Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

An untargeted comparative metabolomics analysis of infants with and without late-onset breast milk jaundice

BACKGROUND

Late-onset breast milk jaundice (LBMJ) is a common form of hyperbilirubinemia, which can result in serious complications for newborns with persistently high bilirubin levels. The aim of this study was to investigate the differences in fecal metabolites between breastfed infants with and without LBMJ in order to elucidate potential biological mechanisms.

METHODS

Biological samples were collected from 12 infants with LBMJ and 12 healthy individuals. Ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF/MS) was utilized for non-targeted determination of fecal metabolites. Principal components analysis (PCA), cluster analysis, and differential metabolite analysis were performed in both positive ion mode and negative ion mode for the two groups. Additionally, the KEGG database was employed to comprehensively analyze the pathways of differential metabolites.

RESULTS

There were no significant differences in maternal and neonatal demographic characteristics between the two groups (p > 0.05). The results of PCA and cluster heat map analysis in both modes showed that there were significant differences in metabolites between the two groups. Among 751 differential metabolites (DMs) detected in positive ion mode, 720 were up-regulated in the case group while 31 were down-regulated. In negative ion mode, 1891 DMs were detected, including 817 up-regulated metabolites and 1074 down-regulated metabolites in the case group. Analysis of differential metabolic pathways showed that the DMs of the two groups were mainly annotated and enriched in Biotin metabolism, N-Glycan biosynthesis, Taurine and hypotaurine metabolism, Pyrimidine metabolism, and Pentose and glucuronate interconversions.

CONCLUSION

Significant differences exist in fecal metabolites between LBMJ infants and healthy controls. The study of differential metabolic pathways provides insights into the mechanism of LBMJ.

Comparative Analysis of Breast Cancer Metabolomes Highlights Fascin's Central Role in Regulating Key Pathways Related to Disease Progression

Omics technologies provide useful tools for the identification of novel biomarkers in many diseases, including breast cancer, which is the most diagnosed cancer in women worldwide. We and others have reported a central role for the actin-bundling protein (fascin) in regulating breast cancer disease progression at different levels. However, whether fascin expression promotes metabolic molecules that could predict disease progression has not been fully elucidated. Here, fascin expression was manipulated via knockdown (fascinKD+NORF) and rescue (fascinKD+FORF) in the naturally fascin-positive (fascinpos+NORF) MDA-MB-231 breast cancer cells. Whether fascin dysregulates metabolic profiles that are associated with disease progression was assessed using untargeted metabolomics analyses via liquid chromatography-mass spectrometry. Overall, 12,226 metabolic features were detected in the tested cell pellets. Fascinpos+NORF cell pellets showed 2510 and 3804 significantly dysregulated metabolites compared to their fascinKD+NORF counterparts. Fascin rescue (fascinKD+FORF) revealed 2710 significantly dysregulated cellular metabolites compared to fascinKD+NORF counterparts. A total of 101 overlapped cellular metabolites between fascinKD+FORF and fascinpos+NORF were significantly dysregulated in the fascinKD+NORF cells. Analysis of the significantly dysregulated metabolites by fascin expression revealed their involvement in the metabolism of sphingolipid, phenylalanine, tyrosine, and tryptophan biosynthesis, and pantothenate and CoA biosynthesis, which are critical pathways for breast cancer progression. Our findings of fascin-mediated alteration of metabolic pathways could be used as putative poor prognostic biomarkers and highlight other underlying mechanisms of fascin contribution to breast cancer progression.

A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats

Cardiac hypertrophy (CH) is one of the stages in the occurrence and development of severe cardiovascular diseases, and exploring its biomarkers is beneficial for delaying the progression of severe cardiovascular diseases. In this research, we established a comprehensive and highly efficient pseudotargeted metabolomics method, which demonstrated a superior capacity to identify differential metabolites when compared to traditionaluntargeted metabolomics. The intra/inter-day precision and reproducibility results proved the method is reliable and precise. The established method was then applied to seek the potential differentiated metabolic biomarkers of cardiac hypertrophy (CH) rats, and oxylipins, phosphorylcholine (PC), lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), Krebs cycle intermediates, carnitines, amino acids, and bile acids were disclosed to be the possible differentiate components. Their metabolic pathway analysis revealed that the potential metabolic alterations in CH rats were mainly associated with phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and tyrosine metabolism. In sum, this research provided a comprehensiveand reliable LC-MS/MS MRM platform for pseudo-targeted metabolomics investigation of disease condition, and some interesting potential biomarkers were disclosed for CH, which merit further exploration in the future.

Chinmedomics: a potent tool for the evaluation of traditional Chinese medicine efficacy and identification of its active components

As an important part of medical science, Traditional Chinese Medicine (TCM) attracts much public attention due to its multi-target and multi-pathway characteristics in treating diseases. However, the limitations of traditional research methods pose a dilemma for the evaluation of clinical efficacy, the discovery of active ingredients and the elucidation of the mechanism of action. Therefore, innovative approaches that are in line with the characteristics of TCM theory and clinical practice are urgently needed. Chinmendomics, a newly emerging strategy for evaluating the efficacy of TCM, is proposed. This strategy combines systems biology, serum pharmacochemistry of TCM and bioinformatics to evaluate the efficacy of TCM with a holistic view by accurately identifying syndrome biomarkers and monitoring their complex metabolic processes intervened by TCM, and finding the agents associated with the metabolic course of pharmacodynamic biomarkers by constructing a bioinformatics-based correlation network model to further reveal the interaction between agents and pharmacodynamic targets. In this article, we review the recent progress of Chinmedomics to promote its application in the modernisation and internationalisation of TCM.

The effect and mechanism of Qingre Huashi formula in the treatment of chronic hepatitis B with Gan-dan-shi-Re syndrome: An integrated transcriptomic and targeted metabolomic analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Qingre Huashi (QRHS) formula is an empirical prescription for the treatment of Gan-Dan-Shi-Re syndrome (GDSR) syndrome in traditional Chinese medicine (TCM). GDSR is one of the typical TCM syndromes in chronic hepatitis B (CHB). However, little is known about the mechanism of the QRHS formula in treating CHB patients with GDSR. The biological basis of GDSR also remains largely unknown.

AIM OF THE STUDY

GDSR mostly occurs in the acute and early stages of chronic liver disease. Effectively alleviating GDSR stalls disease development and benefits patients. The purpose of this study was to explore the molecular basis of GDSR in CHB and then study the mechanism of the QRHS formula treating GDSR using transcriptomics and metabolomics.

MATERIALS AND METHODS

The transcriptome and metabolome of CHB patients with GDSR syndrome were detected using RNA microarray combined with ultra-high performance liquid chromatography/mass spectrometry and information mining. The potential biomarkers were identified from differentially expressed genes and metabolites, and the metabolic pathway was analyzed. We also investigated the callback of metabolic biomarkers after treatment with the QRHS formula, an empirical prescription for the treatment of GDSR syndrome. RT-PCR analysis was carried out in an independent patient cohort of CHB for validation.

RESULTS

Four candidate genes-GPT2, HK2, DDIT3, and HIF1A-and 14 candidate metabolic biomarkers, including L-alpha-aminobutyric acid, selenomethionine, and fructose 1,6-bisphosphate, were identified and validated. All four transcripts of GPT2, HK2, DDIT3, and HIF1A were significantly differentially expressed between the GDSR and non-GDSR groups through independent microarray data and RT-PCR. After treatment with the QRHS formula, the clinical indexes and TCM syndrome were significantly improved, and the 14 disturbed biomarkers were obviously corrected. Three metabolic pathways were confirmed to be perturbed in CHB GDSR patients: alanine, aspartate, and glutamate metabolism, arginine biosynthesis, and aminoacyl-tRNA biosynthesis.

