• Journal of Pharmaceutical Investigation
• /
• v.39 no.1
• /
• pp.43-49
• /
• 2009

Aspirin or acetylsalicylic acid, a member of the salicylate family, is frequently used as an analgesic, antipyretic, anti-inflammatory and antiplatelet drug. Because aspirin is chemically unstable in water and heat for tablet formulation, additives including lubricants are used in preparing aspirin tablets, using a dry-granulation process. Aspirin tablets are produced by a number of manufacturers which usually use their own unique combination of additives during the manufacturing process. In this study, we employed an NMR based metabolomics technique to identify the manufacturers of various aspirin tablets. Aspirin tablets from six different companies were analyzed by 1H 400 MHz NMR. The acquired data was then integrated and processed by principal component analysis (PCA). Based on the NMR data, we were able to identify peaks corresponding to acetylsalicylic acid in all of the six samples, whereas different NMR patterns were found in the aromatic and aliphatic regions depending on the unique additive used. These observations led to the conclusion that the differences in the NMR patterns among the different aspirin tablets were due to the presence of additives.

• Journal of Microbiology and Biotechnology
• /
• v.28 no.5
• /
• pp.784-795
• /
• 2018

Bacillus amyloliquefaciens Pc3 was isolated from Antarctic seawater with antifungal activity. In order to investigate the metabolic regulation mechanism in the biosynthesis of lipopeptides in B. amyloliquefaciens Pc3, GC/MS-based metabolomics was used when exogenous indole was added. The intracellular metabolite profiles showed decreased asparagine, aspartic acid, glutamine, glutamic acid, threonine, valine, isoleucine, hexadecanoic acid, and octadecanoic acid in the indole-treated groups, which were involved in the biosynthesis of lipopeptides. B. amyloliquefaciens Pc3 exhibited a growth promotion, bacterial total protein increase, and lipopeptide biosynthesis inhibition upon the addition of indole. Besides this, real-time PCR analysis further revealed that the transcription of lipopeptide biosynthesis genes ituD, fenA, and srfA-A were downregulated by indole with 22.4-, 21.98-, and 26.0-fold, respectively. It therefore was speculated that as the metabolic flux of most of the amino acids and fatty acids were transferred to the synthesis of proteins and biomass, lipopeptide biosynthesis was weakened owing to the lack of precursor amino acids and fatty acids.

• YAKHAK HOEJI
• /
• v.60 no.1
• /
• pp.29-35
• /
• 2016

Traditional Korean medicines may be managed more scientifically, through the development of logical criterion to verify their cultivation region. It contributes to advance the industry of traditional herbal medicines. Volatile compounds were obtained from 14 samples of domestic Taeksa and 30 samples of Chinese Taeksa by steam distillation. The metabolites were identified by NIST mass spectral library in the obtained gas chromatography/mass spectrometer (GC/MS) data of 35 training samples. The multivariate statistical analysis, such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), were performed based on the qualitative and quantitative data. Finally trans-(2,3-diphenylcyclopropyl)methyl phenyl sulfoxide (47.265 min), 1,2,3,4-tetrahydro-1-phenyl-naphthalene (47.781 min), spiro[4-oxatricyclo[5.3.0.0.(2,6)]decan-3-one-5,2'-cyclohexane] (54.62 min), 6-[7-nitrobenzofurazan-4-yl]amino-morphinan-4,5-epoxy (54.86 min), p-hydroxynorephedrine (55.14 min) were determined as marker metabolites to verify candidates for the origin of Taeksa. The statistical model was well established to determine the origin of Taeksa. The cultivation areas of test samples, each 3 domestic and 6 Chinese Taeksa were predicted by the established OPLS-DA model and it was confirmed that all 9 samples were precisely classified.

