Discovery of azaleatin as a potential allosteric inhibitor for dengue NS2B-NS3 protease using in vitro and in silico studies.

The rise in dengue cases in tropical and sub-tropical areas has become a significant health concern. At present, there is no definitive cure for dengue fever, which underscores the importance of identifying potent inhibitors. Dengue NS2B-NS3 protease is the prime drug target due to its vital function for replication. Quercetin, a flavone, has anti-dengue virus properties but is limited by low bioavailability. Previous studies have shown that methoxy substitution in flavones improves bioavailability and metabolic stability. Azaleatin is a derivative of quercetin with a methoxy substitution at the C5 position, however its ability to inhibit dengue is unknown. In this study, azaleatin was investigated for its inhibition against dengue NS2B-NS3 protease using in vitro and in silico techniques. The fluorescence assay was used to determine the IC50 value and inhibition kinetics. The molecular interaction between azaleatin and NS2B-NS3 was studied using CB-Dock2 and AutoDock Vina. The complex's stability was then analysed using GROMACS. Besides, the ADMETlab 2.0 was utilized to predict pharmacokinetic of the azaleatin. Results showed that azaleatin inhibits dengue NS2B-NS3 protease non-competitively with a Ki of 26.82 µg/ml and an IC50 of 38 µg/ml. Molecular docking indicated binding of the azaleatin to the allosteric pocket of NS2B-NS3 with a docking score of -8.2 kcal/mol. Azaleatin was found stable in the pocket along 100 ns, supporting its inhibitory mode. The compound has favourable pharmacokinetic profiles and conformed to Lipinski's Rule of Five. Taken together, azaleatin inhibits NS2B-NS3 protease in a non-competitive mode, suggesting its potential as safer anti-dengue compound.Communicated by Ramaswamy H. Sarma.

Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease.

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging.

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics.

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Immune Checkpoint Molecules and Glucose Metabolism in HIV-Induced T Cell Exhaustion.

The progressive decline of CD8+ cytotoxic T cells in human immunodeficiency virus (HIV)-infected patients due to infection-triggered cell exhaustion and cell death is significantly correlated with disease severity and progression into the life-threatening acquired immunodeficiency syndrome (AIDS) stage. T cell exhaustion is a condition of cell dysfunction despite antigen engagement, characterized by augmented surface expression of immune checkpoint molecules such as programmed cell death protein 1 (PD-1), which suppress T cell receptor (TCR) signaling and negatively impact the proliferative and effector activities of T cells. T cell function is tightly modulated by cellular glucose metabolism, which produces adequate energy to support a robust reaction when battling pathogen infection. The transition of the T cells from an active to an exhausted state following pathogen persistence involves a drastic change in metabolic activity. This review highlights the interplay between immune checkpoint molecules and glucose metabolism that contributes to T cell exhaustion in the context of chronic HIV infection, which could deliver an insight into the rational design of a novel therapeutic strategy.

iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data.

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

Phytochemistry, Bioactivities and Traditional Uses of Michelia × alba.

Michelia × alba (M. alba) is a flowering tree best known for its essential oil, which has long been used as a fragrance ingredient for perfume and cosmetics. In addition, the plant has been used in traditional medicine in Asia and dates back hundreds of years. To date, there is a limited number of publications on the bioactivities of M. alba, which focused on its tyrosinase inhibition, antimicrobial, antidiabetic, anti-inflammatory, and antioxidant activities. Nevertheless, M. alba may have additional unexplored bioactivities associated with its bioactive compounds such as linalool (72.8% in flower oil and 80.1% in leaf oil), α-terpineol (6.04% flower oil), phenylethyl alcohol (2.58% flower oil), ß-pinene (2.39% flower oil), and geraniol (1.23% flower oil). Notably, these compounds have previously been reported to exhibit therapeutic activities such as anti-cancer, anti-inflammation, anti-depression, anti-ulcer, anti-hypertriglyceridemia, and anti-hypertensive activities. In this review paper, we examine and discuss the scientific evidence on the phytochemistry, bioactivities, and traditional uses of M. alba. Here, we report a total of 168 M. alba biological compounds and highlight the therapeutic potential of its key bioactive compounds. This review may provide insights into the therapeutic potential of M. alba and its biologically active components for the prevention and treatment of diseases and management of human health and wellness.

NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data.

In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.

Edible Bird's Nest: The Functional Values of the Prized Animal-Based Bioproduct From Southeast Asia-A Review.

Edible Bird's Nest (EBN) is the most prized health delicacy among the Chinese population in the world. Although some scientific characterization and its bioactivities have been studied and researched, no lights have been shed on its actual composition or mechanism. The aim of this review paper is to address the advances of EBN as a therapeutic animal bioproduct, challenges and future perspectives of research involving EBN. The methodology of this review primarily involved a thorough search from the literature undertaken on Web of Science (WoS) using the keyword "edible bird nest". Other information were obtained from the field/market in Malaysia, one of the largest EBN-producing countries. This article collects and describes the publications related to EBN and its therapeutic with diverse functional values. EBN extracts display anti-aging effects, inhibition of influenza virus infection, alternative traditional medicine in athletes and cancer patients, corneal wound healing effects, stimulation of proliferation of human adipose-derived stem cells, potentiate of mitogenic response, epidermal growth factor-like activities, enhancement of bone strength and dermal thickness, eye care, neuroprotective and antioxidant effects. In-depth literature study based on scientific findings were carried out on EBN and its properties. More importantly, the future direction of EBN in research and development as health-promoting ingredients in food and the potential treatment of certain diseases have been outlined.

Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis.

Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.

DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites.

Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.

Momordica charantia Suppresses Inflammation and Glycolysis in Lipopolysaccharide-Activated RAW264.7 Macrophages.

Macrophage activation is a key event that triggers inflammatory response. The activation is accompanied by metabolic shift such as upregulated glucose metabolism. There are accumulating evidences showing the anti-inflammatory activity of Momordica charantia. However, the effects of M. charantia on inflammatory response and glucose metabolism in activated macrophages have not been fully established. The present study aimed to examine the effect of M. charantia in modulating lipopolysaccharide (LPS)-induced inflammation and perturbed glucose metabolism in RAW264.7 murine macrophages. The results showed that LPS-induced NF-κB (p65) nuclear translocation was inhibited by M. charantia treatment. In addition, M. charantia was found to reduce the expression of inflammatory genes including IL6, TNF-α, IL1ß, COX2, iNOS, and IL10 in LPS-treated macrophages. Furthermore, the data showed that M. charantia reduced the expression of GLUT1 and HK2 genes and lactate production (-28%), resulting in suppression of glycolysis. Notably, its effect on GLUT1 gene expression was found to be independent of LPS-induced inflammation. A further experiment also indicated that the bioactivities of M. charantia may be attributed to its key bioactive compound, charantin. Taken together, the study provided supporting evidences showing the potential of M. charantia for the treatment of inflammatory disorders.

Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

1H NMR-Based Metabolic Profiles Delineate the Anticancer Effect of Vitamin C and Oxaliplatin on Hepatocellular Carcinoma Cells.

Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Because of its high recurrence rate and heterogeneity, effective treatment for advanced stage of HCC is currently lacking. There are accumulating evidences showing the therapeutic potential of pharmacologic vitamin C (VC) on HCC. However, the metabolic basis underlying the anticancer property of VC remains to be elucidated. In this study, we used a high-resolution proton nuclear magnetic resonance-based metabolomics technique to assess the global metabolic changes in HCC cells following VC treatment. In addition, the HCC cells were also treated with oxaliplatin (OXA) to explore the potential synergistic effect induced by the combined VC and OXA treatment. The current metabolomics data suggested different mechanisms of OXA and VC in modulating cell growth and metabolism. In general, VC treatment led to inhibition of energy metabolism via NAD+ depletion and amino acid deprivation. On the other hand, OXA caused significant perturbation in phospholipid biosynthesis and phosphatidylcholine biosynthesis pathways. The current results highlighted glutathione metabolism, and pathways related to succinate and choline may play central roles in conferring the combined effect between OXA and VC. Taken together, this study provided metabolic evidence of VC and OXA in treating HCC and may contribute toward the potential application of combined VC and OXA as complementary HCC therapies.

Metabolic Response in Rats following Electroacupuncture or Moxibustion Stimulation.

Electroacupuncture and moxibustion are traditional Chinese medicine practices that exert therapeutic effects through stimulation of specific meridian acupoints. However, the biological basis of the therapies has been difficult to establish; thus the current practices still rely on ancient TCM references. Here, we used a rat model to study perturbations in cortex, liver, and stomach metabolome and plasma hormones following electroacupuncture or moxibustion treatment on either stomach meridian or gallbladder meridian acupoints. All treatment groups, regardless of meridian and mode of treatment, showed perturbation in cortex metabolome and increased phenylalanine, tyrosine, and branched-chain amino acids in liver. In addition, electroacupuncture was found to increase ATP in cortex, creatine, and dimethylglycine in stomach and GABA in liver. On the other hand, moxibustion increased plasma enkephalin concentration, as well as betaine and fumarate concentrations in stomach. Furthermore, we had observed meridian-specific changes including increased N-acetyl-aspartate in liver and 3-hydroxybutyrate in stomach for gallbladder meridian stimulation and increased noradrenaline concentration in blood plasma following stimulation on stomach meridian. In summary, the current findings may provide insight into the metabolic basis of electroacupuncture and moxibustion, which may contribute towards new application of acupoint stimulation.

Deciphering the metabolic perturbation in hepatic alveolar echinococcosis: a 1H NMR-based metabolomics study.

