Seongho Kim

Chronic Alcohol Exposure Stimulates Adipose Tissue Lipolysis in Mice: Role of Reverse Triglyceride Transport in the Pathogenesis of Alcoholic Steatosis

Alcohol consumption induces liver steatosis; therefore, this study investigated the possible role of adipose tissue dysfunction in the pathogenesis of alcoholic steatosis. Mice were pair-fed an alcohol or control liquid diet for 8 weeks to evaluate the alcohol effects on lipid metabolism at the adipose tissue-liver axis. Chronic alcohol exposure reduced adipose tissue mass and adipocyte size. Fatty acid release from adipose tissue explants was significantly increased in alcohol-fed mice in association with the activation of adipose triglyceride lipase and hormone-sensitive lipase. Alcohol exposure induced insulin intolerance and inactivated adipose protein phosphatase 1 in association with the up-regulation of phosphatase and tensin homolog (PTEN) and suppressor of cytokine signaling 3 (SOCS3). Alcohol exposure up-regulated fatty acid transport proteins and caused lipid accumulation in the liver. To define the mechanistic link between adipose triglyceride loss and hepatic triglyceride gain, mice were first administered heavy water for 5 weeks to label adipose triglycerides with deuterium, and then pair-fed alcohol or control diet for 2 weeks. Deposition of deuterium-labeled adipose triglycerides in the liver was analyzed using Fourier transform ion cyclotron mass spectrometry. Alcohol exposure increased more than a dozen deuterium-labeled triglyceride molecules in the liver by up to 6.3-fold. These data demonstrate for the first time that adipose triglycerides due to alcohol-induced hyperlipolysis are reverse transported and deposited in the liver.

Cytochrome P450 2D6 Activity Predicts Discontinuation of Tamoxifen Therapy in Breast Cancer Patients

The selective estrogen receptor modulator tamoxifen is routinely used for treatment and prevention of estrogen-receptor-positive breast cancer. Studies of tamoxifen adherence suggest that over half of patients discontinue treatment before the recommended 5 years. We hypothesized that polymorphisms in CYP2D6, the enzyme responsible for tamoxifen activation, predict for tamoxifen discontinuation. Tamoxifen-treated women (n=297) were genotyped for CYP2D6 variants and assigned a ‘score’ based on predicted allele activities from 0 (no activity) to 2 (high activity). Correlation between CYP2D6 score and discontinuation rates at 4 months was tested. We observed a strong nonlinear correlation between higher CYP2D6 score and increased rates of discontinuation (r2=0.935, P=0.018). These data suggest that presence of active CYP2D6 alleles may predict for higher likelihood of tamoxifen discontinuation. Therefore, patients who may be most likely to benefit from tamoxifen may paradoxically be most likely to discontinue treatment prematurely.

Understanding relationship between sequence and functional evolution in yeast proteins

The underlying relationship between functional variables and sequence evolutionary rates is often assessed by partial correlation analysis. However, this strategy is impeded by the difficulty of conducting meaningful statistical analysis using noisy biological data. A recent study suggested that the partial correlation analysis is misleading when data is noisy and that the principal component regression analysis is a better tool to analyze biological data. In this paper, we evaluate how these two statistical tools (partial correlation and principal component regression) perform when data are noisy. Contrary to the earlier conclusion, we found that these two tools perform comparably in most cases. Furthermore, when there is more than one ‘true’ independent variable, partial correlation analysis delivers a better representation of the data. Employing both tools may provide a more complete and complementary representation of the real data. In this light, and with new analyses, we suggest that protein length and gene dispensability play significant, independent roles in yeast protein evolution.

Heterogeneous Genomic Molecular Clocks in Primates

Using data from primates, we show that molecular clocks in sites that have been part of a CpG dinucleotide in recent past (CpG sites) and non-CpG sites are of markedly different nature, reflecting differences in their molecular origins. Notably, single nucleotide substitutions at non-CpG sites show clear generation-time dependency, indicating that most of these substitutions occur by errors during DNA replication. On the other hand, substitutions at CpG sites occur relatively constantly over time, as expected from their primary origin due to methylation. Therefore, molecular clocks are heterogeneous even within a genome. Furthermore, we propose that varying frequencies of CpG dinucleotides in different genomic regions may have contributed significantly to conflicting earlier results on rate constancy of mammalian molecular clock. Our conclusion that different regions of genomes follow different molecular clocks should be considered when inferring divergence times using molecular data and in phylogenetic analysis.

