Kichun Lee

Metabolome-Wide Association Study of Neovascular Age-Related Macular Degeneration

Purpose To determine if plasma metabolic profiles can detect differences between patients with neovascular age-related macular degeneration (NVAMD) and similarly-aged controls. Methods Metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry (LC-FTMS) was performed on plasma samples from 26 NVAMD patients and 19 controls. Data were collected from mass/charge ratio (m/z) 85 to 850 on a Thermo LTQ-FT mass spectrometer, and metabolic features were extracted using an adaptive processing software package. Both non-transformed and log2 transformed data were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Orthogonal Partial Least Squares-Discriminant Analysis was performed to determine metabolic features that distinguished NVAMD patients from controls. Individual m/z features were matched to the Kyoto Encyclopedia of Genes and Genomes database and the Metlin metabolomics database, and metabolic pathways associated with NVAMD were identified using MetScape. Results Of the 1680 total m/z features detected by LC-FTMS, 94 unique m/z features were significantly different between NVAMD patients and controls using FDR (q = 0.05). A comparison of these features to those found with log2 transformed data (n = 132, q = 0.2) revealed 40 features in common, reaffirming the involvement of certain metabolites. Such metabolites included di- and tripeptides, covalently modified amino acids, bile acids, and vitamin D-related metabolites. Correlation analysis revealed associations among certain significant features, and pathway analysis demonstrated broader changes in tyrosine metabolism, sulfur amino acid metabolism, and amino acids related to urea metabolism. Conclusions These data suggest that metabolomic analysis can identify a panel of individual metabolites that differ between NVAMD cases and controls. Pathway analysis can assess the involvement of certain metabolic pathways, such as tyrosine and urea metabolism, and can provide further insight into the pathophysiology of AMD.

Serum Metabolomics of Slow vs. Rapid Motor Progression Parkinson’s Disease: a Pilot Study

Progression of Parkinson’s disease (PD) is highly variable, indicating that differences between slow and rapid progression forms could provide valuable information for improved early detection and management. Unfortunately, this represents a complex problem due to the heterogeneous nature of humans in regards to demographic characteristics, genetics, diet, environmental exposures and health behaviors. In this pilot study, we employed high resolution mass spectrometry-based metabolic profiling to investigate the metabolic signatures of slow versus rapidly progressing PD present in human serum. Archival serum samples from PD patients obtained within 3 years of disease onset were analyzed via dual chromatography-high resolution mass spectrometry, with data extraction by xMSanalyzer and used to predict rapid or slow motor progression of these patients during follow-up. Statistical analyses, such as false discovery rate analysis and partial least squares discriminant analysis, yielded a list of statistically significant metabolic features and further investigation revealed potential biomarkers. In particular, N8-acetyl spermidine was found to be significantly elevated in the rapid progressors compared to both control subjects and slow progressors. Our exploratory data indicate that a fast motor progression disease phenotype can be distinguished early in disease using high resolution mass spectrometry-based metabolic profiling and that altered polyamine metabolism may be a predictive marker of rapidly progressing PD.

High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring.

High-performance metabolic profiling (HPMP) by Fourier-transform mass spectrometry coupled to liquid chromatography gives relative quantification of thousands of chemicals in biologic samples but has had little development for use in toxicology research. In principle, the approach could be useful to detect complex metabolic response patterns to toxicologic exposures and to detect unusual abundances or patterns of potentially toxic chemicals. As an initial study to develop these possible uses, we applied HPMP and bioinformatics analysis to plasma of humans, rhesus macaques, marmosets, pigs, sheep, rats and mice to determine: (1) whether more chemicals are detected in humans living in a less controlled environment than captive species and (2) whether a subset of plasma chemicals with similar inter-species and intra-species variation could be identified for use in comparative toxicology. Results show that the number of chemicals detected was similar in humans (3221) and other species (range 2537-3373). Metabolite patterns were most similar within species and separated samples according to family and order. A total of 1485 chemicals were common to all species; 37% of these matched chemicals in human metabolomic databases and included chemicals in 137 out of 146 human metabolic pathways. Probability-based modularity clustering separated 644 chemicals, including many endogenous metabolites, with inter-species variation similar to intra-species variation. The remaining chemicals had greater inter-species variation and included environmental chemicals as well as GSH and methionine. Together, the data suggest that HPMP provides a platform that can be useful within human populations and controlled animal studies to simultaneously evaluate environmental exposures and biological responses to such exposures.

Bile Acid Aspiration Associated With Lung Chemical Profile Linked to Other Biomarkers of Injury After Lung Transplantation

Aspiration of gastrointestinal contents has been linked to worse outcomes following lung transplantation but uncertainty exists about underlying mechanisms. We applied high‐resolution metabolomics of bronchoalveolar lavage fluid (BALF) in patients with episodic aspiration (defined by bile acids in the BALF) to identify potential metabolic changes associated with aspiration. Paired samples, one with bile acids and another without, from 29 stable lung transplant patients were studied. Liquid chromatography coupled to high‐resolution mass spectroscopy was used to interrogate metabolomic contents of these samples. Data were obtained for 7068 ions representing intermediary metabolites, environmental agents and chemicals associated with microbial colonization. A substantial number (2302) differed between bile acid positive and negative samples when analyzed by false discovery rate at q = 0.01. These included pathways associated with microbial metabolism. Hierarchical cluster analysis defined clusters of chemicals associated with bile acid aspiration that were correlated to previously reported biomarkers of lung injury including T cell granzyme B level and the chemoattractants CXCL9 and CXCL10. These data specifically link bile acids presence in lung allografts to inflammatory pathways known to segregate with worsening allograft outcome, and provide additional mechanistic insight into the association between reflux and lung allograft injury.