CONCLUSION

Using integrated transcriptomic and targeted metabolomic methods, we identified the potential biomarkers and dysregulated metabolic pathways in CHB patients with GDSR syndrome, which was alleviated by the QRHS formula treatment. These results may provide the mechanism of metabolic dysregulation in GDSR syndrome as well as that underlying the curative effect of the QRHS formula.

Transcriptomic and Metabolomic Analysis of Liver Cirrhosis

BACKGROUND

Liver cirrhosis is one of the leading causes of decreased life expectancy worldwide. However, the molecular mechanisms underlying liver cirrhosis remain unclear. In this study, we performed a comprehensive analysis using transcriptome and metabolome sequencing to explore the genes, pathways, and interactions associated with liver cirrhosis.

METHODS

We performed transcriptome and metabolome sequencing of blood samples from patients with cirrhosis and healthy controls (1:1 matched for sex and age). We validated the differentially expressed microRNA (miRNA) and mRNAs using real-time quantitative polymerase chain reaction.

RESULTS

For transcriptome analysis, we screened for differentially expressed miRNAs and mRNAs, analyzed mRNAs to identify possible core genes and pathways, and performed coanalysis of miRNA and mRNA sequencing results. In terms of the metabolome, we screened five pathways that were substantially enriched in the differential metabolites. Next, we identified the metabolites with the most pronounced differences among these five metabolic pathways. We performed receiver operating characteristic (ROC) curve analysis of these five metabolites to determine their diagnostic efficacy for cirrhosis. Finally, we explored possible links between the transcriptome and metabolome.

CONCLUSION

Based on sequencing and bioinformatics, we identified miRNAs and genes that were differentially expressed in the blood of patients with liver cirrhosis. By exploring pathways and disease-specific networks, we identified unique biological mechanisms. In terms of metabolomes, we identified novel biomarkers and explored their diagnostic efficacy. We identified possible common pathways in the transcriptome and metabolome that could serve as candidates for further studies.

The metabolic fingerprint of chronic hepatitis C progression: Metabolome shifts and cutting-edge diagnostic options

Hepatitis C virus infection causes chronic diseases such as cirrhosis and hepatocellular carcinoma. Metabolomics research has been shown to be linked to pathophysiologic pathways in liver illnesses. The aim of this study was to investigate the serum metabolic profile of patients with chronic hepatitis C (CHC) infection and to identify underlying mechanisms as well as potential biomarkers associated with the disease. Nuclear magnetic resonance (NMR) was used to evaluate the sera of 83 patients with CHC virus and 52 healthy control volunteers (NMR). Then, multivariate statistical analysis was used to find distinguishing metabolites between the two groups. Sixteen out of 40 metabolites including include 3-HB, betaine, carnitine, creatinine, fucose, glutamine, glycerol, isopropanol, lysine, mannose, methanol, methionine, ornithine, proline, serine, and valine-were shown to be significantly different between the CHC and normal control (NC) groups (variable importance in projection >1 and p < 0.05). All the metabolic perturbations in this disease are associated with pathways of Glycine, serine, and threonine metabolism, glycerolipid metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate, and glutamate metabolism. Multivariate statistical analysis constructed using these expressed metabolites showed CHC patients can be discriminated from NCs with high sensitivity (90%) and specificity (99%). The metabolomics approach may expand the diagnostic armamentarium for patients with CHC while contributing to a comprehensive understanding of disease mechanisms.

Metabolomics Point out the Effects of Carfilzomib on Aromatic Amino Acid Biosynthesis and Degradation

(1) Carfilzomib (Cfz) is an antineoplastic agent indicated for the treatment of multiple myeloma. However, its beneficial action is attenuated by the occurrence of cardiotoxicity and nephrotoxicity as the most common adverse effects. Presently, there is well-established knowledge on the pathomechanisms related to these side effects; however, the research on the metabolic alterations provoked by the drug is limited. (2) An in vivo simulation of Cfz-induced toxicity was developed in (i) Cfz-treated and (ii) control mice. An RP-HRMS-based protocol and an advanced statistical treatment were used to investigate the impact of Cfz on the non-polar metabolome. (3) The differential analysis classified the Cfz-treated and control mice and resulted in a significant number of identified biomarkers with AUC > 0.9. The drug impaired the biosynthesis and degradation of aromatic amino acids (AAA) and led to alterations of uremic toxins in the renal and urine levels. Furthermore, the renal degradation of tryptophan was affected, inducing its degradation via the kynurenine pathway. (4) The renal levels of metabolites showed impaired excretion and degradation of AAAs. Cfz was, finally, correlated with the biosynthesis of renal dopamine, explaining the biochemical causes of water and ion retention and the increase in systolic pressure.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Metabolomic Analysis of Multiple Biological Specimens (Feces, Serum, and Urine) by 1H-NMR Spectroscopy from Dairy Cows with Clinical Mastitis

Due to huge economic losses to the dairy industry worldwide, mastitis can be considered as one of the most common diseases in dairy cows. This work aimed to study this disease by comparing multiple biological specimens (feces, serum, and urine) from individuals with or without clinical mastitis. This was performed by a single analytical platform, namely ¹H-NMR, through a multi-matrix strategy. Thanks to the high reproducibility of ¹H-NMR, we could characterize 120 molecules across dairy cow feces, serum, and urine. Among them, 23 molecules were in common across the three biofluids. By integrating the results of multi-matrix metabolomics, several pathways pertaining to energy metabolism and amino acid metabolism appeared to be affected by clinical mastitis. The present work wished to deepen the understanding of dairy cow mastitis in its clinical form. Simultaneous analysis of metabolome changes across several key biofluids could facilitate knowledge discovery and the reliable identification of potential biomarkers, which could be, in turn, used to shed light on the early diagnosis of dairy cow mastitis in its subclinical form.

An Untargeted Metabolomics Approach on Carfilzomib-Induced Nephrotoxicity

BACKGROUND

Carfilzomib (Cfz) is an anti-cancer drug related to cardiorenal adverse events, with cardiovascular and renal complications limiting its clinical use. Despite the important progress concerning the discovery of the underlying causes of Cfz-induced nephrotoxicity, the molecular/biochemical background is still not well clarified. Furthermore, the number of metabolomics-based studies concerning Cfz-induced nephrotoxicity is limited.

METHODS

A metabolomics UPLC-HRMS-DIA methodology was applied to three bio-sample types i.e., plasma, kidney, and urine, obtained from two groups of mice, namely (i) Cfz (8 mg Cfz/ kg) and (ii) Control (0.9% NaCl) (n = 6 per group). Statistical analysis, involving univariate and multivariate tools, was applied for biomarker detection. Furthermore, a sub-study was developed, aiming to estimate metabolites' correlation among bio-samples, and to enlighten potential mechanisms.

RESULTS

Cfz mostly affects the kidneys and urine metabolome. Fifty-four statistically important metabolites were discovered, and some of them have already been related to renal diseases. Furthermore, the correlations between bio-samples revealed patterns of metabolome alterations due to Cfz.

CONCLUSIONS

Cfz causes metabolite retention in kidney and dysregulates (up and down) several metabolites associated with the occurrence of inflammation and oxidative stress.

Integrated spatially resolved metabolomics and network toxicology to investigate the hepatotoxicity mechanisms of component D of Polygonum multiflorum Thunb

ETHNOPHARMACOLOGICAL RELEVANCE

The liver toxicity of Reynoutria multiflora (Thunb.) Moldenke. (Polygonaceae) (Polygonum multiflorum Thunb, PM) has always attracted much attention, but the related toxicity materials and mechanisms have not been elucidated due to multi-component and multi-target characteristics. In previous hepatotoxicity screening, different components of PM were first evaluated and the hepatotoxicity of component D [95% ethanol (EtOH) elution] in a 70% EtOH extract of PM (PM-D) showed the highest hepatotoxicity. Furthermore, the main components of PM-D were identified and their hepatotoxicity was evaluated based on a zebrafish embryo model. However, the hepatotoxicity mechanism of PM-D is unknown.

