA) reduces spinal cord inflammation, demyelination, neuronal and axonal loss and ameliorates disability in the relapsing phase of experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis (MS). TSA up-regulates antioxidant, anti-excitotoxicity and pro-neuronal growth and differentiation mRNAs. TSA also inhibits caspase activation and down-regulates gene targets of the pro-apoptotic E2F transcription factor pathway. In splenocytes, TSA reduces chemotactic, pro-Th1 and pro-proliferative mRNAs. A transcriptional imbalance in MS may contribute to immune dysregulation and neurodegeneration, and we identify HDAC inhibition as a transcriptional intervention to ameliorate this imbalance.

Animals↗

PubMed2004

Identification of optimal classification functions for biological sample and state discrimination from metabolic profiling data.

MOTIVATIONS: Classification of biological samples for diagnostic purposes is a difficult task because of the many decisions involved on the number, type and functional manipulations of the input variables. This study presents a generally applicable strategy for systematic formulation of optimal diagnostic indexes. To this end, we develop a novel set of computational tools by integrating regression optimization, stepwise variable selection and cross-validation algorithms. RESULTS: The proposed discrimination methodology was applied to plasma and tissue (liver) metabolic profiling data describing the time progression of liver dysfunction in a rat model of acute hepatic failure generated by d-galactosamine (GalN) injection. From the plasma data, our methodology identified seven (out of a total of 23) metabolites, and the corresponding transform functions, as the best inputs to the optimal diagnostic index. This index showed better time resolution and increased noise robustness compared with an existing metabolic index, Fischer's BCAA/AAA molar ratio, as well as indexes generated using other commonly used discriminant analysis tools. Comparison of plasma and liver indexes found two consensus metabolites, lactate and glucose, which implicate glycolysis and/or gluconeogenesis in mediating the metabolic effects of GalN.

Algorithms↗

PubMed2004

Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function.

MOTIVATIONS: Tissue engineering constitutes an important field with its potential of addressing the current shortage in organ availability. To successfully develop tissue-engineered organs, it is crucial to understand how to maintain the cells under conditions that maximize their ability to perform their physiological roles, regardless of the environment, whether the cells are part of an extracorporeal system, such as the bioartificial liver assist device, or an implantable tissue-engineered device. Our goals are to (1) provide insight into how cells will behave when confronted with changes in its environment and (2) determine the optimal environmental factors to achieve a desired level of cellular function. RESULTS: Diverse sets of environmental factors were used to systematically perturb the metabolic behavior associated with pre-conditioning and plasma supplementation. To probe metabolic state of hepatocytes, metabolic flux analysis was used to obtain the metabolic profile. We applied a multi-block partial least square (MPLS) model to relate environmental factors and fluxes to levels of intracellular lipids and urea synthesis. The MPLS model identified: (1) the most influential environmental factors and (2) how the metabolic pathways are altered by these factors. Finally, we inverted the MPLS model to determine the concentrations and types of environmental factors required to obtain the most economical solution for achieving optimal levels of cellular function for practical situations.

Amino Acids↗

PubMed2004

Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter.

Little is known about global gene expression patterns in the human neurodegenerative disease amyotrophic lateral sclerosis (ALS). To address this, we used high-density oligonucleotide microarray technology to compare expression levels of approximately 6,800 genes in postmortem spinal cord gray matter obtained from individuals with ALS as well as normal individuals. Using Fisher discriminant analysis (FDA) and leave-one-out cross-validation (LOOCV), we discerned an ALS-specific signature. Moreover, it was possible to distinguish familial ALS (FALS) from sporadic ALS (SALS) gene expression profiles. Characterization of the specific genes significantly altered in ALS uncovered a pro-inflammatory terminal state. Moreover, we found alterations in genes involved in mitochondrial function, oxidative stress, excitotoxicity, apoptosis, cytoskeletal architecture, RNA transcription and translation, proteasomal function, and growth and signaling. It is apparent from this study that DNA microarray analysis and appropriate bioinformatics can reveal distinct phenotypic changes that underlie the terminal stages of neurodegeneration in ALS.

Amyotrophic Lateral Sclerosis↗

PubMed2004

Microarray detection of E2F pathway activation and other targets in multiple sclerosis peripheral blood mononuclear cells.

We performed microarray analysis of peripheral blood mononuclear cells (PBMCs) from multiple sclerosis (MS) patients and detected a profile of immune cell activation, autoantigen upregulation, and enhanced E2F pathway transcription. Accordingly, E2f1-deficient mice manifested only mild disability upon induction of experimental autoimmune encephalomyelitis (EAE). Furthermore, PBMCs from Avonex-treated patients had lower expression of E2F targets. The profile was enriched in genes known to harbor MS-associated polymorphisms, or localized to MS susceptibility chromosomal regions. Our study shows that PBMC microarrays reflect MS pathobiology that can be validated in the EAE model.

Adolescent↗

PubMed2003

Genomic dissection for characterization of cancerous oral epithelium tissues using transcription profiling.

