Published papers

Feiyanning formula (FYN) is a traditional Chinese medicine (TCM) prescription used for more than 20 years in the treatment of lung cancer. FYN is composed of Astragalus membranaceus, Polygonatum sibiricum, Atractylodes macrocephala, Cornus officinalis, Paris polyphylla, and Polistes olivaceous, etc. All of them have been proved to have anti-tumor effect. In this study, we used the TCM network pharmacological analysis to perform the collection of compound and disease target, the prediction of compound target and biological signal and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. It was found that the activation of mitochondrial pathway might be the molecular mechanism of the anti-lung cancer effect of FYN. The experimental results showed that FYN had an inhibitory effect on the growth of lung cancer cells in a dose-dependent and time-dependent manner. Moreover, FYN induced G2/M cell cycle arrest and apoptotic cell death as early as 6 h after treatment. In addition, FYN significantly induced mitochondrial membrane depolarization and increased calreticulin expression. Metabolomics analysis showed the increase of ATP utilization (assessed by a significant increase of the AMP/ATP and ADP/ATP ratio, necessary for apoptosis induction) and decrease of polyamines (that reflects growth potential). Taken together, our study suggested that FYN induced apoptosis of lung adenocarcinoma cells by promoting metabolism and changing the mitochondrial membrane potential, further supporting the validity of network pharmacological prediction.

Cancer cell lines are widely used in basic research to study cancer development, growth, invasion, or metastasis. They are also used for the development and screening of anticancer drugs. However, there are no clear criteria for choosing the most suitable cell lines among the wide variety of cancer cell lines commercially available for research, and the choice is often based on previously published reports. Here, we investigated the characteristics of liver cancer cell lines by analyzing the gene expression data available in the Cancer Cell Line Encyclopedia. Unsupervised clustering analysis of 28 liver cancer cell lines yielded two main clusters. One cluster showed a gene expression pattern similar to that of hepatocytes, and the other showed a pattern similar to that of fibroblasts. Analysis of hepatocellular carcinoma gene expression profiles available in The Cancer Genome Atlas showed that the gene expression patterns in most hepatoma tissues were similar to those in the hepatocytelike cluster. With respect to liver cancer research, our findings may be useful for selecting an appropriate cell line for a specific study objective. Furthermore, our approach of utilizing a public database for comparing the properties of cell lines could be an attractive cell line selection strategy that can be applied to other fields of research.

Plasma amino acids (AAs) have emerged as promising biomarkers for metabolic disorders, yet their causality remains unclear. We aimed to investigate the genetic determinants of AA levels in a cohort of 10,333 individuals and their causal effects on cardiometabolic traits using Mendelian randomization (MR). Plasma levels of 20 AAs were quantified using capillary electrophoresis mass spectrometry. Genome-wide association studies were conducted using BOLT-LMM and heritability estimation via LDSC analysis. Causal effects of AAs on 11 cardiometabolic traits were examined using two-sample MR analyses. We identified 85 locus-metabolite associations across 43 genes for 18 AAs, including 44 novel loci linked to metabolic genes. Heritability for AAs was estimated at 16%. MR analysis demonstrated cystine to positively associate with systolic blood pressure (SBP) (β = 0.056, SE = 0.010), while serine indicated protective effects on SBP (β = − 0.040, SE = 0.011), diastolic BP (β = − 0.044, SE = 0.010), and coronary artery disease (odds ratio 0.888, SE = 0.028). We identified potentially novel genetic loci associated with AA levels and demonstrated robust causal associations between several AAs and cardiometabolic traits. These findings reinforce the importance of AAs as potential biomarkers and therapeutic targets in cardiometabolic health.

Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking. Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS), which allows sequential 40-sample injection followed by electrophoretic separation and specific mass detection. To achieve consecutive analysis of polyamine samples, 1 M formic acid was used as the background electrolyte (BGE). The BGE spacer volume had an apparent effect on peak resolution among samples, and 20 nL was selected as the optimal volume. The use of polyamine isotopomers as the internal standard enabled the correction of matrix effects in MS detection. This method is sensitive, selective and quantitative, and its utility was demonstrated by screening polyamines in 359 salivary samples within 360 min, resulting in discrimination of colorectal cancer patients from noncancer controls.