AIM OF THE STUDY

This work is to explore the hepatotoxicity mechanisms of PM-D by integrating network toxicology and spatially resolved metabolomics strategy.

MATERIALS AND METHODS

A hepatotoxicity interaction network of PM-D was constructed based on toxicity target prediction for eight key toxic ingredients and a hepatotoxicity target collection. Then the key signaling pathways were enriched, and molecular docking verification was implemented to evaluate the ability of toxic ingredients to bind to the core targets. The pathological changes of liver tissues and serum biochemical assays of mice were used to evaluate the liver injury effect of mice with oral administration of PM-D. Furthermore, spatially resolved metabolomics was used to visualize significant differences in metabolic profiles in mice after drug administration, to screen hepatotoxicity-related biomarkers and analyze metabolic pathways.

RESULTS

The contents of four key toxic compounds in PM-D were detected. Network toxicology identified 30 potential targets of liver toxicity of PM-D. GO and KEGG enrichment analyses indicated that the hepatotoxicity of PM-D involved multiple biological activities, including cellular response to endogenous stimulus, organonitrogen compound metabolic process, regulation of the apoptotic process, regulation of kinase, regulation of reactive oxygen species metabolic process and signaling pathways including PI3K-Akt, AMPK, MAPK, mTOR, Ras and HIF-1. The molecular docking confirmed the high binding activity of 8 key toxic ingredients with 10 core targets, including mTOR, PIK3CA, AKT1, and EGFR. The high distribution of metabolites of PM-D in the liver of administrated mice was recognized by mass spectrometry imaging. Spatially resolved metabolomics results revealed significant changes in metabolic profiles after PM-D administration, and metabolites such as taurine, taurocholic acid, adenosine, and acyl-carnitines were associated with PM-D-induced liver injury. Enrichment analyses of metabolic pathways revealed tht linolenic acid and linoleic acid metabolism, carnitine synthesis, oxidation of branched-chain fatty acids, and six other metabolic pathways were significantly changed. Comprehensive analysis revealed that the hepatotoxicity caused by PM-D was closely related to cholestasis, mitochondrial damage, oxidative stress and energy metabolism, and lipid metabolism disorders.

CONCLUSIONS

In this study, the hepatotoxicity mechanisms of PM-D were comprehensively identified through an integrated spatially resolved metabolomics and network toxicology strategy, providing a theoretical foundation for the toxicity mechanisms of PM and its safe clinical application.

Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View

Abiotic stresses rewire plant central metabolism to maintain metabolic and energy homeostasis. Metabolites involved in the plant central metabolic network serve as a hub for regulating carbon and energy metabolism under various stress conditions. In this review, we introduce recent metabolomics techniques used to investigate the dynamics of metabolic responses to abiotic stresses and analyze the trend of publications in this field. We provide an updated overview of the changing patterns in central metabolic pathways related to the metabolic responses to common stresses, including flooding, drought, cold, heat, and salinity. We extensively review the common and unique metabolic changes in central metabolism in response to major abiotic stresses. Finally, we discuss the challenges and some emerging insights in the future application of metabolomics to study plant responses to abiotic stresses.

Targets and Effective Constituents of ZhiziBaipi Decoction for Treating Damp-Heat Jaundice Syndrome Based on Chinmedomics Coupled with UPLC-MS/MS

Background: Damp-heat jaundice syndrome (DHJS) is a diagnostic model of traditional Chinese medicine (TCM) that refers to jaundice caused by damp-heat pathogen invasion. DHJS is the most common clinical manifestation of TCM, with yellow skin, yellow eyes and anorexia. ZhiziBaipi Decoction (ZBD) is a classic TCM formula that is effective at treating DHJS and various liver diseases. However, the effective components of ZBD in the context of DHJS and the underlying mechanism are unclear.

Purpose: This study of ZBD using the DHJS rat model aimed to elucidate the pathobiology of DHJS and the metabolic targets of therapeutic ZBD, construct the network relationship between the components of ZBD and endogenous biomarkers, and clarify the underlying mechanism of ZBD in preventing and treating DHJS.

Methods: Using chinmedomics as the core strategy, an animal model was generated, and the therapeutic effect of ZBD was evaluated based on behavioral, histopathological and biochemical indicators. Metabonomics tools were used to identify biomarkers of DHJS, TCM-based serum pharmacochemistry was used to analyze the effective constituents of ZBD, and chinmedomics technology was used to identify ZBD components highly related to DHJS biomarkers.

Results: A total of 42 biomarkers were preliminarily identified, and ZBD significantly affected the levels of 29 of these biomarkers. A total of 59 compounds in ZBD were characterized in vivo. According to chinmedomics analysis, the highly correlated components found in blood were isoformononetin, 3-O-feruloylquinic acid, glycyrrhizic acid, oxyberberine, obaculactone and five metabolites.

Conclusions: Chinmedomics combined with UPLC-MS/MS was used to study the targets and effective constituents of ZBD for the treatment of DHJS.

Metabonomics Study on the hepatoprotective effect mechanism of polysaccharides from different processed products of Angelica Sinensis on the layer chickens based on UPLC-Q/TOF-MS/MS, multivariate statistical analysis and conjoint analysis

Chicken colibacillosis is one of the most severe diseases in the poultry industry. Ceftiofur sodium (CS) is often used to treat it in clinical practice and lipopolysaccharide (LPS) accumulates in the chicken's body. Previous experimental studies found that CS combined with LPS could induce liver injury in layer chickens, and polysaccharides from charred Angelica sinensis(CASP) had a better hepatoprotective effect than polysaccharides from unprocessed Angelica sinensis(UASP). However, the intervention mechanism was unclear. Thus, UPLC–Q/TOF–MS/MS‐based metabonomics and transcriptomics were used in this study to clarify the hepatoprotective effect mechanism of CASP and UASP in layer chickens. Transcriptomics and enzyme‐linked immunosorbent assay were used for biological verification of some critical mutual metabolic pathways screened with metabonomics. The comprehensive analysis results showed that in a layer chicken liver injury model built with LPS and CS, 12 critical metabolic pathways were disturbed, involving 10 important differential metabolites. The hepatoprotective effect mechanism of CASP is related to the arachidonic acid metabolism and mTOR signaling pathways, involving nine important differential metabolites. In contrast, the hepatoprotective effect mechanism of UASP is related to the arachidonic acid metabolism pathway, involving six important differential metabolites.

Discovery of Biomarkers and Potential Mechanisms of Agarwood Incense Smoke Intervention by Untargeted Metabolomics and Network Pharmacology

Background

Agarwood, as a traditional Chinese medicine, has great potential value for the treatment of tranquilization. However, its potential mechanisms and biomarkers are still unclear.

Methods

In this study, ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap-MS)-based metabonomics was adopted to discover the potential biomarkers in mice after agarwood incense smoke (AIS) intervention. Furthermore, the chemical components in agarwood were identified based on UHPLC-Q-Exactive Orbitrap-MS. The global view of potential compound-target-pathway (C-T-B) network was constructed through network pharmacology to understand the potentially material basis of biomarkers.

Results

Metabolic profiling indicated that the metabolic changed significantly in mice serum after AIS intervention. A total of 18 potential biomarkers closely related to insomnia and emotional disease were identified, mainly involving in tryptophan metabolism, arginine and proline metabolism, cysteine and methionine metabolism and steroid hormone biosynthesis pathways. A total of 138 components in agarwood were identified based on UHPLC-Q-Exactive Orbitrap-MS. The results showed that mainly compounds such as flidersia type 2-(2-phenylethyl) chromones (FTPECs) and sesquiterpenes exerted good docking abilities with key target proteins, which were involved in multiple diseases including depression and hypnosis.

