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Metabolic Profile of C-Prenyl Coumarins Using Mass Spectrometry-Based Metabolomics

ABSTRACT: C-prenyl coumarins (C-PYCs) are compounds with similar structures and various bioactivities, which are widely distributed in medicinal plants. Until now, the metabolic characterizations of C-PYCs and the relationship between metabolism and bioactivities remain unclear. In this study, ultra-performance chromatography electrospray ionization quadrupole time-of-flight mass spectrometry-based metabolomics (UPLC-ESI-QTOF-MS) was firstly used to determine the metabolic characterizations of three C-PYCs, including meranzin hydrate (MH), isomeranzin (ISM), and meranzin (MER). In total, 52 metabolites were identified, and all of them were found to be novel metabolites. Among these metabolites, 10 were from MH, 22 were from ISM, and 20 were from MER. The major metabolic pathways of these C-PYCs were hydroxylation, dehydrogenation, demethylation, and conjugation with cysteine, N-acetylcysteine, and glucuronide. The metabolic rate of MH was much lower than ISM and MER, which was only 27.1% in MLM and 8.7% in HLM, respectively. Additionally, recombinant cytochrome P450 (CYP) screening showed that CYP1A1, 2B6, 3A4, and 3A5 were the major metabolic enzymes involved in the formation of metabolites. Further bioactivity assays indicated that all of these three C-PYCs exhibited anti-inflammatory activity, but the effects of ISM and MER were slightly higher than MH, accompanied by a significant decrease in inflammatory cytokines transcription induced by lipopolysaccharide (LPS) in macrophages RAW 264.7. Taken together, the metabolic characterizations of the three C-PYCs suggested that the side chain of the prenyl group may impact the metabolism and biological activity of C-PYCs.

SUBMITTER: Cheng Y

PROVIDER: S-EPMC8588418 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAssessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets.ResultsWe apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well.ConclusionsThis research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

Project description:Both obesity and the metabolic syndrome are risk factors for type 2 diabetes and cardiovascular disease. Identification of novel biomarkers are needed to distinguish metabolic syndrome from equally obese individuals in order to direct them to early interventions that reduce their risk of developing further health problems. We utilized mass spectrometry-based targeted metabolic profiling of 221 metabolites to evaluate the associations between metabolite profiles and established metabolic syndrome criteria (i.e. elevated waist circumference, hypertension, elevated fasting glucose, elevated triglycerides, and low high-density lipoprotein cholesterol) in plasma samples from obese men ( n = 29; BMI = 35.5 ± 5.2 kg/m2) and women ( n = 40; 34.9 ± 6.7 kg/m2), of which 26 met the criteria for metabolic syndrome (17 men and 9 women). Compared to obese individuals without metabolic syndrome, univariate statistical analysis and partial least squares discriminant analysis showed that a specific group of metabolites from multiple metabolic pathways (i.e. purine metabolism, valine, leucine and isoleucine degradation, and tryptophan metabolism) were associated with the presence of metabolic syndrome. Receiver operating characteristic curves generated based on the PLS-DA models showed excellent areas under the curve (0.85 and 0.96, for metabolites only model and enhanced metabolites model, respectively), high specificities (0.86 and 0.93), and good sensitivities (0.71 and 0.91). Moreover, principal component analysis revealed that metabolic profiles can be used to further differentiate metabolic syndrome with 3 versus 4-5 metabolic syndrome criteria. Collectively, these findings support targeted metabolomics approaches to distinguish metabolic syndrome from obesity alone, and to stratify metabolic syndrome status based on the number of criteria met. Impact statement We utilized mass spectrometry-based targeted metabolic profiling of 221 metabolites to evaluate the associations between metabolite profiles and established MetS criteria. To our best knowledge, the findings of this study provide the first evidence that metabolic profiles can be used to differentiate participants with MetS from similarly obese individuals who do not meet established criteria of MetS. Furthermore, the study demonstrated that within MetS participants, their unique metabolic profiles correlated to the number of criteria used for MetS determination. Taken together, this metabolic profiling approach can potentially serve as a novel tool for MetS detection and monitoring, and provide useful metabolic information for future interventions targeting obesity and MetS.

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Metabolic Profile of C-Prenyl Coumarins Using Mass Spectrometry-Based Metabolomics

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