• Bulletin of the Korean Chemical Society
• /
• v.31 no.4
• /
• pp.825-828
• /
• 2010

To classify Glycyrrhiza species, samples of different species were analyzed by $^1H$ NMR-based metabolomics technique. Partial least squares discriminant analysis (PLS-DA) was used as the multivariate statistical analysis of the 1H NMR data sets. There was a clear separation between various Glycyrrhiza species in the PLS-DA derived score plots. The PLS-DA model was validated, and the key metabolites contributing to the separation in the score plots of various Glycyrrhiza species were lactic acid, alanine, arginine, proline, malic acid, asparagine, choline, glycine, glucose, sucrose, 4-hydroxy-phenylacetic acid, and formic acid. The compounds present at relatively high levels were glucose, and 4-hydroxyphenylacetic acid in G. glabra; lactic acid, alanine, and proline in G. inflata; and arginine, malic acid, and sucrose in G. uralensis. This is the first study to perform the global metabolomic profiling and differentiation of Glycyrrhiza species using $^1H$ NMR and multivariate statistical analysis.

• Bulletin of the Korean Chemical Society
• /
• v.34 no.5
• /
• pp.1467-1472
• /
• 2013

Metabolomics is a field that studies systematic dynamics and secretion of metabolites from cells to understand biological pathways based on metabolite changes. The metabolic profiling of intact human colorectal tissues was performed using high-resolution magic angle spinning (HR-MAS) NMR spectroscopy, which was unnecessary to extract metabolites from tissues. We used two different groups of samples, which were defined as normal and cancer, from 9 patients with colorectal cancer and investigated the samples in NMR experiments with a water suppression pulse sequence. We applied target profiling and multivariative statistical analysis to the analyzed 1D NMR spectra to identify the metabolites and discriminate between normal and cancer tissues. Cancer tissue showed higher levels of arginine, betaine, glutamate, lysine, taurine and lower levels of glutamine, hypoxanthine, isoleucine, lactate, methionine, pyruvate, tyrosine relative to normal tissue. In the OPLS-DA (orthogonal partial least square discriminant analysis), the score plot showed good separation between the normal and cancer groups. These results suggest that metabolic profiling of colorectal cancer could provide new biomarkers.

• Mass Spectrometry Letters
• /
• v.9 no.4
• /
• pp.91-94
• /
• 2018

Bilin tetrapyrroles are metabolic products of the breakdown of porphyrins within a species. In the case of mammals, these bilins are formed by the catabolism of heme and can be utilized as either biomarkers in disease or as an indicator of human waste contamination. Although a small subset of bilin tandem mass spectrometry reports exist, limited data is available in online databases for their fragmentation. The use of fragmentation data is important for metabolomics analyses to determine the identity of compounds detected within a sample. Therefore, in this study, the fragmentation of bilins generated by positive ion mode electrospray ionization is examined by collision-induced dissociation (CID) as a function of collision energy on an FT-ICR MS. The use of the FT-ICR MS allows for high mass accuracy measurements, and thus the formulas of resultant product ions can be ascertained. Based on our observations, fragmentation behavior for hemin, biliverdin and its dimethyl ester, phycocyanobilin, bilirubin, bilirubin conjugate, mesobilirubin, urobilin, and stercobilin are discussed in the context of the molecular structure and collision energy. This report provides insight into the identification of structures within this class of molecules for untargeted analyses.

• Food Science of Animal Resources
• /
• v.43 no.6
• /
• pp.1067-1086
• /
• 2023

With rapid advances in meat science in recent decades, changes in meat quality during the pre-slaughter phase of muscle growth and the post-slaughter process from muscle to meat have been investigated. Commonly used techniques have evolved from early physicochemical indicators such as meat color, tenderness, water holding capacity, flavor, and pH to various omic tools such as genomics, transcriptomics, proteomics, and metabolomics to explore fundamental molecular mechanisms and screen biomarkers related to meat quality and taste characteristics. This review highlights the application of omics and integrated multi-omics in meat quality and taste characteristics studies. It also discusses challenges and future perspectives of multi-omics technology to improve meat quality and taste. Consequently, multi-omics techniques can elucidate the molecular mechanisms responsible for changes of meat quality at transcriptome, proteome, and metabolome levels. In addition, the application of multi-omics technology has great potential for exploring and identifying biomarkers for meat quality and quality control that can make it easier to optimize production processes in the meat industry.