Conclusion

In conclusion, this study enhanced current understanding of the change of metabolic markers after AIS intervention. Meanwhile, it also confirmed the feasibility of combining metabolomics and network pharmacology to identify active components and elucidate the material basis of biomarkers and mechanisms.

Metabolomics Analysis Coupled With UPLC/MS on Therapeutic Effect of Jigucao Capsule Against Dampness-Heat Jaundice Syndrome

Dampness-heat Jaundice Syndrome (DHJS) is a complex Chinese medicine syndrome, while Jigucao capsule (JGCC) is an effective compound preparation of Chinese medicine for the treatment of DHJS about liver and gallbladder, but its mechanism is not clear yet. The purpose of this study is to clarify the pathogenesis of DHJS and the treatment mechanism of JGCC. We used ultra-high performance liquid chromatography/mass spectrometry (UPLC/MS) combined with pattern recognition, accompanied the advanced software and online database for the urine metabolomics of rats. The potential biomarkers disturbing metabolism were identified and the metabolic pathway was analyzed. We investigated the callback of biomarkers after treatment with JGCC. Finally, A total of 25 potential urine biomarkers were identified, including Arachidonic acid, Phenylpyruvic acid, L-Urobilin and so on, and 14 related metabolic pathways were disturbed. After treatment with JGCC, the clinical biochemical indexes and histopathological were significantly improved, and the disturbed biomarkers were also obviously adjusted. It is proved that JGCC has remarkable effect on the treatment of DHJS.

Comparison of Microflow and Analytical Flow Liquid Chromatography Coupled to Mass Spectrometry Global Metabolomics Methods Using a Urea Cycle Disorder Mouse Model

Microscale-based separations are increasingly being applied in the field of metabolomics for the analysis of small-molecule metabolites. These methods have the potential to provide improved sensitivity, less solvent waste, and reduced sample-size requirements. Ion-pair free microflow-based global metabolomics methods, which we recently reported, were further compared to analytical flow ion-pairing reagent containing methods using a sample set from a urea cycle disorder (UCD) mouse model. Mouse urine and brain homogenate samples representing healthy, diseased, and disease-treated animals were analyzed by both methods. Data processing was performed using univariate and multivariate techniques followed by analyte trend analysis. The microflow methods performed comparably to the analytical flow ion-pairing methods with the ability to separate the three sample groups when analyzed by partial least-squares analysis. The number of detected metabolic features present after each data processing step was similar between the microflow-based methods and the ion-pairing methods in the negative ionization mode. The observed analyte trend and coverage of known UCD biomarkers were the same for both evaluated approaches. The 12.5-fold reduction in sample injection volume required for the microflow-based separations highlights the potential of this method to support studies with sample-size limitations.

Metabolomics and microbiomes for discovering biomarkers of antituberculosis drugs-induced hepatotoxicity

Anti-tuberculosis (TB) drug-induced hepatotoxicity (ATDH) was related to metabolic and microbial dysregulation, but only limited data was available about the metabolomes and microbiomes in ATDH. We aimed at detecting the metabolic and microbial signatures of ATDH. Urine samples were obtained from ATDH (n = 33) and non-ATDH control (n = 41) and analyzed by untargeted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). Metabolites were analyzed by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) and pathway analysis. Eight ATDH and eight non-ATDH control were evaluated by sequencing of 16S rRNA genes, and the Clusters of Orthologous Groups of proteins (COG) database were used for function prediction. Linear discriminant analysis (LDA) effect size (LEfSe) was applied to detect the differential microbiotas between the two groups. The differential microbiotas were further validated by correlation analysis with differential metabolites. OPLS-DA analysis suggested 11 metabolites that differed ATDH from non-ATDH control. Pathway analysis demonstrated that metabolism of arginine and proline, metabolism of d-arginine and d-ornithine, glutathione glycine metabolism, galactose metabolism, niacin and nicotinamide metabolism, and glycine, serine and threonine metabolism were related to ATDH. LEfSe suggested significant differences in microbiotas between the two groups. The o_ Bacteroidales, f_Prevotellaceae, and g_Prevotella were significantly increased in ATDH. In contrast, the f_Chitinophagaceae, c_Gammaproteobacteria, and p_Proteobacteria were significantly increased in non-ATDH group. The biological functions of the sequenced microbiota in this study were related to amino acid transport and metabolism and defense mechanisms. Finally, we detected strong association between urine metabolites and specific urine bacteria (|r| > 0.8). d-glucoheptose showed a strong relationship to Symbiobacterium. Creatine (r = -0.901; P < 0.001) and diglycerol were strongly associated with Alishewanella. Metabolomics and microbiomes indicate ATDH characterized by metabolic and microbial profiles may differ from non-ATDH control.

Integrative Analysis of Metabolomics and Transcriptomics Data Identifies Prognostic Biomarkers Associated With Oral Squamous Cell Carcinoma

Background

Oral squamous cell carcinoma (OSCC) is the most malignant neoplasm in oral cancer. There is growing evidence that its progression involves altered metabolism. The current method of evaluating prognosis is very limited, and metabolomics may provide a new approach for quantitative evaluation. The aim of the study is to evaluate the use of metabolomics as prognostic markers for patients with OSCC.

Methods

An analytical platform, Ultra-Performance Liquid Chromatography-Quadrupole/Orbitrap High Resolution Mass Spectrometry (UHPLC-Q-Orbitrap HRMS), was used to acquire the serum fingerprinting profiles from a total of 103 patients of OSCC before and after the operation. In total, 103 OSCC patients were assigned to either a training set (n = 73) or a test set (n = 30). The potential biomarkers and the changes of serum metabolites were profiled and correlated with the clinicopathological parameters and survival of the patients by statistical analysis. To further verify our results, we linked them to gene expression using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

In total, 14 differential metabolites and five disturbed pathways were identified between the preoperative group and postoperative group. Succinic acid change-low, hypoxanthine change-high tumor grade, and tumor stage indicated a trend towards improved recurrence-free survival (RFS), whether in a training set or a test set. In addition, succinic acid change-low, hypoxanthine change-high, and tumor grade provided the highest predictive accuracy of the patients with OSCC. KEGG enrichment analysis showed that the imbalance in the amino acid and purine metabolic pathway may affect the prognosis of OSCC.

Conclusions

The changes of metabolites before and after operation may be related to the prognosis of OSCC patients. UHPLC-Q-Orbitrap HRMS serum metabolomics analysis could be used to further stratify the prognosis of patients with OSCC. These results can better understand the mechanisms related to early recurrence and help develop more effective therapeutic targets.

Elevated levels of urine isocitrate, hydroxymethylglutarate, and formiminoglutamate are associated with arterial stiffness in Korean adults

Recent evidence suggests that cellular perturbations play an important role in the pathogenesis of cardiovascular diseases. Therefore, we analyzed the association between the levels of urinary metabolites and arterial stiffness. Our cross-sectional study included 330 Korean men and women. The brachial-ankle pulse wave velocity was measured as a marker of arterial stiffness. Urinary metabolites were evaluated using a high-performance liquid chromatograph-mass spectrometer. The brachial-ankle pulse wave velocity was found to be positively correlated with l-lactate, citrate, isocitrate, succinate, malate, hydroxymethylglutarate, α-ketoisovalerate, α-keto-β-methylvalerate, methylmalonate, and formiminoglutamate among men. Whereas, among women, the brachial-ankle pulse wave velocity was positively correlated with cis-aconitate, isocitrate, hydroxymethylglutarate, and formiminoglutamate. In the multivariable regression models adjusted for conventional cardiovascular risk factors, three metabolite concentrations (urine isocitrate, hydroxymethylglutarate, and formiminoglutamate) were independently and positively associated with brachial-ankle pulse wave velocity. Increased urine isocitrate, hydroxymethylglutarate, and formiminoglutamate concentrations were associated with brachial-ankle pulse wave velocity and independent of conventional cardiovascular risk factors. Our findings suggest that metabolic disturbances in cells may be related to arterial stiffness.

