Medicinal Plants against Viral Infections: A Review of Metabolomics Evidence for the Antiviral Properties and Potentials in Plant Sources

Most plants have developed unique mechanisms to cope with harsh environmental conditions to compensate for their lack of mobility. A key part of their coping mechanisms is the synthesis of secondary metabolites. In addition to their role in plants' defense against pathogens, they also possess therapeutic properties against diseases, and their use by humans predates written history. Viruses are a unique class of submicroscopic agents, incapable of independent existence outside a living host. Pathogenic viruses continue to pose a significant threat to global health, leading to innumerable fatalities on a yearly basis. The use of medicinal plants as a natural source of antiviral agents has been widely reported in literature in the past decades. Metabolomics is a powerful research tool for the identification of plant metabolites with antiviral potentials. It can be used to isolate compounds with antiviral capacities in plants and study the biosynthetic pathways involved in viral disease progression. This review discusses the use of medicinal plants as antiviral agents, with a special focus on the metabolomics evidence supporting their efficacy. Suggestions are made for the optimization of various metabolomics methods of characterizing the bioactive compounds in plants and subsequently understanding the mechanisms of their operation.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

INTRODUCTION

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

OBJECTIVE

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

METHODS

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

RESULTS

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction.

CONCLUSIONS

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Unravelling the anti-inflammatory and antioxidant effects of standardized green and black caffeinated coffee, tea, and their mixtures in an obese male rat model: Insights from biochemical, metabolomic, and histopathological analyses

Obesity is one of the major metabolic syndrome risk factors upon which altered metabolic pathways follow. This study aimed to discern altered metabolic pathways associated with obesity and to pinpoint metabolite biomarkers in serum of obese rats fed on high fructose diet using metabolomics. Further, the effect of standardized green versus black caffeinated aqueous extracts (tea and coffee) in controlling obesity and its comorbidities through monitoring relevant serum biomarkers viz. Leptin, adiponectin, spexin, malondialdehyde, total antioxidant capacity. Liver tissue oxidative stress (catalase, super oxide dismutase and glutathione) and inflammation (IL-1β and IL-6) markers were assessed for green coffee and its mixture with green tea. Results revealed improvement of all parameters upon treatments with more prominence for those treated with green caffeinated extract (coffee and tea) especially in mixture. Upon comparing with obese rat group, the green mixture of coffee and tea exhibited anti-hyperlipidemic action through lowering serum triglycerides by 35.0% and elevating high density lipoprotein by 71.0%. Black tea was likewise effective in lowering serum cholesterol and low density lipoprotein by 28.0 and 50.6%, respectively. GC-MS- based metabolomics of rat serum led to the identification of 34 metabolites with obese rat serum enriched in fatty acids (oleamide).

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

The association between acylcarnitine and amino acids profile and metabolic syndrome and its components in Iranian adults: Data from STEPs 2016

BACKGROUND

Evidence, albeit with conflicting results, has suggested that cardiometabolic risk factors, including obesity, type 2 diabetes (T2D), dyslipidemia, and hypertension, are highly associated with changes in metabolic signature, especially plasma amino acids and acylcarnitines levels. Here, we aimed to evaluate the association of circulating levels of amino acids and acylcarnitines with metabolic syndrome (MetS) and its components in Iranian adults.

METHODS

This cross-sectional study was performed on 1192 participants from the large-scale cross-sectional study of Surveillance of Risk Factors of non-communicable diseases (NCDs) in Iran (STEP 2016). The circulating levels of amino acids and acylcarnitines were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in individuals with MetS (n=529) and without MetS (n=663).

RESULTS

The higher plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asp to Asn were significantly associated with MetS, whereas lower circulating levels of Gly, Ser, His, Asn, and citrulline were significantly associated with MetS. As for plasma levels of free carnitine and acylcarnitines, higher levels of short-chain acylcarnitines (C2, C3, C4DC), free carnitine (C0), and long-chain acylcarnitines (C16, C18OH) were significantly associated with MetS. Principal component analysis (PCA) showed that factor 3 (Tyr, Leu, Val, Met, Trp, Phe, Thr) [OR:1.165, 95% CI: 1.121-1.210, P<0.001], factor 7 (C0, C3, C4) [OR:1.257, 95% CI: 1.150-1.374, P<0.001], factor 8 (Gly, Ser) [OR:0.718, 95% CI: 0.651-0.793, P< 0.001], factor 9 (Ala, Pro, C4DC) [OR:1.883, 95% CI: 1.669-2.124, P<0.001], factor 10 (Glu, Asp, C18:2OH) [OR:1.132, 95% CI: 1.032-1.242, P= 0.009], factor 11 (citrulline, ornithine) [OR:0.862, 95% CI: 0.778-0.955, P= 0.004] and 13 (C18OH, C18:1 OH) [OR: 1.242, 95% CI: 1.042-1.480, P= 0.016] were independently correlated with metabolic syndrome.

CONCLUSION

Change in amino acid, and acylcarnitines profiles were seen in patients with MetS. Moreover, the alteration in the circulating levels of amino acids and acylcarnitines is along with an increase in MetS component number. It also seems that amino acid and acylcarnitines profiles can provide valuable information on evaluating and monitoring MetS risk. However, further studies are needed to establish this concept.

Untargeted Metabolomics Based on Liquid Chromatography-Mass Spectrometry for the Analysis of Plasma and Erythrocyte Samples in Childhood Obesity

The circulating metabolome of human peripheral blood provides valuable information to investigate the molecular mechanisms underlying the development of diseases and to discover candidate biomarkers. In particular, erythrocytes have been proposed as potential systemic indicators of the metabolic and redox status of the organism. To accomplish wide-coverage metabolomics analysis, the combination of complementary analytical techniques is necessary to manage the physicochemical complexity of the human metabolome. Herein, we describe an untargeted metabolomics method to capture the plasmatic and erythroid metabolomes based on ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry, combining reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography. The method provides comprehensive metabolomics fingerprinting of plasma and erythrocyte samples, thereby enabling the elucidation of the distinctive metabolic disturbances behind childhood obesity and associated comorbidities, such as insulin resistance.