Genome-wide and high-throughput functional genomic tools offer the potential of identifying disease-associated genes and dissecting disease regulatory patterns. There is a need for a set of systematic bioinformatic tools that handles efficiently a large number of variables for extracting biological meaning from experimental outputs. We present well-characterized statistical tools to discover genes that are differentially expressed between malignant oral epithelial and normal tissues in microarray experiments and to construct a robust classifier using the identified discriminatory genes. Those tools include Wilks' lambda score, error rate estimated from leave-one out cross-validation (LOOCV) and Fisher Discriminant Analysis (FDA). High Density DNA microarrays and Real Time Quantitative PCR were employed for the generation and validation of the transcription profile of the oral cancer and normal samples. We identified 45 genes that are strongly correlated with malignancy. Of the 45 genes identified, six have been previously implicated in the disease, and two are uncharacterized clones.

Gene Expression Profiling↗

PubMed2002

Mapping physiological states from microarray expression measurements.

MOTIVATION: The increasing use of DNA microarrays to probe cell physiology requires methods for visualizing different expression phenotypes and explicitly connecting individual genes to discriminating expression features. Such methods should be robust and maintain biological interpretability. RESULTS: We propose a method for the mapping of the physiological state of cells and tissues from multidimensional expression data such as those obtained with DNA microarrays. The method uses Fisher discriminant analysis to create a linear projection of gene expression measurements that maximizes the separation of different sample classes. Relative to other typical classification methods, this method provides insights into the discriminating characteristics of expression measurements in terms of the contribution of individual genes to the definition of distinct physiological states. This projection method also facilitates visualization of classification results in a reduced dimensional space. Examples from four different cases demonstrate the ability of the method to produce well-separated groups in the projection space and to identify important genes for defining physiological states. The method can be augmented to also include data from the proteomic and metabolic phenotypes and can be useful in disease diagnosis, drug screening and bioprocessing applications.

Algorithms↗

PubMed2002

Determination of minimum sample size and discriminatory expression patterns in microarray data.

MOTIVATION: Transcriptional profiling using microarrays can reveal important information about cellular and tissue expression phenotypes, but these measurements are costly and time consuming. Additionally, tissue sample availability poses further constraints on the number of arrays that can be analyzed in connection with a particular disease or state of interest. It is therefore important to provide a method for the determination of the minimum number of microarrays required to separate, with statistical reliability, distinct disease states or other physiological differences. RESULTS: Power analysis was applied to estimate the minimum sample size required for two-class and multi-class discrimination. The power analysis algorithm calculates the appropriate sample size for discrimination of phenotypic subtypes in a reduced dimensional space obtained by Fisher discriminant analysis (FDA). This approach was tested by applying the algorithm to existing data sets for estimation of the minimum sample size required for drawing certain conclusions on multi-class distinction with statistical reliability. It was confirmed that when the minimum number of samples estimated from power analysis is used, group means in the FDA discrimination space are statistically different. CONTACT: gregstep@mit.edu

Acute Disease↗

PubMed2002

Interactive exploration of microarray gene expression patterns in a reduced dimensional space.

The very high dimensional space of gene expression measurements obtained by DNA microarrays impedes the detection of underlying patterns in gene expression data and the identification of discriminatory genes. In this paper we show the use of projection methods such as principal components analysis (PCA) to obtain a direct link between patterns in the genes and patterns in samples. This feature is useful in the initial interactive pattern exploration of gene expression data and data-driven learning of the nature and types of samples. Using oligonucleotide microarray measurements of 40 samples from different normal human tissues, we show that distinct patterns are obtained when the genes are projected on a two-dimensional plane spanned by the loadings of the two major principal components. These patterns define the particular genes associated with a sample class (i.e., tissue). When used separately from the other genes, these class-specific (i.e., tissue-specific) genes in turn define distinct tissue patterns in the projection space spanned by the scores of the two major principal components. In this study, PCA projection facilitated discriminatory gene selection for different tissues and identified tissue-specific gene expression signatures for liver, skeletal muscle, and brain samples. Furthermore, it allowed the classification of nine new samples belonging to these three types using the linear combination of the expression levels of the tissue-specific genes determined from the first set of samples. The application of the technique to other published data sets is also discussed.

Gene Expression Profiling↗

PubMedDate not supplied

Application of multivariate analysis to optimize function of cultured hepatocytes.

Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. To characterize intermediary metabolism in the hepatocytes, metabolic flux analysis (MFA) was applied to elucidate the changes in intracellular pathway fluxes of primary rat hepatocytes exposed to human plasma and to provide a comprehensive snapshot of the hepatic metabolic profile. In the current study, the combination of preconditioning and plasma supplementation produced distinct metabolic states. Combining the metabolic flux distribution obtained by MFA with methodologies such as Fisher discriminant analysis (FDA) and partial least squares or projection to latent structures (PLS) provided insights into the underlying structure and causal relationship within the data. With the aid of these analyses, patterns in the cellular response of the hepatocytes that contributed to the separation of the different hepatic states were identified. Of particular interest was the recognition of distal pathways that strongly correlated with a particular hepatic function. The hepatic functions investigated were intracellular triglyceride accumulation and urea production. This study illustrates a framework for optimizing hepatic function and a possibility of identifying potential targets for improving hepatic functions.

Algorithms↗