Molecular classification and clinical diagnosis of acute-on-chronic liver failure patients by serum metabolomics

Prevalence of acute-on-chronic liver failure (ACLF) patients is growing worldwide, associating with multi-organ failure and high short-term mortality rates. ACLF can be of varying entity manifestation, whereas it remains poorly defined. Traditional Chinese medicine (TCM) stratifies ACLF into two types, damp hot (DH) and cold damp (CD), by seasoned TCM practitioners, for specific treatment with different TCMs. The biggest challenge for the outcome of TCM therapy is the accuracy of diagnosis. However, it is difficult to guarantee it due to lack of the molecule classification of ACLF. Herein, we recruited 58 subjects including 34 ACLF patients (18 DH and 16 CD) and 24 healthy controls, and analyzed serum metabolic profiles using untargeted ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) metabolomics approach. A total of 10 serum metabolites were found as potential biomarkers for diagnosis of ACLF. Among them, taurochenodesoxycholic acid (N3), glycyldeoxycholic acid (N5) and 12-HETE-GABA (N7), varied between two types of ACLF and can be merged as a combination marker to differentiate CD from DH patients with area under the receiver operating curve (AUC) of 0.928 (95 % CI 0.8-1). CD patients possessed comparatively higher bile acid metabolism and lower arachidonic acid metabolism compared with DH patients. The results provide not only serum molecules for early accurate diagnosis of ACLF patients, but also potential clinical biomarkers for classification of CD and DH types. The findings clarify that molecular markers will be objective criteria for diagnosis of clinical types in TCM practice.

R-JaunLab: Automatic Multi-Class Recognition of Jaundice on Photos of Subjects with Region Annotation Networks

Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder or pancreas. Therefore, clinical measurement with special equipment is a common method that is used to identify the total serum bilirubin level in patients. Fully automated multi-class recognition of jaundice combines two key issues: (1) the critical difficulties in multi-class recognition of jaundice approaches contrasting with the binary class and (2) the subtle difficulties in multi-class recognition of jaundice represent extensive individuals variability of high-resolution photos of subjects, huge coherency between healthy controls and occult jaundice, as well as broadly inhomogeneous color distribution. We introduce a novel approach for multi-class recognition of jaundice to detect occult jaundice, obvious jaundice and healthy controls. First, region annotation network is developed and trained to propose eye candidates. Subsequently, an efficient jaundice recognizer is proposed to learn similarities, context, localization features and globalization characteristics on photos of subjects. Finally, both networks are unified by using shared convolutional layer. Evaluation of the structured model in a comparative study resulted in a significant performance boost (categorical accuracy for mean 91.38%) over the independent human observer. Our work was exceeded against the state-of-the-art convolutional neural network (96.85% and 90.06% for training and validation subset, respectively) and showed a remarkable categorical result for mean 95.33% on testing subset. The proposed network makes a performance better than physicians. This work demonstrates the strength of our proposal to help bringing an efficient tool for multi-class recognition of jaundice into clinical practice.

STRATEGIES AND CHALLENGES IN METHOD DEVELOPMENT AND VALIDATION FOR THE ABSOLUTE QUANTIFICATION OF ENDOGENOUS BIOMARKER METABOLITES USING LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY

Metabolomics is a dynamically evolving field, with a major application in identifying biomarkers for drug development and personalized medicine. Numerous metabolomic studies have identified endogenous metabolites that, in principle, are eligible for translation to clinical practice. However, few metabolomic-derived biomarker candidates have been qualified by regulatory bodies for clinical applications. Such interruption in the biomarker qualification process can be largely attributed to various reasons including inappropriate study design and inadequate data to support the clinical utility of the biomarkers. In addition, the lack of robust assays for the routine quantification of candidate biomarkers has been suggested as a potential bottleneck in the biomarker qualification process. In fact, the nature of the endogenous metabolites precludes the application of the current validation guidelines for bioanalytical methods. As a result, there have been individual efforts in modifying existing guidelines and/or developing alternative approaches to facilitate method validation. In this review, three main challenges for method development and validation for endogenous metabolites are discussed, namely matrix effects evaluation, alternative analyte-free matrices, and the choice of internal standards (ISs). Some studies have modified the equations described by the European Medicines Agency for the evaluation of matrix effects. However, alternative strategies were also described; for instance, calibration curves can be generated in solvents and in biological samples and the slopes can be compared through ratios, relative standard deviation, or a modified Stufour suggested approaches while quantifying mainly endogenous metabolitesdent t-test. ISs, on the contrary, are diverse; in which seven different possible types, used in metabolomics-based studies, were identified in the literature. Each type has its advantages and limitations; however, isotope-labeled ISs and ISs created through isotope derivatization show superior performance. Finally, alternative matrices have been described and tested during method development and validation for the quantification of endogenous entities. These alternatives are discussed in detail, highlighting their advantages and shortcomings. The goal of this review is to compare, apprise, and debate current knowledge and practices in order to aid researchers and clinical scientists in developing robust assays needed during the qualification process of candidate metabolite biomarkers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Chinmedomics, a new strategy for evaluating the therapeutic efficacy of herbal medicines

Herbal medicines have accumulated valuable clinical experience in thousands of years of applications in traditional Chinese medicine (TCM) or ethnomedicine. The unique multi-target efficacy on complex diseases made herbal medicines gained a global popularity in recent years. However, the characteristic of multi-component acting on multi-target poses a dilemma for the evaluation of therapeutic efficacy of herbal medicines. Advances in metabolomics enable efficient identification of the various changes in biological systems exposed to different treatments or conditions. The use of serum pharmacochemistry of TCM has significant implications for tackling the major issue in herbal medicines development-pharmacodynamic material basis. Chinmedomics integrates metabolomics and serum pharmacochemistry of TCM to investigate the pharmacodynamic material basis and effective mechanisms of herbal medicines on the basis of TCM syndromes and holds the promise of explaining therapeutic efficacy of herbal medicines in scientific language. In this review, the historical development of chinmedomics from concept formation to successful applications was discussed. We also took the systematic research of Yin Chen Hao Tang (YCHT) as an example to show the research strategy of chinmedomics.

Potential Metabolic Biomarkers for Early Detection of Oral Lichen Planus, a Precancerous Lesion

Background: Oral lichen planus (OLP) is a T-cell-mediated chronic inflammatory disorder and precancerous oral lesion with high incidence. The current diagnostic method of OLP is very limited and metabolomics may provide a new approach for quantitative evaluation.

Methods: The Ultra-Performance Liquid Chromatography-Quadrupole/Orbitrap High Resolution Mass Spectrometry (UHPLC-Q-Orbitrap HRMS) was applied to analyze the change of metabolites in serum of patients with OLP. A total of 115 OLP patients and 124 healthy controls were assigned to either a training set (n = 160) or a test set (n = 79). The potential biomarkers and the change of serum metabolites were profiled and evaluated by multivariate analysis.

Results: Totally, 23 differential metabolites were identified in the training set between OLP group and healthy group. Three prominent metabolites in receiver operating characteristic (ROC) were selected as a panel to distinguish OLP or healthy individuals in the test set, and the diagnostic accuracy was 86.1%.

Conclusions: This study established a new method for the early detection of OLP by analyzing serum metabolomics using UHPLC-Q-Orbitrap HRMS, which will help in understanding the pathological processes of OLP and identifying precancerous lesions in oral cavity.