A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies

As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLine^TM and Ulib^MS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.

Reproducibility of the Blood and Urine Exposome: A Systematic Literature Review and Meta-Analysis

Endogenous and exogenous metabolite concentrations may be susceptible to variation over time. This variability can lead to misclassification of exposure levels and in turn to biased results. To assess the reproducibility of metabolites, the intraclass correlation coefficient (ICC) is computed. A literature search in three databases from 2000 to May 2021 was conducted to identify studies reporting ICCs for blood and urine metabolites. This review includes 192 studies, of which 31 studies are included in the meta-analyses. The ICCs of 359 single metabolites are reported, and the ICCs of 10 metabolites were meta-analyzed. The reproducibility of the single metabolites ranges from poor to excellent and is highly compound-dependent. The reproducibility of bisphenol A (BPA), mono-ethyl phthalate (MEP), mono-n-butyl phthalate (MnBP), mono-2-ethylhexyl phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-benzyl phthalate (MBzP), mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP), methylparaben, and propylparaben is poor to moderate (ICC median: 0.32; range: 0.15-0.49), and for 25-hydroxyvitamin D [25(OH)D], it is excellent (ICC: 0.95; 95% CI, 0.90-0.99). Pharmacokinetics, mainly the half-life of elimination and exposure patterns, can explain reproducibility. This review describes the reproducibility of the blood and urine exposome, provides a vast dataset of ICC estimates, and hence constitutes a valuable resource for future reproducibility and clinical epidemiologic studies.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.

High-Throughput and Untargeted Metabolic Profiling Revealed the Potential Effect and Mechanisms of Paeoniflorin in Young Asthmatic Rats

Paeoniflorin (PF) is a multi-target monoterpenoid glycoside and possesses broad pharmacological functions, e.g., anti-inflammation, anti-depression, antitumor, abirritation, neuroprotection, antioxidant, and enhancing cognitive and learning ability. PF has gained a large amount of attention for its effect on asthma disease as the growth rate of asthma has increased in recent years. However, its mechanism of action on asthma is still unclear. In this study, we have explored the action mechanism of PF on asthma disease. Furthermore, high-throughput untargeted metabolic profiling was performed through ultraperformance liquid chromatography/electrospray ionization quadruple time-of-flight high-definition mass spectrometry (QA) UPLC-Q/TOF-MS combined with pattern recognition approaches and pathway analysis. A total of 20 potential biomarkers were discovered by UPLC/MS and urine metabolic profiling. The key pathways including the citrate cycle (the TCA cycle), pyrimidine metabolism, pentose phosphate pathway, tyrosine metabolism, and tryptophan metabolism were affected by PF. In conclusion, we have discovered metabolite biomarkers and revealed the therapeutic mechanism of PF based on liquid chromatography coupled with mass spectrometry untargeted metabolomics. The untargeted metabolomics combined with UPLC-MS is a useful tool for exploring the therapeutic mechanism and targets of PF in the treatment of asthma. Metabolomics combined with UPLC-MS is an integrated method to explore the metabolic mechanism of PF in the treatment of asthma rats and to reveal the potential targets, providing theoretical support for the study of the treatment of PF.

Plasma amino acid profile, a biomarker for visceral adipose tissue that can substitute for waist circumference in Japanese Americans

BACKGROUND

Greater visceral fat area (VFA) is associated with cardiometabolic outcomes. We sought to identify cross-sectional and longitudinal associations between amino acid (AA) levels and VFA in Japanese-Americans.

METHODS

From the cohort of 342 Japanese-American participants (51% men) in a study of diabetes risk factors who were free from diabetes, we measured levels of 20 AA by mass spectrometry, height, weight, waist circumference (WC), VFA, subcutaneous fat area by single-slice CT at the umbilicus. Using AA significantly associated with VFA in univariate analyses, we created a VFA prediction index, termed the 4A index. We compared area under receiver-operating characteristic curve (AUROC) of the 4A index to WC and an existing AA index (Yamakado et al. Clin Obes 2012) in classifying VFA at different cutoff values. We fit age-adjusted linear regression models to evaluate associations between AA levels and change in VFA over 5 years.

RESULTS

All 20 AA levels significantly detected VFA excess, but WC was better. The 4A index performed better than Yamakado index at classifying VFA ≥ 100 cm² (0.798, 0.807 vs. 0.677, 0.671 for men and women, p < 0.0033) and VFA ≥ sex-specific median values (0.797, 0.786 vs. 0.676, 0.629 for men and women, p < 0.0017). AA significantly associated with change in VFA over 5 years were asparagine, glutamate, glutamine, glycine, methionine, proline, threonine in men; and histidine, isoleucine, tyrosine in women (p < 0.05).

CONCLUSIONS

The 4A index can serve as a biomarker for VFA in Japanese-Americans and be considered for this purpose when WC is not available.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Tibet Kefir Milk Regulated Metabolic Changes Induced by High-Fat Diet via Amino Acids, Bile Acids, and Equol Metabolism in Human-Microbiota-Associated Rats

This study aimed to confirm the effects of Tibet kefir milk (TKM) on gut microbiota and metabolism. An obesity model was established by feeding a high-fat diet (HFD) to human-microbiota-associated rats. Next-generation sequencing and ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry were applied for gut microbiota and untargeted metabolomics, respectively. After 8 weeks of feeding, the enterotype in the HFD group was switched from ET1 (Prevotella/Akkermansia-dominant) to ET2 (Bacteroides/Akkermansia-dominant). Branched-chain amino-acids- and aromatic amino-acids-metabolism increased, and taurine-conjugated bile acids decreased in the HFD group. Compared with the HFD group, taurocholic acid increased in the TKM1 group, while l-threonine decreased, and equol, taurochenodeoxycholate, and taurodeoxycholic acid increased in the TKM2 group. The metabolite alteration suggested restorative bile acid metabolism, modified metabolic pattern of amino acids, and elevation of anti-obesity factors in the TKM-intervened animals. It can be deduced that changes by TKM intervention in the host gut metabolites are the major contributors to reducing fat deposition.

