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Liquid Chromatograph-Mass Spectrometry-Based Non-targeted Metabolomics Discovery of Potential Endogenous Biomarkers Associated With Prostatitis Rats to Reveal the Effects of Magnoflorine

ABSTRACT: Magnoflorine (Mag) has multiple pharmacological activities for the prevention and treatment of prostatitis. However, its molecular mechanisms andpharmacological targets are not clear. In this study, the ultra-performance liquid tandem mass spectrometry-based metabolomics method was used to clarify the intervention of Mag against prostatitis and the biological mechanism. A total of 25 biomarkers associated with the prostatitis model were identified by metabolomics, and a number of metabolic pathways closely related to the model were obtained by MetPA analysis. After given Mag treatment, the results of each indicator were shown that Mag alkaloid could inhibit the development of prostatitis effectively. We found that Mag had regulative effects on potential biomarkers of prostatitis model, which can regulate them to the control group. Our results indicated that alkaloids have an effective intervention therapy for prostatitis, and five types of metabolic pathways closely related to prostatitis model were obtained, including phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, tyrosine metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism. This study has provided the basic experimental data for the development of Mag in the prevention and treatment of prostatitis.

SUBMITTER: Yuan Y

PROVIDER: S-EPMC8591080 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Corrigendum: Liquid chromatograph-mass spectrometry-based non-targeted metabolomics discovery of potential endogenous biomarkers associated with prostatitis rats to reveal the effects of magnoflorine.

Project description:[This corrects the article DOI: 10.3389/fphar.2021.741378.].

| S-EPMC9597864 | biostudies-literature

Isotope Labeling-Assisted Evaluation of Hydrophilic and Hydrophobic Liquid Chromatograph-Mass Spectrometry for Metabolomics Profiling.

Project description:High throughput untargeted metabolomics usually relies on complementary liquid chromatography-mass spectrometry (LC-MS) methods to expand the coverage of diverse metabolites, but the integration of those methods is not fully characterized. We systematically investigated the performance of hydrophilic interaction liquid chromatography (HILIC)-MS and nanoflow reverse-phase liquid chromatography (nRPLC)-MS under 8 LC-MS settings, varying stationary phases (HILIC and C18), mobile phases (acidic and basic pH), and MS ionization modes (positive and negative). Whereas nRPLC-MS optimization was previously reported, we found in HILIC-MS (2.1 mm × 150 mm) that the optimal performance was achieved in a 90 min gradient with 100 ?L/min flow rate by loading metabolite extracts from 2 mg of cell/tissue samples. Since peak features were highly compromised by contaminants, we used stable isotope labeled yeast to enhance formula identification for comparing different LC-MS conditions. The 8 LC-MS settings enabled the detection of a total of 1050 formulas, among which 78%, 73%, and 62% formulas were recovered by the best combination of 4, 3, and 2 LC-MS settings, respectively. Moreover, these yeast samples were harvested in the presence or absence of nitrogen starvation, enabling quantitative comparisons of altered formulas and metabolite structures, followed by validation with selected synthetic metabolites. The results revealed that nitrogen starvation downregulated amino acid components but upregulated uridine-related metabolism. In summary, this study introduces a thorough evaluation of hydrophilicity and hydrophobicity based LC-MS and provides information for selecting complementary settings to balance throughput and efficiency during metabolomics experiments.

| S-EPMC6324930 | biostudies-literature

BPDA2d--a 2D global optimization-based Bayesian peptide detection algorithm for liquid chromatograph-mass spectrometry.

Project description:MotivationPeptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides.ResultsBPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together--especially by deriving information across charge states--low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy.AvailabilityThe BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/.

| S-EPMC3278754 | biostudies-literature

Determination of endocannabinoid receptor antagonist SR141716 (rimonabant) in plasma by liquid chromatograph tandem mass spectrometry.