Urine Untargeted Metabolomic Profiling Is Associated with the Dietary Pattern of Successful Aging among Malaysian Elderly

Food intake biomarkers (FIBs) can reflect the intake of specific foods or dietary patterns (DP). DP for successful aging (SA) has been widely studied. However, the relationship between SA and DP characterized by FIBs still needs further exploration as the candidate markers are scarce. Thus, 1H-nuclear magnetic resonance (¹H-NMR)-based urine metabolomics profiling was conducted to identify potential metabolites which can act as specific markers representing DP for SA. Urine sample of nine subjects from each three aging groups, SA, usual aging (UA), and mild cognitive impairment (MCI), were analyzed using the ¹H-NMR metabolomic approach. Principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were applied. The association between SA urinary metabolites and its DP was assessed using the Pearson’s correlation analysis. The urine of SA subjects was characterized by the greater excretion of citrate, taurine, hypotaurine, serotonin, and melatonin as compared to UA and MCI. These urinary metabolites were associated with alteration in “taurine and hypotaurine metabolism” and “tryptophan metabolism” in SA elderly. Urinary serotonin (r = 0.48, p < 0.05) and melatonin (r = 0.47, p < 0.05) were associated with oat intake. These findings demonstrate that a metabolomic approach may be useful for correlating DP with SA urinary metabolites and for further understanding of SA development.

Astragaloside IV Extends Lifespan of Caenorhabditis elegans by Improving Age-Related Functional Declines and Triggering Antioxidant Responses

Astragaloside IV (AS-IV) is a representative component of astragaloside saponins in dried roots of Astragali Radix. Astragaloside possesses a broad spectrum of pharmacological activities, including antibacterial, anti-fibrosis, antioxidant, anti-inflammatory, and neuroprotective effects. However, the role of AS-IV in antiaging remains unclear. In this article, we studied the function of AS-IV in antiaging by using the Caenorhabditis elegans (C. elegans) model. We showed that AS-IV can prolong the lifespan of C. elegans in a natural aging model, a paraquat injury model, and a heat stress model and improve the movement capacity of nematodes. ¹H-NMR data indicate an improvement of glutamate content and a decrease in glucose in the AS-IV treatment group compared with the control. Further investigation revealed that AS-IV can induce the mRNA expression of superoxide dismutase (SOD) and catalase (CAT) genes and increase the activities of SOD and CAT in the nematode. Interestingly, AS-IV could not extend the lifespan of sod-1, sod-2, sod-3, sod-4, sod-5, ctl-1, ctl-2, ctl-3, and daf-16 mutants. These data indicate that AS-IV prevents aging via mainly improving age-related functional declines, the antioxidant capacity of nematodes and partially modulating the insulin/insulin growth factor 1 signaling pathway activity. Our results provide new insights into how AS-IV prevents and treats aging.

Screening for Susceptibility-Related Factors and Biomarkers of Xianling Gubao Capsule-Induced Liver Injury

Although increasing reports from the literature on herbal-related hepatotoxicity, the identification of susceptibility-related factors and biomarkers remains challenging due to idiosyncratic drug-induced liver injury (IDILI). As a well-known Chinese medicine prescription, Xianling Gubao Capsule (XLGB) has attracted great attention due to reports of potential liver toxicity. But the mechanism behind it is difficult to determine. In this paper, we found that XLGB-induced liver injury belongs to IDILI through the analysis of clinical liver injury cases. In toxicological experiment assessment, co-exposure to XLGB and non-toxic dose of lipopolysaccharide (LPS) could cause evident liver injury as manifested by significantly increased plasma alanine aminotransferase activity and obvious liver histological damage. However, it failed to induce observable liver injury in normal rats, suggesting that mild immune stress may be a susceptibility factor for XLGB-induced idiosyncratic liver injury. Furthermore, plasma cytokines were determined and 15 cytokines (such as IL-1β, IFN-γ, and MIP-2α etc) were acquired by receiver operating characteristic (ROC) curves analysis. The expression of these 15 cytokines in LPS group was significantly up-regulated in contrast to the normal group. Meanwhile, the metabolomics profile showed that mild immune stress caused metabolic reprogramming, including sphingolipid metabolism, phenylalanine metabolism, and glycerophospholipid metabolism. 8 potential biomarkers (such as sphinganine, glycerophosphoethanolamine, and phenylalanine etc.) were identified by correlation analysis. Therefore, these results suggested that intracellular metabolism and immune changes induced by mild immune stress may be important susceptibility mechanisms for XLGB IDILI.

Integrated Proteomics and Metabolomics Reveal the Mechanism of Nephrotoxicity Induced by Triptolide

Triptolide (TP), the main active ingredient of Tripterygium wilfordii Hook F., has great potential in the treatment of autoimmune diseases. However, it has been found that the side effects of TP involve multiple organs and systems, of which the most serious side effects relate to the kidney. The mechanism of nephrotoxicity caused by TP requires further investigation. In the present study, we integrated proteomic and metabolomic methods to identify proteins and small molecule metabolites associated with TP-induced nephrotoxicity. There was a significant difference (p value <0.05) in the expression changes of 357 proteins for quantitative proteomics. In addition, high resolution metabolomic data showed significant changes in the levels of 9 metabolites, including hypoxanthine, PC(22:0/18:4), sphingosine, phenylalanine, etc. Finally, based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database for network analysis, it was determined that the 7 differentially expressed proteins were highly correlated with these 9 metabolites. Enrichment analysis revealed that the metabolic pathways involved purine and pyrimidine metabolism, glycerol and phospholipid metabolism, sphingolipid metabolism, and amino acid metabolism. The key target proteins were verified by Western blot technology, and the mechanism of TP-induced nephrotoxicity was further elucidated to provide a basis for safe and rational application.

Novel Metabolomics Serum Biomarkers for Pancreatic Ductal Adenocarcinoma by the Comparison of Pre-, Postoperative and Normal Samples

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive human malignancies. The metabolomic approaches are developed to discover the novel biomarkers of PDAC. Methods: 550 preoperative, postoperative PDAC and normal controls (NCs) serums were employed to characterize metabolic alterations in training and validation sets by LC-MS. Results: The results of PLS-DA analysis indicated that three groups could be distinguished clearly and the post-PDAC group is adjacent to a normal group as compared with pre-PDAC group. Further results showed that histidinyl-lysine significantly increased whereas docosahexaenoic acid and LysoPC (14:0) decreased in pre-PDAC patients as compared with NCs. And these three markers had a significant tendency to recover after tumor resection. The validation set results revealed that for CA19-9 negative patients, 92.3% (12/13) of them can be screened using these three metabolites. The combination of these markers could significantly improve the diagnostic performance for PDAC, with higher sensitivity (0.93), specificity (0.92) and AUC (0.97). Moreover, network and pathways analyses explored the latent relationship among differential metabolites. The glycerolipid metabolism and primary bile acid synthesis showed variation in network and pathway analysis. Conclusions: These three markers combined with CA199 displayed high sensitivity and specificity for detecting PDAC patients from NCs. The results indicated that these three metabolites could be regarded as potential biomarkers to distinguish PDAC from NCs.

Enhanced single-cell metabolomics by capillary electrophoresis electrospray ionization-mass spectrometry with field amplified sample injection

Single-cell metabolomics provides information on the biochemical state of an individual cell and its relationship with the surrounding environment. Characterization of metabolic cellular heterogeneity is challenging, in part due to the small amounts of analytes and their wide dynamic concentration ranges within individual cells. CE-ESI-MS is well suited to single-cell assays because of its low sample-volume requirements and low detection limits. While the volume of a cell is in the picoliter range, after isolation, the typical volume of the lysed cell sample is on the order of a microliter; however, only nanoliters are injected into the CE system, with the volume mismatch limiting analytical performance. Here we developed an approach for the detection of intracellular metabolites from a single neuron using field amplified sample injection (FASI) CE-ESI-MS. Through the application of FASI, we achieved 100- to 300-fold detection limit enhancement compared to hydrodynamic injections. We further enhanced the analyte identification and quantification accuracy via introduction of two internal standards. As a result, the relative standard deviations of migration times were reduced to <5%, aiding identification. Finally, we successfully applied FASI CE-ESI-MS to the untargeted profiling of metabolites of Aplysia californica pleural sensory neurons with <50 μm diameter cell somata. As a result, twenty one neurotransmitters and metabolites have been quantified in these neurons.