Serum metabolic signature of binge-like palatable food consumption in female rats by nuclear magnetic resonance spectroscopy

Maladaptive eating behavior is a growing public health problem and compulsively eating excessive food in a short time, or binge eating, is a key symptom of many eating disorders. In order to investigate the binge-like eating behavior in female rats, induced by intermittent food restrictions/refeeding and frustration stress, we analyzed for the first time the metabolic profile obtained from serum of rats, through nuclear magnetic resonance (NMR) spectroscopy. In this experimental protocol, rats were exposed to chow food restricting/refeeding and frustration stress manipulation. This stress procedure consists of 15 min exposure to the odor and sight of a familiar chocolate paste, without access to it, just before offering the palatable food. In this model, a "binge-eating episode" was considered the significantly higher palatable food consumption within 2 h in restricted and stressed rats (R + S) than in the other three experimental groups: rats with no food restriction and no stress (NR + NS), only stressed rats (NR + S) or only restricted rats (R + NS). Serum samples from these four different rat groups were collected. The statistical analysis of the ¹ H NMR spectral profiles of the four sets of samples pointed to O- and N-acetyl glycoproteins as the main biomarkers for the discrimination of restriction effects. Other metabolites, such as threonine, glycine, glutamine, acetate, pyruvate and lactate, showed trends that may be useful to understand metabolic pathways involved in eating disorders. This study suggested that NMR-based metabolomics is a suitable approach to detect biomarkers related to binge-eating behavior.

Obesity Connected Metabolic Changes in Type 2 Diabetic Patients Treated With Metformin

Metformin is widely used in the treatment of Type 2 Diabetes Mellitus (T2DM). However, it is known to have beneficial effects in many other conditions, including obesity and cancer. In this study, we aimed to investigate the metabolic effect of metformin in T2DM and its impact on obesity. A mass spectrometry (MS)-based metabolomics approach was used to analyze samples from two cohorts, including healthy lean and obese control, and lean as well as obese T2DM patients on metformin regimen in the last 6 months. The results show a clear group separation and sample clustering between the study groups due to both T2DM and metformin administration. Seventy-one metabolites were dysregulated in diabetic obese patients (30 up-regulated and 41 down-regulated), and their levels were unchanged with metformin administration. However, 30 metabolites were dysregulated (21 were up-regulated and 9 were down-regulated) and then restored to obese control levels by metformin administration in obese diabetic patients. Furthermore, in obese diabetic patients, the level of 10 metabolites was dysregulated only after metformin administration. Most of these dysregulated metabolites were dipeptides, aliphatic amino acids, nucleic acid derivatives, and urea cycle components. The metabolic pattern of 62 metabolites was persistent, and their levels were affected by neither T2DM nor metformin in obesity. Interestingly, 9 metabolites were significantly dysregulated between lean and obese cohorts due to T2DM and metformin regardless of the obesity status. These include arginine, citrulline, guanidoacetic acid, proline, alanine, taurine, 5-hydroxyindoleacetic acid, and 5-hydroxymethyluracil. Understanding the metabolic alterations taking place upon metformin treatment would shed light on possible molecular targets of metformin, especially in conditions like T2DM and obesity.

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.

Identification of the Perturbed Metabolic Pathways Associating With Renal Fibrosis and Evaluating Metabolome Changes of Pretreatment With Astragalus polysaccharide Through Liquid Chromatography Quadrupole Time-Of-Flight Mass Spectrometry

Renal fibrosis is glomerulosclerosis and renal tubulointerstitial fibrosis caused by the increase of interstitial cells and intercellular substances and the accumulation of extracellular matrix, and is a common pathological manifestation of renal disease progressing to end-stage renal failure. It has proved that Astragalus polysaccharide (AP) has curative effect on renal disease; however, its therapeutic mechanism on renal fibrosis is still unclear. Metabolomics approach provides an opportunity to identify novel molecular biomarkers. The purpose of this study is to study the changes of serum metabolic profile of rats with unilateral tubal ligation and replication of renal fibrosis model and the therapeutic effect of AP on it. The blood samples of rats in the control group, renal fibrosis model group, and AP treatment group collected on the 21st day were analyzed by metabolomics method based on UPLC-Q-TOF-MS. Principal component analysis (PCA) showed that clustering was obvious and significantly separated, and paired partial least squares discriminant analysis (OPLS-DA) was used for further analysis. Combined with the network databases such as HMDB and KEGG and a large number of literatures, 32 potential biomarkers related to renal fibrosis were preliminarily screened out and further verified by MS/MS secondary debris information. After pretreatment with AP, 20 biomarkers were significantly regulated, and correlated with phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, etc. It also revealed the metabolic changes of renal fibrosis and intervention effect of AP. These data uncover a link between metabolism and the molecular mechanism with potential implications in the understanding of the intervention effect of AP. Conclusively, UPLC-Q-TOF-MS-based metabolomics can be valuable and promising strategy to understand the disease mechanism and natural drug pretreatment.

Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers

Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets.

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.