• Analytical Science and Technology
• /
• v.30 no.6
• /
• pp.355-378
• /
• 2017

Because neurochemicals are related to homeostasis and cognitive and behavioral functions in human body and because they enable the diagnosis of numerous neurodegenerative disorders, there has been increasing interest in the development of analytical platforms for neurochemical profiling in biological samples. In particular, mass spectrometry (MS)-based analytical methods combined with chromatographic separation have been widely used to profile neurochemicals in metabolic pathways. However, development of delicate sample preparation procedures and highly sensitive instrumental detection is necessary considering the trace levels and chemical instabilities of neurochemicals in biological samples. Therefore, in this review, analytical trends in MS-based metabolomics approaches to neurochemicals in multiple biological samples, such as urine, blood, CSF, and biological tissues, are discussed. This paper is expected to contribute to the development of an analytical platform to discover biomarkers that will aid diagnosis, prognosis, and treatment of neurodegenerative disorders.

• Biomolecules & Therapeutics
• /
• v.19 no.1
• /
• pp.38-44
• /
• 2011

Cisplatin is widely used for various types of cancers. However, its side effects, most notably, renal toxicity often limit its clinical utility. Although previous metabolomic studies reported possible toxicity markers, they used small number of animals and statistical approaches that may not perform best in the presence of intra-group variation. Here, we identified urinary biomarkers associated with renal toxicity induced by cisplatin using NMR-based metabolomics combined with Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA). Male Sprague-Dawley rats (n=22) were treated with cisplatin (10 mg/kg single dose), and the urines obtained before and after treatment were analyzed by NMR. Multivariable analysis of NMR data presented clear separation between non-treated and treated groups. The OPLS-DA statistical results revealed that 1,3-dimethylurate, taurine, glucose, glycine and branched-chain amino acid (isoleucine, leucine and valine) were significantly elevated in the treated group and that phenylacetylglycine and sarcosine levels were decreased in the treated group. To test the robustness of the approach, we built a prediction model for the toxicity and were able to predict all the unknown samples (n=14) correctly. We believe the proposed NMR-based metabolomics with OPLS-DA approach and the resulting urine markers can be used to augment the currently available blood markers.

• Genomics & Informatics
• /
• v.16 no.3
• /
• pp.52-58
• /
• 2018

In this report, we present a case study of how pharmacogenomics and pharmacometabolomics can be useful to characterize safety and pharmacokinetic profiles in early phase new drug development clinical trials. During conducting a first-in-human trial for a new molecular entity, we were able to determine the mechanism of dichotomized variability in plasma drug concentrations, which appeared closely related to adverse drug reactions (ADRs) through integrated omics analysis. The pharmacogenomics screening was performed from whole blood samples using the Affymetrix DMET (Drug-Metabolizing Enzymes and Transporters) Plus microarray, and confirmation of genetic variants was performed using real-time polymerase chain reaction. Metabolomics profiling was performed from plasma samples using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. A GSTM1 null polymorphism was identified in pharmacogenomics test and the drug concentrations was higher in GSTM1 null subjects than GSTM1 functional subjects. The apparent drug clearance was 13-fold lower in GSTM1 null subjects than GSTM1 functional subjects (p < 0.001). By metabolomics analysis, we identified that the study drug was metabolized by cysteinylglycine conjugation in GSTM functional subjects but those not in GSTM1 null subjects. The incidence rate and the severity of ADRs were higher in the GSTM1 null subjects than the GSTM1 functional subjects. Through the integrated omics analysis, we could understand the mechanism of inter-individual variability in drug exposure and in adverse response. In conclusion, integrated multi-omics analysis can be useful for elucidating the various characteristics of new drug candidates in early phase clinical trials.