High-throughput metabolomics reveals the perturbed metabolic pathways and biomarkers of Yang Huang syndrome as potential targets for evaluating the therapeutic effects and mechanism of geniposide

High-throughput metabolomics can clarify the underlying molecular mechanism of diseases via the qualitative and quantitative analysis of metabolites. This study used the established Yang Huang syndrome (YHS) mouse model to evaluate the efficacy of geniposide (GEN). Urine metabolic data were quantified by ultraperformance liquid chromatography-tandem mass spectrometry. The non-target screening of the massive biological information dataset was performed, and a total of 33 metabolites, including tyramine glucuronide, aurine, and L-cysteine, were identified relating to YHS. These differential metabolites directly participated in the disturbance of phase I reaction and hydrophilic transformation of bilirubin. Interestingly, they were completely reversed by GEN. While, as the auxiliary technical means, we also focused on the molecular prediction and docking results in network pharmacological and integrated analysis part. We used integrated analysis to communicate the multiple results of metabolomics and network pharmacology. This study is the first to report that GEN indirectly regulates the metabolite "tyramine glucuronide" through its direct effect on the target heme oxygenase 1 in vivo. Meanwhile, heme oxygenase-1, a prediction of network pharmacology, was the confirmed metabolic enzyme of phase I reaction in hepatocytes. Our study indicated that the combination of high-throughput metabolomics and network pharmacology is a robust combination for deciphering the pathogenesis of the traditional Chinese medicine (TCM) syndrome.

Targeted analysis reveals alteration in pathway in 5p minus individuals

Cri du Chat or 5p minus (5p-) syndrome is characterized by a deletion located on the chromosome 5 short (-p) arm and has an incidence rate of 1 in 50,000 individuals worldwide. This disease manifests in disturbances across a range of systems biochemicals. Therefore, a targeted metabolomics analysis was evaluated in patients with 5p- syndrome to help unravel the biochemical changes that occur in this disease. Urine samples were collected from people of both sexes aged 1-38 years old and analyzed by ultra-performance liquid chromatography coupled to mass spectrometry. Student' statistical test, metabolomic pathway analysis and metabolite set enrichment analysis were applied to the data. Alterations of some amino acids and amine biogenics levels were found in Cri du Chat Syndrome individuals. The alteration of most of these metabolites is associated with energy recuperation and glycolysis. In general, we found the catabolism of some metabolic pathways to be affected in 5p- patients.

Functional metabolomics using UPLC-Q/TOF-MS combined with ingenuity pathway analysis as a promising strategy for evaluating the efficacy and discovering amino acid metabolism as a potential therapeutic mechanism-related target for geniposide against alcoholic liver disease

Metabolomics has been used as a strategy to evaluate the efficacy of and potential targets for natural products.

Quantitative Comparative Proteomics Reveal Biomarkers for Dengue Disease Severity

Dengue fever (DF) could develop into dengue haemorrhagic fever (DHF) with increased mortality rate. Since the clinical characteristics and pathogen are same in DF and DHF. It's important to identify different molecular biomarkers to predict DHF patients from DF. We conducted a clinical plasma proteomics study using quantification (TMT)-based quantitative proteomics methodology to found the differential expressed protein in DF patients before they developed into DHF. In total 441 proteins were identified up or down regulated. There proteins are enriched in diverse biological processes such as proteasome pathway, Alanine, aspartate, and glutamate metabolism and arginine biosynthesis. Several proteins such as PLAT, LAMB2, and F9 were upregulated in only DF patients which developed into DHF cases, not in DF, compared with healthy-control. In another way, FGL1, MFAP4, GLUL, and VCAM1 were upregulated in both DHF and DF cases compare with healthy-control. RT-PCR and ELISA were used to validate these upregulated gene expression and protein level in 54 individuals. Results displayed the same pattern as proteomics analysis. All including PLAT, LAMB2, F9, VCAM1, FGL1, MFAP4, and GLUL could be considered as potential markers of predicting DHF since the levels of these proteins vary between DF and DHF. These new founding identified potential molecular biomarkers for future development in precision prediction of DHF in DF patients.

Differential Constituents in Roots, Stems and Leaves of Polygonum multiflorum Thunb. Screened by UPLC/ESI-Q-TOF-MS and Multivariate Statistical Analysis

The differential constituents in leaves, stems and roots of Polygonum multiflorum Thunb. were analyzed by ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-Q-TOF-MS) and by multivariate statistical analysis. The established extraction and analysis method showed relative standard deviations (RSDs) for intra-day precision of less than 3.40%, for repeatability of less than 4.06% and for stability of less than 5.10%. Principal component analysis and orthogonal projections to latent structures discriminant analysis of the UPLC/ESI-Q-TOF-MS data showed good ability to classify the leaves, stems and roots of P. multiflorum Thunb. The differential constituents, such as stilbenes, polygoacetophenoside, flavonoids and anthraquinones, accounting for variations between the leaves, stems and roots, were filtered through the variable importance in projection values and were further identified by elemental composition analysis, mass fragmentation data and retention times of available standards. Differences between the chemical compositions in the leaves, stems and roots of P. multiflorum Thunb. were closely related to their various therapeutic effects. This UPLC/ESI-Q-TOF-MS-based analytical strategy could be further utilized to evaluate the overall quality of traditional Chinese medicines and their differences of chemical constituents in different parts of the plant and/or in the plants of different geographical locations.

Research progress on Traditional Chinese Medicine syndromes of diabetes mellitus

With the improvement of people's living standard and the changes of environment, the incidence of diabetes mellitus (DM) is on the rise day by day, while clinical treatment mainly aims at lowering blood glucose, instead of fundamental prevention and treatment. What's worse, the measures of prevention and treatment of DM complications remain inadequate. Both Chinese and modern medicine have advantages and disadvantages in treating DM, therefore, it would be a worthy attempt to break through the bottleneck of DM treatment by combining the advantages of both, and explore the new measures to prevent and deal with DM from the perspective of the combination of Traditional Chinese Medicine (TCM) syndrome and modern medicine. In this paper, modern research methods and possible indicators of TCM syndromes of DM were expounded from clinical and basic research aspects, aiming to find specific biomarkers of TCM syndromes, and providing experimental supports for the diagnosis and treatment of DM and the verification of TCM theory.

Untargeted metabolomics using liquid chromatography coupled with mass spectrometry for rapid discovery of metabolite biomarkers to reveal therapeutic effects of Psoralea corylifolia seeds against osteoporosis

Liquid chromatography coupled with mass spectrometry has been used as metabolomics profiling tool to discover and identify the metabolites in metabolic diseases. Osteoporosis (OP) syndrome is a chronic metabolic disease characterized by bone mass reduction and changes in bone microstructure. Psoralea corylifolia Linn. seeds (PCS) have a therapeutic effect on osteoporosis, but their action mechanism and therapeutic target are still unclear. This study aims to explore the metabolic changes of the urine profile in glucocorticoid-induced OP model rats and the therapeutic effect of PCS. High-throughput metabolomics based on the liquid chromatography coupled with mass spectrometry quadrupole time-of-flight mass spectrometry and multivariate data analysis were used to analyze the urine metabolites. The results showed that has an obvious separation between model and control groups. OPLS-DA was used to further analyze and discover substances that contributed to the separation. 42 potential biomarkers and 12 related metabolic pathways were identified in combination with network databases. After the intervention of PCS, 24 biomarkers were significantly regulated, mainly with glycone, serine and threonine metabolism, glutathione metabolism and purine metabolism and other metabolic pathways are related and discovered. This study has proved that PCS has therapeutic effect against OP by regulating that metabolic pathways disturbed in the OP. It provided a basis for the research and future development of new drugs for OP treatment.

Liquid chromatography coupled with mass spectrometry has been used as metabolomics profiling tool to discover and identify the metabolites in metabolic diseases.