BACKGROUND: Hepatic alveolar echinococcosis (HAE) is caused by the growth of Echinococcus multilocularis larvae in the liver. It is a chronic and potentially lethal parasitic disease. Early stage diagnosis for this disease is currently not available due to its long asymptomatic incubation period. In this study, a proton nuclear magnetic resonance (1H NMR)-based metabolomics approach was applied in conjunction with multivariate statistical analysis to investigate the altered metabolic profiles in blood serum and urine samples obtained from HAE patients. The aim of the study was to identify the metabolic signatures associated with HAE. RESULTS: A total of 21 distinct metabolic differences between HAE patients and healthy individuals were identified, and they are associated with perturbations in amino acid metabolism, energy metabolism, glyoxylate and dicarboxylate metabolism. Furthermore, the present results showed that the Fischer ratio, which is the molar ratio of branched-chain amino acids to aromatic amino acids, was significantly lower (P < 0.001) in the blood serum obtained from the HAE patients than it was in the healthy patient group. CONCLUSIONS: The altered Fischer ratio, together with perturbations in metabolic pathways identified in the present study, may provide new insights into the mechanistic understanding of HAE pathogenesis and potential therapeutic interventions.

Metabolomics analysis of herb-partitioned moxibustion treatment on rats with diarrhea-predominant irritable bowel syndrome.

BACKGROUND: Irritable bowel syndrome (IBS) is a common functional gastrointestinal disorder, which is commonly treated with antidiarrhoeal, antispasmodics, serotonergic agents or laxative agents. These treatments provide relief for IBS symptoms but may also lead to undesired side effects. Previously, herb-partitioned moxibustion (HPM) treatment has been demonstrated to be effective in ameliorating symptoms of IBS. However, the underlying mechanism of this beneficial treatment is yet to be established. The aim of the current study was to systematically assess the metabolic alterations in response to diarrhea-predominant IBS (IBS-D) and therapeutic effect of HPM. METHODS: Proton nuclear magnetic resonance spectroscopy (1H NMR)-based metabolomics approach was used to investigate fecal and serum metabolome of rat model of IBS-D with and without HPM treatment. RESULTS: The current results showed that IBS-induced metabolic alterations in fecal and serum sample include higher level of threonine and UDP-glucose together with lower levels of aspartate, ornithine, leucine, isoleucine, proline, 2-hydroxy butyrate, valine, lactate, ethanol, arginine, 2-oxoisovalerate and bile acids. These altered metabolites potentially involve in impaired gut secretory immune system and intestinal inflammation, malabsorption of nutrients, and disordered metabolism of bile acids. Notably, the HPM treatment was found able to normalize the Bristol stool forms scale scores, fecal water content, plasma endotoxin level, and a number of IBS-induced metabolic changes. CONCLUSIONS: These findings may provide useful insight into the molecular basis of IBS and mechanism of the HPM intervention.

Metabolic Effect of Dietary Taurine Supplementation on Nile Tilapia (Oreochromis nilotictus) Evaluated by NMR-Based Metabolomics.

Taurine is indispensable in aquatic diets that are based solely on plant protein, and it promotes growth of many fish species. However, the physiological and metabolome effects of taurine on fish have not been well described. In this study, 1H NMR-based metabolomics approaches were applied to investigate the metabolite variations in Nile tilapia (Oreochromis nilotictus) muscle in order to visualize the metabolic trajectory and reveal the possible mechanisms of metabolic effects of dietary taurine supplementation on tilapia growth. After extraction using aqueous and organic solvents, 19 taurine-induced metabolic changes were evaluated in our study. The metabolic changes were characterized by differences in carbohydrate, amino acid, lipid, and nucleotide contents. The results indicate that taurine supplementation could significantly regulate the physiological state of fish and promote growth and development. These results provide a basis for understanding the mechanism of dietary taurine supplementation in fish feeding. 1H NMR spectroscopy, coupled with multivariate pattern recognition technologies, is an efficient and useful tool to map the fish metabolome and identify metabolic responses to different dietary nutrients in aquaculture.

1H NMR-based Investigation of Metabolic Response to Electro-Acupuncture Stimulation.

Acupuncture is a traditional Chinese medicine therapy that has been found useful for treating various diseases. The treatments involve the insertion of fine needles at acupoints along specific meridians (meridian specificity). This study aims to investigate the metabolic basis of meridian specificity using proton nuclear magnetic resonance (1H NMR)-based metabolomics. Electro-acupuncture (EA) stimulations were performed at acupoints of either Stomach Meridian of Foot-Yangming (SMFY) or Gallbladder Meridian of Foot-Shaoyang (GMFS) in healthy male Sprague Dawley (SD) rats. 1H-NMR spectra datasets of serum, urine, cortex, and stomach tissue extracts from the rats were analysed by multivariate statistical analysis to investigate metabolic perturbations due to EA treatments at different meridians. EA treatment on either the SMFY or GMFS acupoints induced significant variations in 31 metabolites, e.g., amino acids, organic acids, choline esters and glucose. Moreover, a few meridian-specific metabolic changes were found for EA stimulations on the SMFY or GMFS acupoints. Our study demonstrated significant metabolic differences in response to EA stimulations on acupoints of SMFY and GMFS meridians. These results validate the hypothesis that meridian specificity in acupuncture is detectable in the metabolome and demonstrate the feasibility and effectiveness of a metabolomics approach in understanding the mechanism of acupuncture.