Background: Asthma is characterized by phenotypes of different clinical, demographic, and pathological characteristics. Identifying the profile of exhaled volatile organic compounds (VOCs) in asthma phenotypes may facilitate establishing biomarkers and understanding asthma background pathogenesis. This study aimed to identify exhaled VOCs that characterize severe asthma phenotypes among patients with asthma. Methods: This was a multicenter cross-sectional study of patients with severe asthma in Japan. Clinical data were obtained from medical records, and questionnaires were collected. Exhaled breath was sampled and subjected to thermal desorption gas chromatography-mass spectrometry (GC/MS). Results: Using the decision tree established in the previous nationwide asthma cohort study, 245 patients with asthma were divided into five phenotypes and subjected to exhaled VOC analysis with 50 healthy controls (HCs). GC/MS detected 243 VOCs in exhaled breath samples, and 142 frequently detected VOCs (50% of all samples) were used for statistical analyses. Cluster analysis assigning the groups with similar VOC profile patterns showed the highest similarities between phenotypes 3 and 4 (early-onset asthma phenotypes), followed by the similarities between phenotypes 1 and 2 (late-onset asthma phenotypes). Comparisons between phenotypes 1–5 and HC revealed 19 VOCs, in which only methanesulfonic anhydride showed p < 0.05 adjusted by false discovery rate (FDR). Comparison of these phenotypes yielded several VOCs showing different trends (p < 0.05); however, no VOCs showed p < 0.05 adjusted by FDR. Conclusions: Exhaled VOC profiles may be useful for distinguishing asthma and asthma phenotypes; however, these findings need to be validated, and their pathological roles should be clarified.

Background and aims: Collecting clinical samples without inconveniencing participants is desirable. The profile of metabolites in mouth-rinsed water is similar to that in saliva. However, the intra- and inter-day variations in unstimulated or stimulated saliva metabolites from mouth-rinsed water have yet to be clarified. Thus, we aimed to fill this research gap using capillary electrophoresis–mass spectrometry metabolomics. Materials and methods: We collected mouth-rinsed water from 15 healthy participants at 9:00, 11:30, 14:00, and 16:30 daily for 3 days. In total, 509 metabolite concentrations from 180 samples were obtained using capillary electrophoresis time-of-flight mass spectrometry. Variations in each metabolite were evaluated using the Wilcoxon signed-rank test to determine at which time/day significant differences occurred after removing metabolites without significant changes using the Friedman test. Results: Of 167 frequently detected metabolites, 100 exhibited intra-day variations, and none exhibited inter-day variations. Intra-day variations were classified into four patterns, and the intra-day variation in each metabolite was assessed. The variations may reflect elapsed time after meals, oral cleaning, or circadian rhythms. Conclusion: This study could serve as a reference for improving the design of future clinical trials and the accuracy of metabolome analysis of mouth-rinsed water samples collected at different dates and times.

Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

Saliva is an ideal biofluid for monitoring oral and systemic health. Repeated mastication is a typical physical stimulus that improves salivary flow and oral hygiene. Recent metabolomic studies have shown the potential of salivary metabolomic components for various disease monitoring systems. Here, we evaluated the effect of long-term mastication on salivary metabolomic profiles. Young women with good oral hygiene (20.8 ± 0.3 years, n = 17) participated. They were prohibited from chewing gum during control periods (4 weeks each) and were instructed to chew a piece of gum base seven times a day for 10 min each time during the intervention period. Paired samples of unstimulated whole saliva collected on the last day of the control and intervention period were compared. Liquid chromatography–time-of-flight mass spectrometry successfully quantified 85 metabolites, of which 41 showed significant differences (p < 0.05, Wilcoxon paired test corrected by false discovery rate). Except for a few metabolites, such as citrate, most metabolites showed lower concentrations after the intervention. The pathways related to glycogenic amino acids, such as alanine, arginine, and glutamine, altered considerably. This study suggests that long-term mastication induces unstimulated salivary component-level changes.