Wavelet- and Fourier-Transform-Based Spectrum Similarity Approaches to Compound Identification in Gas Chromatography/Mass Spectrometry

The high-throughput gas chromatography/mass spectrometry (GC/MS) technology offers a powerful means of analyzing a large number of chemical and biological samples. One of the important analyses of GC/MS data is compound identification. In this work, novel spectral similarity measures based on the discrete wavelet and Fourier transforms were proposed. The proposed methods are composite similarities that are composed of weighted intensities and wavelet/Fourier coefficients using cosine correlation. The performance of the proposed approaches along with the existing similarity measures was evaluated using the NIST Chemistry WebBook mass database maintained by the National Institute of Standards and Technology (NIST) as a library of reference spectra and repetitive mass spectral data as query spectra. The analysis results showed that the identification accuracies of the wavelet- and Fourier-transform-based methods were improved by 2.02% and 1.95%, respectively, compared to that of the weighted dot product (cosine correlation) and by 3.01% and 3.08%, respectively, compared to that of the composite similarity measure. The improved identification accuracy demonstrates that the proposed approaches outperformed the existing similarity measures in the literature.

MetSign: a computational platform for high-resolution mass spectrometry-based metabolomics.

Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.

An optimal peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry using mixture similarity measure

MOTIVATION Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) brings much increased separation capacity, chemical selectivity and sensitivity for metabolomics and provides more accurate information about metabolite retention times and mass spectra. However, there is always a shift of retention times in the two columns that makes it difficult to compare metabolic profiles obtained from multiple samples exposed to different experimental conditions. RESULTS The existing peak alignment algorithms for GC × GC-MS data use the peak distance and the spectra similarity sequentially and require predefined either distance-based window and/or spectral similarity-based window. To overcome the limitations of the current alignment methods, we developed an optimal peak alignment using a novel mixture similarity by employing the peak distance and the spectral similarity measures simultaneously without any variation windows. In addition, we examined the effect of the four different distance measures such as Euclidean, Maximum, Manhattan and Canberra distances on the peak alignment. The performance of our proposed peak alignment algorithm was compared with the existing alignment methods on the two sets of GC × GC-MS data. Our analysis showed that Canberra distance performed better than other distances and the proposed mixture similarity peak alignment algorithm prevailed against all literature reported methods. AVAILABILITY The data and software mSPA are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development.

A method of finding optimal weight factors for compound identification in gas chromatography–mass spectrometry

MOTIVATION The compound identification in gas chromatography-mass spectrometry (GC-MS) is achieved by matching the experimental mass spectrum to the mass spectra in a spectral library. It is known that the intensities with higher m/z value in the GC-MS mass spectrum are the most diagnostic. Therefore, to increase the relative significance of peak intensities of higher m/z value, the intensities and m/z values are usually transformed with a set of weight factors. A poor quality of weight factors can significantly decrease the accuracy of compound identification. With the significant enrichment of the mass spectral database and the broad application of GC-MS, it is important to re-visit the methods of discovering the optimal weight factors for high confident compound identification. RESULTS We developed a novel approach to finding the optimal weight factors only through a reference library for high accuracy compound identification. The developed approach first calculates the ratio of skewness to kurtosis of the mass spectral similarity scores among spectra (compounds) in a reference library and then considers a weight factor with the maximum ratio as the optimal weight factor. We examined our approach by comparing the accuracy of compound identification using the mass spectral library maintained by the National Institute of Standards and Technology. The results demonstrate that the optimal weight factors for fragment ion peak intensity and m/z value found by the developed approach outperform the current weight factors for compound identification. AVAILABILITY The results and R package are available at http://stage.louisville.edu/faculty/x0zhan17/software/ software-development.

Compound identification using partial and semipartial correlations for gas chromatography-mass spectrometry data.

Compound identification is a key component of data analysis in the applications of gas chromatography-mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and m/z value, respectively.

Association of Genotypes of the CYP3A Cluster with Midazolam Disposition In Vivo

The genes that encode for CYP3A4 and CYP3A5 are located in the same region (CYP3A cluster) on chromosome 7. Midazolam (MDZ) is a substrate for both CYP3A4 and CYP3A5. We hypothesize that MDZ disposition in vivo is associated with genotypes of the CYP3A cluster. A meta-analysis of the pharmacokinetic (PK) parameters from seven clinical trials was carried out, in which MDZ was administered both intravenously and orally. DNA samples were available from 116 patients. There were significant ethnic differences in the allelic frequencies of these four common single-nucleotide polymorphisms (SNPs) in the CYP3A cluster. Significant linkage disequilibrium was found between CYP3A5*3 and CYP3A4*1A in Caucasians, and between CYP3A5*1 and CYP3A4*1B in African Americans. There were no differences in MDZ disposition in vivo between different genotypes, haplotypes and diplotypes in the CYP3A cluster (P>0.05). No significant differences in MDZ PK parameters were observed between Caucasians and African Americans. Women had higher weight-corrected systemic and oral clearance than men, but dose-adjusted AUC and bioavailability differences were not observed between sexes. The clinical importance of elevated CYP3A activity in women remains to be determined. The rGCs of MDZ PK parameters were between 0.3 and 13.6%. In conclusion, the meta-analysis of seven studies suggests that environmental factors explain the majority of CYP3A activity variation. Further studies are necessary to define the functional significance of SNPs in the CYP3A cluster and the effects of CYP3A genotypes on MDZ disposition in vivo.