Comparative metabolomics unveils molecular changes and metabolic networks of syringin against hepatitis B mice by untargeted mass spectrometry

Untargeted metabolomics technology was used to discover the metabolic pathways and biomarkers for revealing the potential biological mechanism of syringin on hepatitis B virus. Serum samples were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based comparative metabolomics coupled with pattern recognition methods and network pathway. In addition, the histopathology, HBV DNA detection of liver tissue, and biochemical indicators of liver function change were also explored for investigating the antiviral effect of syringin. In comparison to the model group, the metabolic profiles of the turbulence in transgenic mice tended to recover to the same as the control group after syringin therapy. A total of 33 potential biomarkers were determined to explore the metabolic disorders in the hepatitis B animal model, of which 25 were regulated by syringin, and 8 metabolic pathways, such as phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, glyoxylate and dicarboxylate metabolism, were involved. Syringin markedly reduced the liver pathology change, inhibited HBV DNA replication, and improved liver function. Amino acid metabolism is a potential target for the treatment of hepatitis B. The hepatoprotective effect of syringin may contribute to ameliorating oxidative stress and preventing protein and DNA replication. Comparative metabolomics is a promising tool for discovering metabolic pathways and biomarkers of the hepatitis B animal model as targets to reveal the effects and mechanism of syringin, which benefits the development of natural products and advances the treatment of diseases.

Untargeted metabolomics technology was used to discover the metabolic pathways and biomarkers for revealing the potential biological mechanism of syringin on hepatitis B virus.

High-throughput liquid chromatography mass-spectrometry-driven lipidomics discover metabolic biomarkers and pathways as promising targets to reveal the therapeutic effects of the Shenqi pill

Lipidomics, a branch of metabonomics, could provide a powerful technique for discovery of lipid molecules to reveal disease status and drug efficacy.

Exploring the metabolic biomarkers and pathway changes in crucian under carbonate alkalinity exposure using high-throughput metabolomics analysis based on UPLC-ESI-QTOF-MS

The aims of this study is to explore the metabolomic biomarker and pathway changes in crucian under carbonate alkalinity exposures using high-throughput metabolomics analysis based on ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-tandem mass spectrometry (UPLC-ESI-QTOF-MS) for carrying out adaptive evolution of fish in environmental exposures and understanding molecular physiological mechanisms of saline–alkali tolerance in fishes. Under 60 day exposure management, the UPLC-ESI-QTOF-MS technology, coupled with a pattern recognition approach and metabolic pathway analysis, was utilized to give insight into the metabolic biomarker and pathway changes. In addition, biochemical parameters in response to carbonate alkalinity in fish were detected for chronic impairment evaluation. A total of twenty-seven endogenous metabolites were identified to distinguish the biochemical changes in fish in clean water under exposure to different concentrations of carbonate alkalinity (CA); these mainly involved amino acid synthesis and metabolism, arachidonic acid metabolism, glyoxylate and dicarboxylate metabolism, pyruvate metabolism and the citrate cycle (TCA cycle). Compared with the control group, CA exposure increased the level of blood ammonia; TP; ALB; Gln in the liver and gills; GS; urea in blood, the liver and gills; CREA; CPS; Glu and LDH; and decreased the level of weight gain rate, oxygen consumption, discharge rate of ammonia, SOD, CAT, ALT, AST and Na⁺/K⁺-ATPase. At low concentrations, CA can change the normal metabolism of fish in terms of changing the osmotic pressure regulation capacity, antioxidant capacity, ammonia metabolism and liver and kidney function to adapt to the CA exposure environment. As the concentration of CA increases, various metabolic processes in crucian are inhibited, causing chronic damage to the body. The results show that the metabolomic strategy is a potentially powerful tool for identifying the mechanisms in response to different environmental exposomes and offers precious information about the chronic response of fish to CA.

We explore the metabolic biomarker and pathway changes accompanying the adaptive evolution of crucian subjected to carbonate alkalinity exposure, using UPLC-ESI-QTOF-MS, in order to understand the molecular physiological mechanisms of saline–alkali tolerance.

Metabolomics-driven identification of perturbations in amino acid and sphingolipid metabolism as therapeutic targets in a rat model of anorexia nervosa disease using chemometric analysis and a multivariate analysis platform

It is important to explore novel therapeutic targets and develop an effective strategy for the treatment of anorexia nervosa. In this work, serum samples were analyzed using ultra-performance liquid chromatography coupled with quadrupole time-of flight mass spectrometry (UPLC/Q-TOF MS) coupled with chemometric analysis and multivariate analysis to obtain the metabolites and their corresponding pathways. In addition, knock-in and knock-down of the key enzyme in vivo was performed to verify the reliability of the obtained metabolic pathway, which is closely associated with the anorexia nervosa pathomechanism and the potential targets. There were significant differences in the biochemical parameters between the model group and the control group. A total of 26 potential biomarkers were identified to resolve the difference between the control and model rats, which were closely related to amino acid metabolism, sphingolipid metabolism, arachidonic acid metabolism, the citrate cycle, and so forth. According to the ingenuity pathway analysis, we further elucidated the relationship between the gene, protein, and metabolite alteration in anorexia nervosa, which are involved in cellular compromise, lipid metabolism, small molecule biochemistry, cell signaling, molecular transport, nucleic acid metabolism, cell morphology, cellular function and maintenance. Arginosuccinate synthetase (ASS) deficiency was accompanied by a significant downregulation of the β-endorphin and ghrelin in the animal models. The metabolites and pathways obtained using the metabolomics strategy may provide valuable information for the early treatment for anorexia nervosa.

It is important to explore novel therapeutic targets and develop an effective strategy for the treatment of anorexia nervosa.

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.