Purpose: Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniques have not yet been applied extensively in studies focused on intraocular diseases. We evaluated whether 5 machine learning algorithms applied to the data of immune-mediator levels in aqueous humor can predict the actual diagnoses of 17 selected intraocular diseases and identified which immune mediators drive the predictive power of a machine learning model. Design: Cross-sectional study. Participants: Five hundred twelve eyes with diagnoses from among 17 intraocular diseases. Methods: Aqueous humor samples were collected, and the concentrations of 28 immune mediators were determined using a cytometric bead array. Each immune mediator was ranked according to its importance using 5 machine learning algorithms. Stratified k-fold cross-validation was used in evaluation of algorithms with the dataset divided into training and test datasets. Main Outcome Measures: The algorithms were evaluated in terms of precision, recall, accuracy, F-score, area under the receiver operating characteristic curve, area under the precision-recall curve, and mean decrease in Gini index. Results: Among the 5 machine learning models, random forest (RF) yielded the highest classification accuracy in multiclass differentiation of 17 intraocular diseases. The RF prediction models for vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma achieved the highest classification accuracy, precision, and recall. Random forest recognized vitreoretinal lymphoma, acute retinal necrosis, endophthalmitis, rhegmatogenous retinal detachment, and primary open-angle glaucoma with the top 5 F-scores. The 3 highest-ranking relevant immune mediators were interleukin (IL)-10, interferon-γ-inducible protein (IP)-10, and angiogenin for prediction of vitreoretinal lymphoma; monokine induced by interferon γ, interferon γ, and IP-10 for acute retinal necrosis; and IL-6, granulocyte colony-stimulating factor, and IL-8 for endophthalmitis. Conclusions: Random forest algorithms based on 28 immune mediators in aqueous humor successfully predicted the diagnosis of vitreoretinal lymphoma, acute retinal necrosis, and endophthalmitis. Overall, the findings of the present study contribute to increased knowledge on new biomarkers that potentially can facilitate diagnosis of intraocular diseases in the future.

Objective: This narrative review describes the recent developments and applications of machine learning (ML), a part of artificial intelligence, concerning breast cancer. Background: The advent of new bioinformatic approaches and artificial intelligence-based computational technologies has led to a shift in the decision-making of oncologists regarding breast cancer diagnostics and treatment processes. Various successful applications of ML on image processing, especially the use of deep neural networks and convolutional neural networks, to detect tumor and lymph nodes regions have been reported. Recent high-throughput molecular quantifications, i.e., quantitative omics techniques have enabled simultaneous monitoring of thousands of molecules to understand the molecular-level pathology. These data, including gene expression, protein, metabolite, and methylation profiling, have been analyzed via deep learning, network analysis, clustering, and dimension reductions to explore intrinsic subtypes and new biomarkers. Clinical-pathological features have been conducted by multivariable analysis to predict various outcomes, e.g., the sensitivity of adjuvant therapy and prognosis. The quantitative relationships among their variables have been visualized as nomograms. To analyze complex structures of a larger number of variables, ML combining multiple clinical-pathological features has been developed to predict the prognosis, metastasis, and treatment outcomes of breast cancer. Methods: We provided the narrative review of ML-related topics especially in the quantitative omics data and clinical-pathological prediction models. Conclusions: ML-based prediction methods are powerful tools and contribute to realizing personalized medicine for breast cancer.

Excessive tumour growth results in a hypoxic environment around cancer cells, thus inducing tumour angiogenesis, which refers to the generation of new blood vessels from pre-existing vessels. This mechanism is biologically and physically complex, with various mathematical simulation models proposing to reproduce its formation. However, although temporary vessel regression is clinically known, few models succeed in reproducing this phenomenon. Here, we developed a three-dimensional simulation model encompassing both angiogenesis and tumour growth, specifically including angiopoietin. Angiopoietin regulates both adhesion and migration between vascular endothelial cells and wall cells, thus inhibiting the cell-to-cell adhesion required for angiogenesis initiation. Simulation results showed a regression, i.e. transient decrease, in the overall length of new vessels during vascular network formation. Using our model, we also evaluated the efficacy of administering the drug bevacizumab. The results highlighted differences in treatment efficacy: (1) earlier administration showed higher efficacy in inhibiting tumour growth, and (2) efficacy depended on the treatment interval even with the administration of the same dose. After thorough validation in the future, these results will contribute to the design of angiogenesis treatment protocols.

