1H-NMR Metabolomics Analysis of the Effect of Rubusoside on Serum Metabolites of Golden Hamsters on a High-Fat Diet

Rubusoside mitigates neuroinflammation and cellular apoptosis in Parkinson's disease, and alters gut microbiota and metabolite composition

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative condition characterized by the progressive loss of dopaminergic neurons within the substantia nigra. Neuroinflammation plays a pivotal role in the pathogenesis of PD, involving the activation of microglia cells, heightened production of proinflammatory cytokines, and perturbations in the composition of the gut microbiota. Rubusoside (Ru), the principal steviol bisglucoside present in Rubus chingii var. suavissimus (S.K.Lee) L.T.Lu (Rosaceae), has been documented for its anti-inflammatory properties in diverse disease models. Nonetheless, there is an imperative need to comprehensively assess and elucidate the protective and anti-inflammatory attributes of Ru concerning PD, as well as to uncover the underlying mechanism involved.

OBJECTIVE

The aim of this study is to evaluate the neuroprotective and anti-inflammatory effects of Ru on PD and investigate its potential mechanisms associated with microbes.

RESEARCH DESIGN AND METHODS

We pre-treated mice and cell lines with Ru in order to simulate the progression of PD and the neuroinflammatory state. The mouse model was induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), SN4741 cells were induced by 1-methyl-4-phenylpyridine (mpp+), and BV-2 cells were induced by lipopolysaccharide (LPS). We assessed the impact of Ru on motor function, neuroinflammation, neuron apoptosis, the composition of gut microbes, and their metabolites.

RESULTS

Ru treatment reduces the release of pro-inflammatory mediators by inhibiting microglia activation. It also prevents neuronal apoptosis, thereby safeguarding dopaminergic neurons and ameliorating motor dysfunction. Furthermore, it induces alterations in the fecal microbiota composition and metabolites profile in PD mice. In vitro experiments have demonstrated that Ru inhibits neuronal apoptosis in SN4741 cells induced by mpp+, suppresses the production of pro-inflammatory mediators, and activates the c-Jun N-terminal kinase (JNK), mitogen-activated protein kinase (p38 MAPK), and nuclear factor kappa-B (NF-κB) signaling pathways.

CONCLUSION

Ru exhibits inhibitory effects on the MPTP-induced PD model by mitigating neuroinflammation and neuronal apoptosis while also inducing changes in the gut microbiota and metabolite composition.

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).

Uncovering the Gut-Liver Axis Biomarkers for Predicting Metabolic Burden in Mice

Western diet (WD) intake, aging, and inactivation of farnesoid X receptor (FXR) are risk factors for metabolic and chronic inflammation-related health issues ranging from metabolic dysfunction-associated steatotic liver disease (MASLD) to dementia. The progression of MASLD can be escalated when those risks are combined. Inactivation of FXR, the receptor for bile acid (BA), is cancer prone in both humans and mice. The current study used multi-omics including hepatic transcripts, liver, serum, and urine metabolites, hepatic BAs, as well as gut microbiota from mouse models to classify those risks using machine learning. A linear support vector machine with K-fold cross-validation was used for classification and feature selection. We have identified that increased urine sucrose alone achieved 91% accuracy in predicting WD intake. Hepatic lithocholic acid and serum pyruvate had 100% and 95% accuracy, respectively, to classify age. Urine metabolites (decreased creatinine and taurine as well as increased succinate) or increased gut bacteria (Dorea, Dehalobacterium, and Oscillospira) could predict FXR deactivation with greater than 90% accuracy. Human disease relevance is partly revealed using the metabolite-disease interaction network. Transcriptomics data were also compared with the human liver disease datasets. WD-reduced hepatic Cyp39a1 (cytochrome P450 family 39 subfamily a member 1) and increased Gramd1b (GRAM domain containing 1B) were also changed in human liver cancer and metabolic liver disease, respectively. Together, our data contribute to the identification of noninvasive biomarkers within the gut-liver axis to predict metabolic status.

Exosome-Enriched Plasma Analysis as a Tool for the Early Detection of Hypertensive Gestations

Hypertensive disorders of pregnancy are closely associated with prematurity, stillbirth, and maternal morbidity and mortality. The onset of hypertensive disorders of pregnancy (HDP) is generally noticed after the 20th week of gestation, limiting earlier intervention. The placenta is directly responsible for modulating local and systemic physiology by communicating using mechanisms such as the release of extracellular vesicles, especially exosomes. In this study, we postulated that an analysis of exosome-enriched maternal plasma could provide a more focused and applicable approach for diagnosing HDP earlier in pregnancy. Therefore, the peripheral blood plasma of 24 pregnant women (11 controls, 13 HDP) was collected between 20th and 24th gestational weeks and centrifuged for exosome enrichment. Exosome-enriched plasma samples were analyzed by Raman spectroscopy and by proton nuclear magnetic resonance metabolomics (¹H NMR). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the Raman data, from the spectral region of 600–1,800 cm^–1, to determine its potential to discriminate between groups. Using principal component analysis, we were able to differentiate the two groups, with 89% of all variances found in the first three principal components. In patients with HDP, most significant differences in Raman bands intensity were found for sphingomyelin, acetyl CoA, methionine, DNA, RNA, phenylalanine, tryptophan, carotenoids, tyrosine, arginine, leucine, amide I and III, and phospholipids. The ¹H NMR analysis showed reduced levels of D-glucose, L-proline, L-tyrosine, glycine, and anserine in HDP, while levels of 2-hydroxyvalerate, polyunsaturated fatty acids, and very-low-density lipoprotein (VLDL) were increased. ¹H NMR results were able to assign an unknown sample to either the control or HDP groups at a precision of 88.3% using orthogonal partial least squares discriminant analysis and 87% using logistic regression analysis. Our results suggested that an analysis of exosome-enriched plasma could provide an initial assessment of placental function at the maternal-fetal interface and aid HDP diagnosis, prognosis, and treatment, as well as to detect novel, early biomarkers for HDP.

Integrating Constituents Absorbed into Blood, Network Pharmacology, and Quantitative Analysis to Reveal the Active Components in Rubus chingii var. suavissimus that Regulate Lipid Metabolism Disorder

Rubus chingii var. suavissimus (S. K. Lee) L. T. Lu (RS)—a sweet plant also known as Tiancha distributed in the south of China where it is used as a beverage—recently gained extensive attention as adjuvant therapy of diabetes and hypertension. Although pharmacological studies indicate that RS has beneficial effects in regulating lipid metabolism disorder characteristics, the active chemicals responsible for this effect remains unclear. The present study aims to predict the effective substances of RS on regulating lipid metabolism disorder through the analysis of the chemical profile of RS, the absorbed prototype components in rat plasma, and network pharmacology. Also, a UPLC method able to quantify the screened potential effective chemicals of RS products was established. First, a total of 69 components—including diterpene, triterpenoids, flavonoids, polyphenols, and lignans—were systematically characterized in RS. Of those, 50 compounds were detected in the plasma of rats administered with RS extract. Through network pharmacology, 9 potential effective components, 71 target genes, and 20 pathways were predicted to be involved in RS-mediated regulation of lipid metabolism disorder. The quantitative analysis suggested that the contents of potential effective components varied among samples from different marketplaces. In conclusion, the presented results provide a chemical basis for further research of Rubus chingii var. suavissimus.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.