Ultra-performance liquid chromatography-mass spectrometry-based metabolomics reveals Huangqiliuyi decoction attenuates abnormal metabolism as a novel therapeutic opportunity for type 2 diabetes

Background: As a typical chronic metabolic disease, type 2 diabetes mellitus causes a heavy health-care burden to society. In this study, we applied the metabolomics strategy to explore the potential molecular mechanism of the Huangqiliuyi decoction (HQLYD) for type-2 diabetes (T2D). Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) combined with pattern recognition methods was utilized to select specific metabolites closely associated with HQLYD. Biomarker pathway analysis and biological network were utilized to uncover the therapeutic effect and action mechanism related to HQLYD. A total of twenty-five biomarkers were identified in the animal model, in which sixteen biomarkers are associated with HQLYD treatment for T2D. They attenuated the abnormalities of metabolic pathways such as phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, and the citrate cycle. HQLYD also significantly elevated the serum FINS and SOD, GSP-x level in the liver and kidney, and reduced the serum TC, TG, HDL, LDL, urea, Scr, AST, ALT, FBG, IRS, MDA, and CAT level. We found that the therapeutic mechanism of HQLYD against T2D affected amino acid metabolism, glucose metabolism and lipid metabolism. Metabolomics revealed that the Huangqiliuyi decoction attenuates abnormal metabolism as a novel therapeutic opportunity for type 2 diabetes.

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.

Ultra-performance liquid chromatography/mass spectrometry technology and high-throughput metabolomics for deciphering the preventive mechanism of mirabilite on colorectal cancer via the modulation of complex metabolic networks

Colorectal cancer (CRC) is a highly virulent and malignant disease and always accompanied by metabolic disorders. Currently, there are no effective therapeutic drugs for the treatment of CRC. High-throughput metabolomics approaches have been used to unveil the metabolic pathways related to several diseases. In this study, ultra-performance liquid chromatography/mass spectrometry-based high-throughput metabolomics was used for deciphering the potential preventive mechanism of mirabilite on CRC via the modulation of the associated metabolic disorders; a total of 28 differential biomarkers, including indole acetaldehyde, 5-hydroxyindoleacetic acid, hypoxanthine, retinal, retinal ester, linoleic acid, stearic acid, 6-deoxocastasterone, 2-hydroxybutyric acid and LysoPC, were identified in the APCmin/+ mice. These differential biomarkers are involved in the tryptophan metabolism, glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. Note that these biomarkers and their disturbed metabolic pathways were also regulated by mirabilite. It has been found that the prevention of CRC by mirabilite is mainly associated with tryptophan metabolism; this study shows that high-throughput metabolomics can reveal the perturbed metabolic disorders targeted in the action mechanism of drug treatment.

Analysis of plasma metabolic profiling and evaluation of the effect of the intake of Angelica keiskei using metabolomics and lipidomics

ETHNOPHARMACOLOGICAL RELEVANCE

Angelica keiskei contains many bioactive components with anti-oxidative and anti-inflammatory effects. It is also effective for the treatment of diabetes mellitus, hypertension, and arteriosclerosis, but the relationships between these effects and the active components in the herb have not been studied.

AIM OF THE STUDY

We aimed to confirm the effects of Angelica keiskei on humans.

MATERIALS AND METHODS

A metabolomics and lipidomics study was performed using human plasma samples from 20 subjects after the intake of Angelica keiskei, and the components of Angelica keiskei in the plasma were profiled. UPLC-Orbitrap-MS was used to analyze the plasma and plant extracts, and multivariate analysis and correlation studies between the exogenous components from plant and endogenous metabolite in plasma were performed.

RESULTS

The levels of the 14 metabolites including kynurenic acid, prostaglandin E1, chenodeoxycholic acid, lysoPC (18:1), lysoPC (18:2), lysoPC (20:3), lysoPC (20:4), lysoPC (22:6), PC (34:1), PC (34:2), PC (38:3), PC (38:4), PC (38:6) and PC (40:7) in the plasma were changed. By monitoring the components originating from Angelica keiskei in plasma, five components including 5-methoxypsoralen, 8-methoxypsoralen, 4-hydroxyderricin, xanthoangelol B and xanthoangelol F were detected and they reduced the levels of bile acids and fatty acids.

CONCLUSIONS

The levels of the metabolites, including bile acids, amino acids, glycerophospholipids and fatty acids, in the plasma were changed, and 14 significantly changed metabolites were closely related to the preventive effect against liver diseases, type 2 diabetes, anemia, obesity, atherosclerosis, depression and anti-inflammatory effects. The five components of Angelica keiskei were related the modulatory activity of reducing the levels of bile acids and fatty acids.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Novel applications of mass spectrometry-based metabolomics in herbal medicines and its active ingredients: Current evidence

Current evidence shows that herbal medicines could be beneficial for the treatment of various diseases. However, the complexities present in chemical compositions of herbal medicines are currently an obstacle for the progression of herbal medicines, which involve unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, non-specific features for drug metabolism, etc. To overcome those issues, metabolomics can be a great to improve and understand herbal medicines from the small-molecule metabolism level. Metabolomics could solve scientific difficulties with herbal medicines from a metabolic perspective, and promote drug discovery and development. In recent years, mass spectrometry-based metabolomics was widely applied for the analysis of herbal constituents in vivo and in vitro. In this review, we highlight the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, and to enhance their biomedical value in biomedicine, to shed light on the aid that mass spectrometry-based metabolomics can offer to the investigation of its active ingredients, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. © 2019 Wiley Periodicals, Inc. Mass Spec Rev.

Gene-Environment Interactions on Body Fat Distribution

The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.

Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants

Although Alzheimer's disease (AD) is highly heritable, genetic variants are known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multiomics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer's Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fastings cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between carbamoyl-phosphate synthase 1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.