Hepatic physiology depends on the liver's complex structural composition which among others, provides high oxygen supply rates, locally differential oxygen tension, endothelial paracrine signaling, as well as residual hemodynamic shear stress to resident hepatocytes. While functional improvements were shown by implementing these factors into hepatic culture systems, direct cause-effect relationships are often not well characterized—obfuscating their individual contribution in more complex microphysiological systems. By comparing increasingly complex hepatic in vitro culture systems that gradually implement these parameters, we investigate the influence of the cellular microenvironment to overall hepatic functionality in pharmacological applications. Here, hepatocytes were modulated in terms of oxygen tension and supplementation, endothelial coculture, and exposure to fluid shear stress delineated from oxygen influx. Results from transcriptomic and metabolomic evaluation indicate that particularly oxygen supply rates are critical to enhance cellular functionality—with cellular drug metabolism remaining comparable to physiological conditions after prolonged static culture. Endothelial signaling was found to be a major contributor to differential phenotype formation known as metabolic zonation, indicated by WNT pathway activity. Lastly, oxygen-delineated shear stress was identified to direct cellular fate towards increased hepatic plasticity and regenerative phenotypes at the cost of drug metabolic functionality – in line with regenerative effects observed in vivo. With these results, we provide a systematic evaluation of critical parameters and their impact in hepatic systems. Given their adherence to physiological effects in vivo, this highlights the importance of their implementation in biomimetic devices, such as organ-on-a-chip systems. Considering recent advances in basic liver biology, direct translation of physiological structures into in vitro models is a promising strategy to expand the capabilities of pharmacological models.

Background: Emerging evidence has shown that charged metabolites, such as amino acids, may play an important role in the pathogenesis of various metabolic disorders, many of which women in the postmenopausal period are at high risk of developing. This study examined the metabolic profile of middle-aged Japanese women to investigate alterations in charged metabolites induced by menopausal transition. Methods: The participants were 1193 female residents aged 40–60 at the baseline survey of the Tsuruoka Metabolomics Cohort Study. We investigated the cross-sectional association of menopausal status with 94 metabolomic biomarkers assayed in fasting plasma samples via capillary electrophoresis time-of-flight mass spectrometry using linear regression analysis. Results: Among the participants, 529 were premenopausal, 132 were in menopausal transition (MT), and 532 were postmenopausal. Significant differences were found in age, blood pressure, glucose and lipid levels, and smoking and drinking habits among the three groups. The concentrations of 5 metabolites in the MT group and 15 metabolites in the postmenopausal group were significantly higher than those in the premenopausal group after adjusting for confounding factors. When classified into pathways, these metabolites were related to the tricarboxylic cycle, urea cycle, and homocysteine metabolism, some of which are linked to arteriosclerosis. Conclusion: Multiple charged metabolites were associated with women's menopausal status, showing a gradual increase as women shifted from pre-, to peri-, to postmenopause. These findings might reflect the early changes behind the increased risk of dyslipidemia, diabetes, cardiovascular disease, and osteoporosis in later life.