A Wavelet‐Based Spectral Method for Extracting Self‐Similarity Measures in Time‐Varying Two‐Dimensional Rainfall Maps

Many environmental time‐evolving spatial phenomena are characterized by a large number of energetic modes, the occurrence of irregularities, and self‐organization over a wide range of space or time scales. Precipitation is a classical example characterized by both strong intermittency and multiscale dynamics, and these features generate persistence, long‐range dependence, and extremes (whether be it droughts or extreme floods). Over the last two decades, time‐frequency or time‐scale transforms have become indispensable tools in the analysis of such phenomena and, as a consequence, a number of wavelet‐based spectral methods are now routinely employed to estimate Hurst exponents and other measures of regularity and scaling. In this article, an ensemble of new wavelet‐based spectral tools for analysis of 2‐D images is proposed. The new scale‐mixing wavelet spectrum is applied to the analysis of time sequences of two‐dimensional spatial rainfall radar images characterized by either convective or frontal systems. Intermittent spatial patterns connected to the precipitation‐formation mechanisms were encoded in low‐dimensional informative descriptors appropriate for classification, discrimination analyses and possible integration with climate models. We found that convective rainfall spatial patterns compared to frontal patterns produce spectral signatures consistent with their generation mechanism.

A probability-based trust prediction model using trust-message passing

We propose a probability-based trust prediction model based on trust-message passing which takes advantage of the two kinds of information: an explicit information and an implicit information.

Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data

We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets.

Opinion mining using ensemble text hidden Markov models for text classification

Proposed a new sentiment analysis method, based on text-based hidden Markov models, that uses word orders without the need of sentiment lexicons.Proposed an ensemble of text-based hidden Markov models using boosting and clusters of words produced by latent semantic analysis.Showed the method has potential to classify implicit opinions by the proposed ensemble method.Showed better performance in comparison to several previous algorithms in several datasets.Applied it to a real-life dataset to classify paper titles. With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews.

A Sulfur Amino Acid–Free Meal Increases Plasma Lipids in Humans

The content of sulfur amino acid (SAA) in a meal affects postprandial plasma cysteine concentrations and the redox potential of cysteine/cystine. Because such changes can affect enzyme, transporter, and receptor activities, meal content of SAA could have unrecognized effects on metabolism during the postprandial period. This pilot study used proton NMR ((1)H-NMR) spectroscopy of human plasma to test the hypothesis that dietary SAA content changes macronutrient metabolism. Healthy participants (18-36 y, 5 males and 3 females) were equilibrated for 3 d to adequate SAA, fed chemically defined meals without SAA for 5 d (depletion), and then fed isoenergetic, isonitrogenous meals containing 56 mg·kg(-1)·d(-1) SAA for 4.5 d (repletion). On the first and last day of consuming the chemically defined meals, a morning meal containing 60% of the daily food intake was given and plasma samples were collected over an 8-h postprandial time course for characterization of metabolic changes by (1)H-NMR spectroscopy. SAA-free food increased peak intensity in the plasma (1)H-NMR spectra in the postprandial period. Orthogonal signal correction/partial least squares-discriminant analysis showed changes in signals associated with lipids, some amino acids, and lactate, with notable increases in plasma lipid signals (TG, unsaturated lipid, cholesterol). Conventional lipid analyses confirmed higher plasma TG and showed an increase in plasma concentration of the lipoprotein lipase inhibitor, apoC-III. The results show that plasma (1)H-NMR spectra can provide useful macronutrient profiling following a meal challenge protocol and that a single meal with imbalanced SAA content alters postprandial lipid metabolism.

Metabolic effects of albumin therapy in acute lung injury measured by proton nuclear magnetic resonance spectroscopy of plasma: a pilot study.

Objective:Improved means to monitor and guide interventions could be useful in the intensive care unit. Metabolomic analysis with bioinformatics is used to understand mechanisms and identify biomarkers of disease development and progression. This pilot study evaluated plasma proton nuclear magnetic resonance spectroscopy as a means to monitor metabolism following albumin administration in acute lung injury patients. Design:This study was conducted on plasma samples from six albumin-treated and six saline-treated patients from a larger double-blind trial. The albumin group was administered 25 g of 25% human albumin in 0.9% saline every 8 hrs for a total of nine doses over 72 hrs. A 0.9% concentration of saline was used as a placebo. Blood samples were collected immediately before, 1 hr after, and 4 hrs after the albumin/saline administration for the first, fourth, and seventh doses (first dose of each day for 3 days). Samples were analyzed by proton nuclear magnetic resonance spectroscopy, and spectra were analyzed by principal component analysis and biostatistical methods. Interventions:None. Measurements and Main Results:After 1 day of albumin therapy, changes in small molecules, including amino acids and plasma lipids, were evident with principal component analysis. Differences remained 3 days after the last albumin administration. Analysis of data along with spectra from healthy controls showed that spectra for patients receiving albumin had a trajectory toward the spectra observed for healthy individuals while those of the placebo controls did not. Conclusion:The data suggest that metabolic changes detected by proton nuclear magnetic resonance spectroscopy and the bioinformatics tool may be a useful approach to clinical research, especially in acute lung injury.