High-throughput metabolomics and ingenuity pathway approach reveals the pharmacological effect and targets of Ginsenoside Rg1 in Alzheimer's disease mice

Ginsenoside Rg1, a natural triterpenoid saponins compound isolated from the Panax species, has been found to possess neuroprotective properties in neurodegenerative diseases such as Alzheimer's disease (AD). However, its pharmacological mechanism on AD has not been studied. In this study, an ultra-performance liquid chromatography combined with quadrupole time of-flight mass spectrometry (UPLC-Q/TOF-MS) based non-targeted metabolomics strategy was performed to explore the mechanism of Ginsenoside Rg1 protecting against AD mice by characterizing metabolic biomarkers and regulation pathways changes. A total of nineteen potential metabolites in serum were discovered and identified to manifest the difference between wild-type mice and triple transgenic mice in control and model group, respectively. Fourteen potential metabolites involved in ten metabolic pathways such as linoleic acid metabolism, arachidonic acid metabolism, tryptophan metabolism and sphingolipid metabolism were affected by Rg1. From the ingenuity pathway analysis (IPA) platform, the relationship between gene, protein, metabolites alteration and protective activity of ginsenoside Rg1 in AD mice are deeply resolved, which refers to increased level of albumin, amino acid metabolism and molecular transport. In addition, quantitative analysis of key enzymes in the disturbed pathways by proteomics parallel reaction was employed to verify changed metabolic pathway under Ginsenoside Rg1. The UPLC-Q/TOF-MS based serum metabolomics method brings about new insights into the pharmacodynamic studies of Ginsenoside Rg1 on AD mice.

Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine

The global epidemic of obesity is a major public health problem today. Obesity increases the risk of many chronic diseases, such as type 2 diabetes, coronary heart disease, and certain types of cancer, and is associated with lower life expectancy. The body mass index (BMI), which is currently used to classify obesity, is only an imperfect measure of abnormal or excessive body fat accumulation. Studies have shown that waist circumference as a measure of fat distribution may improve disease prediction. More elaborate techniques such as magnetic resonance imaging are increasingly available to assess body fat distribution, but these measures are not readily available in routine clinical practice, and health-relevant cut-offs not yet been established. The measurement of biomarkers that reflect the underlying biological mechanisms for the increased disease risk may be an alternative approach to characterize the relevant obesity phenotype. The insulin/insulin-like growth factor (IGF) axis and chronic low-grade inflammation have been identified as major pathways. In addition, specific adipokines such as leptin, adiponectin and resistin have been related to obesity-associated health outcomes. This biomarker research, which is currently further developed with the application of high throughput methods, gives important insights in obesity-related disease etiology and pathophysiological pathways and may be used to better characterize obese persons at high risk of disease development and target disease-causing biomarkers in personalized prevention strategies.

Metabolomics biotechnology, applications, and future trends: a systematic review

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed. Mass spectrometry-based metabolomics has been used in a variety of disease research areas. However, the deep research of metabolites remains a difficult and lengthy process. Fortunately, mass spectrometry is considered to be a universal tool with high specificity and sensitivity and is widely used around the world. Mass spectrometry technology has been applied to various basic disciplines, providing technical support for the discovery and identification of endogenous substances in living organisms. The combination of metabolomics and mass spectrometry is of great significance for the discovery and identification of metabolite biomarkers. The mass spectrometry tool could further improve and develop the exploratory research of the life sciences. This mini review discusses metabolomics biotechnology with a focus on recent applications of metabolomics as a powerful tool to elucidate metabolic disturbances and the related mechanisms of diseases.

Given the highly increased incidence of human diseases, a better understanding of the related mechanisms regarding endogenous metabolism is urgently needed.

High-throughput untargeted metabolomics and chemometrics reveals pharmacological action and molecular mechanism of chuanxiong by ultra performance liquid chromatography combined with quadrupole-time-of-flight-mass spectrometry

Metabolomics methods can be used to explore the effect mechanisms underlying treatments with traditional medicine. Lung cancer (LC) causes the highest morbidity and mortality among tumors disease, and has become a serious public health problem. Chuanxiong (CX) is a dried rhizome of Ligusticum Chuanxiong Hort., often used in traditional Chinese medicine and has been widely used in the treatment for tumors. However, the pharmacological effect of CX on the metabolism process of LC mice is still unclear. This study used high-throughput untargeted metabolomics aims to discover biomarkers and metabolic pathways of LC as a potential target to provide insight into the pharmacological action and effective mechanism of CX against LC. The precise structural identification of the LC biomarker has been established using ultra performance liquid chromatography (UPLC) combined with quadrupole-time-of-flight-mass spectrometry (Q-TOF-MS) technology. UPLC-Q-TOF-MS and chemometrics methods were used to analyze the blood metabolism of LC model mice, and revealed the intervention effect of CX on LC model mice and potential therapeutic targets. The results showed that the metabolic profile clustering among the groups was obvious, and 31 potential biomarkers were finally locked, involving 7 related metabolic pathways. After treatment with CX, we found that 22 kinds of biomarkers were recalled to the main metabolic pathway which are associated with lipid metabolism. This study provides an effective biomarker reference for early clinical diagnosis of LC, and also provides a foundation for the expansion of new drugs for CX treatment of LC.

Metabolomics methods can be used to explore the effect mechanisms underlying treatments with traditional medicine.