Urinary metabolomics analysis reveals the anti-diabetic effect of stachyose in high-fat diet/streptozotocin-induced type 2 diabetic rats

As a new platform of systems biology, metabolomics provides a powerful approach to discover therapeutic biomarkers and mechanism of metabolic disease. Type 2 diabetes mellitus (T2DM) is a global metabolic disease, thus, a urinary metabolomics profiling was analyzed to study the anti-diabetic effects and mechanism of stachyose (ST) on high-fat diet- and low dose streptozotocinc-induced T2DM rats. The results showed that ST treatment regulated the level of insulin, low-density lipoprotein cholesterol, and triglycerides, which demonstrates improvement in T2DM on ST treatment. Urinary samples from the ST and T2DM group were enrolled in metabolomics study, 21 differential metabolites were identified from urinary metabolomics analysis, indicating that the ST treatment partly exerted the anti-diabetes activity by regulating energy metabolism, gut microbiota changes and inflammation. A metabolomics strategy is both suitable and reliable for exploring the anti-diabetes effects and understanding the mechanisms of ST treatment against T2DM.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Effects of High-Grain Diet With Buffering Agent on the Hepatic Metabolism in Lactating Goats

To gain insight on the effects of a high-grain diet with buffering agent on liver metabolism and the changes of plasma biochemical parameters and amino acids in hepatic vein and portal vein, commercial kit and high performance liquid chromatography (HPLC) were applied to determine the concentration of amino acids of hepatic vein and portal vein blood samples, quantitative real-time PCR and comparative proteomic approach was employed to investigate proteins differentially expressed in liver in lactating dairy goats feeding high-grain diet with buffering agent or only high-grain diet. Results showed that feeding high-grain diet with buffering agent to lactating dairy goats could outstanding increase amino acid content of Gln (p < 0.01), and the amino acid contents of Arg and Tyr in BG were significantly higher (p < 0.05) than that in HG. After adding the buffering agent, the metabolism of amino acids in the liver were changed and most of the amino acids were increasingly synthesized and decreasingly consumed in the liver. In addition, 46 differentially expressed protein spots (≥1.5-fold changed) were detected in buffering group vs. control group using 2-DE technique and MALDI-TOF/TOF proteomics analyzer. Of these, 24 proteins showed increased expression and 22 proteins showed decreased expression in the buffer group vs. control group. Data on Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis reveals that the high-grain diet with buffering agent alter the expression of proteins related to amino acids metabolism and glycometabolism. In addition, the results conclude that feeding high-grain diet with buffering agent can strengthen anti-oxidant capacity, stress ability, slow down urea metabolism, and alter amino acid metabolism as well as glycometabolism in the liver through different detection methods including proteomic analysis, real-time PCR analysis and biochemical analysis.

Application of metabolomics in viral pneumonia treatment with traditional Chinese medicine

Nowadays, traditional Chinese medicines (TCMs) have been reported to provide reliable therapies for viral pneumonia, but the therapeutic mechanism remains unknown. As a systemic approach, metabolomics provides an opportunity to clarify the action mechanism of TCMs, TCM syndromes or after TCM treatment. This review aims to provide the metabolomics evidence available on TCM-based therapeutic measures against viral pneumonia. Metabolomics has been gradually applied to the efficacy evaluation of TCMs in treatment of viral pneumonia and the metabolomics analysis exhibits a systemic metabolic shift in lipid, amino acids, and energy metabolism. Currently, most studies of TCM in treatment of viral pneumonia are untargeted metabolomics and further validations on targeted metabolomics should be carried out together with molecular biology technologies.

High-throughput chinmedomics strategy for discovering the quality-markers and potential targets for Yinchenhao decoction

BACKGROUND

Yinchenhao decoction (YCHD) has been widely applied in the clinic for various kinds of liver disease, especially for the therapy of dampness-heat jaundice syndrome (DHJS). Some studies have investigated the pharmacological activity and compositions of YCHD. However, its Q-markers and the action targets are still unrevealed.

PURPOSE

This work aims to clarify the therapeutic effect of YCHD against DHJS and discover the quality-markers (Q-markers) of YCHD based on the high-throughput chinmedomics strategy and then predict the potential targets and action mechanism of YCHD against DHJS.

METHODS

Ultra-high performance liquid chromatography/mass spectrometry (UPLC-MS) combined with pattern recognition method was utilized to analyze serum samples and urine samples. Multivariate data analysis and network pharmacology technology were used to identify the effective components and biomarkers associated with therapeutic effects.

RESULTS

With the high sensitivity UPLC-MS technology, a total of 69 compounds from YCHD were identified and 41 of them were absorbed in blood. Besides, 34 urine biomarkers from DHJS were identified. Of note, we utilized chinmedomics technology on the correlation analysis of urine biomarkers and absorbed components to determine 9 core-compounds as the Q-markers responsible for the efficacy of YCHD. Finally, a total of 12 potential targets were discovered.

CONCLUSION

This work provides a powerful method for clarifying the efficacy of TCM and discovering the effective ingredients as Q-markers.

Integrated analysis of proteomics-delineated and metabolomics-delineated hepatic metabolic responses to (-)-hydroxycitric acid in chick embryos

(-)-Hydroxycitric acid [(-)-HCA] is widely used as a nutritional supplement to control body weight and fat accumulation in animals and humans, whereas the underlying biochemical mechanism is unclear. Broiler chicken was used as a model for studies of obesity due to its natural hyperglycemia and being insulin resistant. The current study aimed to obtain a systematic view of serum metabolites and hepatic proteins and well understand the mechanism of hepatic metabolic response to (-)-HCA treatment in chick embryos. The results showed that 22, 90, and 82 of differentially expressed proteins were identified at E14d, E19d, and H1d in chick embryos treated with (-)-HCA, respectively. Meanwhile, 5, 83, and 88 of serum metabolites significantly changed at E14d, E19d, and H1d in chick embryos after (-)-HCA treatment. Bioinformatics analysis showed that the key proteins and metabolites, which were significantly altered in chick embryos treated with (-)-HCA, were mainly involved in the citrate cycle, glycolysis/gluconeogenesis, fatty acid metabolism, and pyruvate metabolism. Our data indicated that (-)-HCA treatment might promote fat metabolism via regulating the key protein expression levels and metabolite contents in the citrate cycle, glycolysis/gluconeogenesis, and oxidative phosphorylation during chicken embryonic development. These results will deepen our understanding of the mechanism of fat reduction by (-)-HCA and provide substantial information for (-)-HCA as a nutritional supplement to control body weight gain and curb obesity-related diseases.

Isopsoralen induces different subchronic toxicities and metabolomic outcomes between male and female Wistar rats

Isopsoralen is a major active and quality-control component of Fructus Psoraleae, but lacks a full safety evaluation. We evaluated the oral toxicity of isopsoralen in Wistar rats treated for 3 months at doses of 0, 3.5, 7.0, and 14 mg/kg. Additionally, the plasma metabolomics of isopsoralen in male and female rats treated for 3 months at doses of 0 and 14 mg/kg were investigated by gas chromatography-mass spectrometry. Many abnormalities were observed in the isopsoralen-treated rats, including suppression of body weight gain, and changes in serum biochemical parameters and visceral coefficients. Histopathological changes in liver, pancreatic, and reproductive system tissues were also observed in the isopsoralen-treated rats. The metabolomic analyses showed alterations in many metabolites (19 in female rats; 28 in male rats) after isopsoralen administration. The significant changes in these metabolites revealed metabolomic alterations in the isopsoralen-treated rats, especially in amino acid metabolism regardless of sex, including phenylalanine, tyrosine, and tryptophan biosynthesis and glycine, serine, and threonine metabolism. Furthermore, fatty acid metabolism comprised the main affected pathways in female rats, while lipid metabolism and energy metabolism were the main affected pathways in male rats.