Aims: Postoperative delirium (PD) is a common and severe complication in older adult patients undergoing invasive cancer resections. This study explored the plasma metabolome associated with PD and evaluated the efficacy of Yokukansan (YKS), a traditional Japanese Kampo medicine, in preventing PD. Methods: An ancillary study was conducted alongside a double-blind, placebo-controlled randomized clinical trial involving patients 65 years and older, focusing on patients older than 75 years as a primary analysis population. Plasma samples were analyzed using targeted and nontargeted metabolomics. An in vivo study using aged mice assessed the effects of YKS on plasma and brain metabolites. Results: A total of 83 patients, including 21 patients older than 75 years, were enrolled. Patients with PD had lower levels of several lipid mediators, free fatty acids, and phospholipids. YKS administration led to increased nine phospholipids and four hydrophilic metabolites in patients older than 75 years, including phosphatidylcholine (40:7) and phosphatidylcholine (42:8), which were also altered in delirium patients. In the patients older than 65 years, only two metabolites increased in the YKS administration group. In aged mice, YKS elevated plasma phospholipids, similar to findings in patients older than 75 years, and influenced brain citrulline and creatine, which related to oxidative stress and cognitive function. Correlation analyses revealed associations between plasma and brain metabolite changes. Conclusion: Our findings suggest that the plasma metabolome provides insight into the pathophysiology of PD and the potential mechanism underlying the preventive effect of YKS against PD.

The oxygen concentration in normal human tissue under physiologic conditions is lower than the atmospheric oxygen concentration. The more hypoxic condition has been observed in the cells with wound healing and cancer. Somatic stem cells reside in a hypoxic microenvironment in vivo and prefer hypoxic culture conditions in vitro. Oral mucosa contains tissue-specific stem cells, which is an excellent tissue source for regenerative medicine. For clinical usage, maintaining the stem cell in cultured cells is important. We previously reported that hypoxic culture conditions maintained primary oral keratinocytes in an undifferentiated and quiescent state and enhanced their clonogenicity. However, the metabolic mechanism of these cells is unclear. Stem cell biological and pathological findings have shown that metabolic reprogramming is important in hypoxic culture conditions, but there has been no report on oral mucosal keratinocytes and fibroblasts. Herein, we conducted metabolomic analyses of oral mucosal keratinocytes and fibroblasts under hypoxic conditions. Hypoxic oral keratinocytes and fibroblasts showed a drastic change of metabolite concentrations in urea cycle metabolites and polyamine pathways. The changes of metabolic profiles in glycolysis and the pentose phosphate pathway under hypoxic conditions in the oral keratinocytes were consistent with those of other somatic stem cells. The metabolic profiles in oral fibroblasts showed only little changes in any pathway under hypoxia except for a significant increase in the antioxidant 2-oxoglutaric acid. This report firstly provides the holistic changes of various metabolic pathways of hypoxic cultured oral keratinocytes and fibroblasts.

Patients with advanced cancer are frequently burdened with a severe sensation of fatigue called cancer-related fatigue (CRF). CRF is induced at various stages and treatments, such as cachexia and chemotherapy, and reduces the overall survival of patients. Objective and quantitative assessment of CRF could contribute to the diagnosis and prediction of treatment efficacy. However, such studies have not been intensively performed, particularly regarding metabolic profiles. Here, we conducted plasma metabolomics of 15 patients with urological cancer. The patients with and without fatigue, including those with cachexia or chemotherapy-induced fatigue, were compared. Significantly lower concentrations of valine and tryptophan were observed in fatigued patients than in non-fatigued patients. In addition, significantly higher concentrations of polyamine pathway metabolites were observed in patients with fatigue and cachexia than in those without cachexia. Patients with exacerbated fatigue due to chemotherapy showed significantly decreased cysteine and methionine metabolism before chemotherapy compared with those without fatigue exacerbation. These findings suggest that plasma metabolic profiles could help improve the diagnosis and monitoring of CRF.

Kidney biopsy is commonly used to diagnose kidney transplant dysfunction after transplantation. Therefore, the development of minimally invasive and quantitative methods to evaluate kidney function in transplant recipients is necessary. Here, we used capillary electrophoresis-mass spectrometry to analyze the biofluids collected from transplant recipients with impaired (Group I, n = 31) and stable (Group S, n = 19) kidney function and from donors (Group D, n = 9). Metabolomics analyses identified and quantified 97 metabolites in plasma, 133 metabolites in urine, and 108 metabolites in saliva. Multivariate analyses revealed apparent differences in the metabolomic profiles of the three groups. In plasma samples, arginine biosynthesis and purine metabolism between the I and S Groups differed. In addition, considerable differences in metabolomic profiles were observed between samples collected from participants with T cell-mediated rejection (TCR), antibody-mediated rejection, and other kidney disorders (KD). The metabolomic profiles in the three types of biofluids showed different patterns between TCR and KD, wherein 3-indoxyl sulfate showed a significant increase in TCR consistently in both plasma and urine samples. These results suggest that each biofluid has different metabolite features to evaluate kidney function after transplantation and that 3-indoxyl sulfate could predict acute rejection.