Exploring potential biomarkers of coronary heart disease treated by Jing Zhi Guan Xin Pian using high-throughput metabolomics

Coronary heart disease (CHD) is a relatively complex disease characterized by narrowing of the arterial lumen and reduction of blood flow to the heart. There is no effective early diagnosis and prevention method. Jing Zhi Guan Xin Pian (JZGXP) is a new preparation prepared from the effective extract of Guanxin II. It is made of five components of traditional Chinese medicine and functions by promoting blood circulation and removing blood stasis and is used for the treatment of CHD and angina pectoris. In our study, a CHD rat model was prepared using a high-fat diet combined with intraperitoneal injection of vitamin D3. Clinical biochemical indexes (TG, CHO and HDL-C), histopathology (coronary and myocardial tissue), electrocardiogram and cardiac indexes were used to evaluate the efficacy of JZGXP in the treatment of CHD model rats. UPLC-HDMS-based metabolomics techniques were used to find metabolic profiles, biomarkers and related metabolic pathways in CHD models and to evaluate the effects of JZGXP on them. At the same time, the targets of JZGXP for the treatment of CHD were analyzed. Our study ultimately identified 25 biomarkers associated with CHD models. Further studies found that these 25 biomarkers involved 9 metabolic pathways in the body and found that JZGXP can recall 21 biomarkers in the urine of model rats and these biomarkers involve nine metabolic pathways. Finally, the targets of JZGXP for the treatment of CHD were β-alanine metabolism and tyrosine metabolism, i.e. amino acids metabolism. This study showed that metabolomics technology is effective for exploring potential biomarkers associated with syndromes or diseases and the therapeutic mechanisms of a traditional Chinese medicine formulation.

Coronary heart disease (CHD) is a relatively complex disease characterized by narrowing of the arterial lumen and reduction of blood flow to the heart.

Targeting regulation of tryptophan metabolism for colorectal cancer therapy: a systematic review

Colorectal cancer (CRC) is one of the most malignant cancers resulting from abnormal metabolism alterations. As one of the essential amino acids, tryptophan has a variety of physiological functions, closely related to regulation of immune system, central nervous system, gastrointestinal nervous system and intestinal microflora. Colorectal cancer, a type of high-grade malignancy disease, stems from a variety of factors and often accompanies inflammatory reactions, dysbacteriosis, and metabolic disorders. Colorectal cancer accompanies inflammation and imbalance of intestinal microbiota and affects tryptophan metabolism. It is known that metabolites, rate-limiting enzymes, and ARH in tryptophan metabolism are associated with the development of CRC. Specifically, IDO1 may be a potential therapeutic target in colorectal cancer treatment. Furthermore, the reduction of tryptophan amount is proportional to the poor quality of life for colorectal cancer patients. This paper aims to discuss the role of tryptophan metabolism in a normal organism and investigate the relationship between this amino acid and colorectal cancer. This study is expected to provide theoretical support for research related to targeted therapy for colorectal cancer. Furthermore, strategies that modify tryptophan metabolism, effectively inhibiting tumor progression, may be more effective for CRC treatment.

Colorectal cancer (CRC) is one of the most malignant cancers resulting from abnormal metabolism alterations.

Alginate oligosaccharide (AOS) improves immuno-metabolic systems by inhibiting STOML2 overexpression in high-fat-diet-induced obese zebrafish

AOS improves immuno-metabolism systems in high-fat-died-induced obese zebrafish by regulating STOML2.

The brominated flame retardant BDE 47 upregulates purine metabolism and mitochondrial respiration to promote adipocyte differentiation

Adipocyte differentiation is closely associated with obesity and obesity-induced metabolic disorders. Epidemiological studies have demonstrated the association of obesity with environmental pollutants, such as polybrominated diphenyl ethers (PBDEs), common flame retardants in various consumer products. However, their obesogenic effects and mechanism are underexplored. We employed non-targeted metabolomics studies based on liquid chromatography-high resolution mass spectrometry to determine how 2,2',4,4'-tetra-brominated biphenyl ether (BDE 47), one of the main congeners of PBDEs detected in human tissue, promotes adipocyte differentiation of mouse preadipocyte 3 T3-L1 cells. The promoting effects of BDE 47 exposure (5 or 10 μM) on adipocyte differentiation were confirmed by enhancing lipid accumulation and expression levels of biomarkers of adipogenesis. For the first time, we demonstrated that BDE 47 upregulated purine metabolism and altered glutathione metabolism to promote oxidative stress and uric acid production in adipocytes. BDE 47 also elevated mitochondrial respiration and glycolysis in adipocytes to induce more ATP to combat oxidative stress. Antioxidant treatments, including the suppression of xanthine oxidase, inhibited the effects of BDE 47 on inducing oxidative stress and lipid accumulation. BDE 47 may be a potential environmental obesogen by providing a permissive oxidative environment to induce adipocyte differentiation.

Roux-en-Y gastric bypass surgery alters serum metabolites and fatty acids in patients with morbid obesity

AIM

Bariatric surgery induces significant weight loss, increases insulin sensitivity, and reduces mortality, but the underlying mechanisms are not clear. It was hypothesized that Roux-en-Y gastric bypass (RYGB) surgery improves metabolic profile along with weight loss. The objective of this pilot study was to evaluate changes in serum metabolites and fatty acids (FA) at 2 weeks and 6 months after RYGB.

MATERIALS AND METHODS

Serum samples were collected pre-surgery, at 2 weeks and 6 months post-surgery from 20 patients undergoing RYGB surgery. Serum non-esterified free FA (NEFA) were measured. Serum metabolites and FA were measured using nuclear magnetic resonance spectroscopy and improved direct fatty acid methyl ester synthesis and the gas chromatography/mass spectrometry method, respectively, in subjects who completed follow-up at 6 months (n = 8).

RESULTS

Mean (standard deviation) percent total weight loss was 6.70% (1.7) and 24.91% (6.63) at 2 weeks (n = 15) and 6 months (n = 8) post-surgery, respectively. NEFA were significantly reduced at 6 months post-surgery (P = 0.001, n = 8). Serum branched chain amino acids, 2-aminobutyrate, butyrate, 2-hydroxybutyrate, 3-hydroxybutyrate, acetone, 2-methylglutarate, and 2-oxoisocaproate were significantly reduced, while serum alanine, glycine, pyruvate, and taurine were significantly elevated at 6 months post-surgery compared with pre-surgery (n = 8, P < 0.05). Also, serum FA C10:0, C13:0, C14:0, C15:0, and C18:0 increased significantly (n = 8, P < 0.05) by 6 months post-surgery.

CONCLUSIONS

Changes in serum metabolites and FA at 6 months post-RYGB surgery in this pilot study with limited number of participants are suggestive of metabolic improvement; larger studies are warranted for confirmation.

High-throughput metabolomics for discovering metabolic biomarkers from intestinal tumorigenesis in APC min/+ mice based on liquid chromatography/mass spectrometry

As a major public health concern, colon cancer is one of the most common cancer types, which is also the second cause of cancer death in developed countries and the third most common cancer in other parts of the world. It was reported that patients diagnosed at early stage have a chance to obtain 5-year survival rates at least compared to patients with late stage. Facing the multistep process in intestinal tumorigenesis, there is an urgent need to develop more effective early detection strategies for ameliorating the patient clinical outcome. Metabolomics open up a novel avenue of seeking valuable potential biomarkers for assessing disease severity and prognosticating course by dynamic snapshot of small molecule metabolites. The study aims to provide deeper insights into the discovery, identification and functional pathways analysis of differentially expressed metabolites in intestinal tumorigenesis in APC ^min/+ mice used by the serum metabolomics, and bring about useful information for further effective prevention and treatment of the disease. 17 marker metabolites and related metabolism pathway were identified using non-targeted metabolomics based on liquid chromatography/mass spectrometry (LC/MS) associated with multivariate statistical analysis. The ingenuity pathway analysis platform involved multiple-pathways was applied to metabolic network analysis for further understanding the relationship between functional metabolic pathways and disease.

Side effects of methotrexate therapy for rheumatoid arthritis: A systematic review

Methotrexate (MTX) is used as an anchor disease-modifying anti-rheumatic drugs (DMARDs) in treating rheumatoid arthritis (RA) because of its potent efficacy and tolerability. MTX benefits a large number of RA patients but partially suffered from side effects. A variety of side effects can be associated with MTX when treating RA patients, from mild to severe or discontinuation of the treatment. In this report, we reviewed the possible side effects that MTX might cause from the most common gastrointestinal toxicity effects to less frequent malignant diseases. In order to achieve regimen with less side effects, the administration of MTX with appropriate dose and a careful pretreatment inspection is necessary. Further investigations are required when combining MTX with other drugs so as to enhance the efficacy and reduce side effects at the same time. The management of MTX treatment is also discussed to provide strategies for occurred side effects. Thus, this review will provide scholars with a comprehensive understanding the side effects of MTX administration by RA patients.

High-throughput lipidomics reveal mirabilite regulating lipid metabolism as anticancer therapeutics

Altered lipid metabolism is an emerging hallmark of cancers. Mirabilite has a therapeutic effect on colorectal cancer (CRC); however, its metabolic mechanism remains unclear. This study aims to explore the potential therapeutic targets of mirabilite protection against colorectal cancer in APC^min/+ mice model. Oral administration of mirabilite was started from the ninth month, while the same dosage of distilled water was given to both the control group and the model group. Based on lipidomics, we collected serum samples of all mice at the 20^th week and used a non-targeted method to identify the lipid biomarkers of CRC. Compared with C57BL/6J mice, the metabolic profile of CRC model mice was significantly disturbed, and we identified that 25 lipid-related biomarkers, including linoleic acid, 2-hydroxybutyric acid, 6-deoxocastasterone, hypoxanthine, PC(16:1), PC(18:4), and retinyl acetate, were associated with CRC. According to the abovementioned results, there were six lipid molecules with significant differences that can be used as new targets for handling of CRC through six metabolic pathways, namely, linoleic acid metabolism, retinol metabolism, propanoate metabolism, arachidonic acid metabolism, biosynthesis of unsaturated fatty acids and purine metabolism. Compared with the model group, the metabolic profiles of these disorders tend to recover after treatment. These results indicated that the lipid molecules associated with CRC were regulated by mirabilite. In addition, we identified seven key lipid molecules, of which four had statistical significance. After administration of mirabilite, all disordered metabolic pathways showed different degrees of regulation. In conclusion, high-throughput lipidomics approach revealed mirabilite regulating the altered lipid metabolism as anticancer therapeutics.

Altered lipid metabolism is an emerging hallmark of cancers.

Advances in mass spectrometry-based metabolomics for investigation of metabolites

Metabolomics is the systematic study of all the metabolites present within a biological system, which consists of a mass of molecules, having a variety of physical and chemical properties and existing over an extensive dynamic range in biological samples. Diverse analytical techniques are needed to achieve higher coverage of metabolites. The application of mass spectrometry (MS) in metabolomics has increased exponentially since the discovery and development of electrospray ionization and matrix-assisted laser desorption ionization techniques. Significant advances have also occurred in separation-based MS techniques (gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, and ion mobility-mass spectrometry), as well as separation-free MS techniques (direct infusion-mass spectrometry, matrix-assisted laser desorption ionization-mass spectrometry, mass spectrometry imaging, and direct analysis in real time mass spectrometry) in the past decades. This review presents a brief overview of the recent advanced MS techniques and their latest applications in metabolomics. The software/websites for MS result analyses are also reviewed.

Metabolomics is the systematic study of all the metabolites present within a biological system, supply functional information and has received extensive attention in the field of life sciences.

Network pharmacology combined with functional metabolomics discover bile acid metabolism as a promising target for mirabilite against colorectal cancer

In this study, a combination of network pharmacology and metabolomics was used to explore the mechanism by which mirabilite regulates bile acid metabolism in the treatment of colorectal cancer.

Plasma metabolic profiling and novel metabolite biomarkers for diagnosing prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer death among men and associated with profound metabolic changes.