Workplace stress impacts productivity and health, necessitating non-invasive, rapid, and objective assessment methods. This study investigates the potential of metabolite profiling of mouth-rinsed water (MW)—an oral biofluid collected in just 10 s—as a screening tool for psychophysiological stress among office workers. Thirty-two participants were classified into high-stress and control groups based on the State-Trait Anxiety Inventory and the Brief Job Stress Questionnaire, and objective physiological measures. MW samples were collected at four time points, including before and after brief mental stress tasks. Biochemical features were profiled using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. A total of 559 analytes, including 532 water-soluble metabolites, 25 steroids, and 2 salivary proteins, were measured, of which 127 analytes with acceptable analytical precision were selected for subsequent analyses. Statistically significant differences in numerous metabolites indicated stress-associated alterations in metabolic activity. Notably, a predictive model using the ratio of N-acetyl-β-alanine to asymmetric dimethylarginine achieved high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.845 for identifying high stress. The distinct metabolite dynamics under varying stress conditions suggest multiple stress-response mechanisms. These findings underscore MW metabolite profiling as a promising approach for assessing and managing workplace stress.

Background: Heated tobacco products (HTPs) have gained global popularity, but their health risks remain unclear. Therefore, the current study aimed to identify plasma metabolites associated with smoking and HTP use in a large Japanese population to improve health risk assessment. Methods: Metabolomics data from 9,922 baseline participants of the Tsuruoka Metabolomics Cohort Study (TMCS) were analyzed to determine the association between smoking habits and plasma metabolites. Moreover, alterations in smoking-related metabolites among HTP users were examined based on data obtained from 3,334 participants involved from April 2018 to June 2019 in a follow-up survey. Results: Our study revealed that cigarette smokers had metabolomics profiles distinct from never smokers, with 22 polar metabolites identified as candidate biomarkers for smoking. These biomarker profiles of HTP users were closer to those of cigarette smokers than those of never smokers. The concentration of glutamate was higher in cigarette smokers, and biomarkers involved in glutamate metabolism were also associated with cigarette smoking and HTP use. Network pathway analysis showed that smoking was associated with the glutamate pathway, which could lead to endothelial dysfunction and atherosclerosis of the vessels. Conclusion: Our study showed that the glutamate pathway is affected by habitual smoking. These changes in the glutamate pathway may partly explain the mechanism by which cigarette smoking causes cardiovascular disease. HTP use was also associated with glutamate metabolism, indicating that HTP use may contribute to the development of cardiovascular disease through mechanisms similar to those in cigarette use.

Efficient utilization of alkaline extracts of several plants for the treatment of oral diseases has been reported. To investigate the mechanism of anti-inflammatory activity of alkaline extract of the leaves of Sasa sp. (SE), multi-omics analysis using metabolomics and DNA array was performed. Human gingival fibroblasts (HGFs) were treated for IL-1β to induce inflammation (detected by PGE2 production in culture medium) in the presence or absence of SE. Both IL-1β and SE showed slight hormetic growth stimulation against HGF. SE inhibited PGE2 production dose-and time-dependently. Its inhibitory action was more pronounced by first treating the cells with SE, rather than with IL-1β. At 3 h after IL-1β treatment, 18 amino acids (except cysteine and glutamic acid), total glutathione (GSH, GSSG, Cys-GSH disulfide), Met-sulfoxide, 5-oxoproline, and SAM declined, whereas DNA expressions of AKT, CASP3, and CXCL3 were elevated. These changes were reversed by simultaneous treatment with SE. The present study suggests that the anti-inflammatory action of SE is mediated via various metabolic pathways for cell survival, apoptosis, and leukocyte recruitment.