Project description:SR141716 (rimonabant) is an endocannabinoid receptor antagonist. Endocannabinoids are a class of chemicals that affect neurotransmission via G-protein coupled CB1 (brain) and CB2 (peripheral tissue) receptors. Numerous animal studies have shown that SR141716 binds with the CB1 receptor in the brain, resulting in several biological consequences including reduced alcohol intake and reward as well as reduced food consumption. In this work, an analytical method based on liquid chromatography and electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) has been developed and validated for the quantitative measurement of SR141716 in both human and rat plasma to support the investigation of this compound. A suitable internal standard (AM251) has been chosen and the experimental conditions have been optimized for the separation and detection of singly charged positive ions of SR141716 and the internal standard. A protein precipitation protocol has been developed for extraction of SR141716 and the internal standard from plasma samples. Quantitation was achieved using multiple-reaction-monitoring (MRM) mode for SR141716 (m/z 463-->m/z 363) and the internal standard (m/z 555-->m/z 455) and calibration curve over the concentration range of 5.00-1000 ng/ml was plotted using the peak-area ratio versus the concentration of SR141716 with a LOD and LLOQ of 1.09 and 3.62 ng/ml, respectively. The method developed has been used to analyze SR141716 in rat plasma samples from an animal study.

| S-EPMC2765253 | biostudies-other

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

Project description: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.

| S-EPMC9074708 | biostudies-literature

Susceptibility to false discovery in biomarker research using liquid chromatography-high resolution mass spectrometry based untargeted metabolomics profiling.

Project description: Not available

| S-EPMC8236120 | biostudies-literature

Systems toxicology study of doxorubicin on rats using ultra performance liquid chromatography coupled with mass spectrometry based metabolomics.

Project description:A metabolomics-based systems toxicology approach was used to profile the urinary metabolites for the toxicity related processes and pathogenesis induced by doxorubicin (DOX) to rats. Endogenous metabolite profiles were obtained with ultra performance liquid chromatography-mass spectrometry (UPLC-MS) for rats receiving different single dosages of DOX (5, 10 or 20 mg/kg) prior and at three time points after dosage. Principal components analysis (PCA) allowed detection of two major systemic metabolic changes with the time due to the induced toxicity. Furthermore, Analysis of variance (ANOVA) Simultaneous Component Analysis (ASCA) was applied to reveal the variation caused by time and dose, and their interaction in a multivariate way. Finally, various metabolites involved in the toxic processes could be identified using their accurate mass and MS(n) experiments, and possible mechanisms of the toxicity of DOX were postulated. In conclusion, metabolomics as a systems toxicology approach was able to provide comprehensive information on the dynamic process of drug induced toxicity. In addition, detection of the systemic toxic effects could be obtained with metabolomics at an earlier stage compared to the clinical chemistry and histopathological assessment.

| S-EPMC2794350 | biostudies-other

Application of Holistic Liquid Chromatography-High Resolution Mass Spectrometry Based Urinary Metabolomics for Prostate Cancer Detection and Biomarker Discovery.

Project description:Human exhibit wide variations in their metabolic profiles because of differences in genetic factors, diet and lifestyle. Therefore in order to detect metabolic differences between individuals robust analytical methods are required. A protocol was produced based on the use of Liquid Chromatography- High Resolution Mass Spectrometry (LC-HRMS) in combination with orthogonal Hydrophilic Interaction (HILIC) and Reversed Phase (RP) liquid chromatography methods for the analysis of the urinary metabolome, which was then evaluated as a diagnostic tool for prostate cancer (a common but highly heterogeneous condition). The LC-HRMS method was found to be robust and exhibited excellent repeatability for retention times (<±1%), and mass accuracy (<±1 ppm). Based on normalised data (against creatinine levels, osmolality or MS total useful signals/MSTUS) coupled with supervised multivariate analysis using Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA), we were able to discriminate urine samples from men with or without prostate cancer with R2Y(cum) >0.9. In addition, using the receiver operator characteristics (ROC) test, the area under curve (AUC) for the combination of the four best characterised biomarker compounds was 0.896. The four biomarker compounds were also found to differ significantly (P<0.05) between an independent patient cohort and controls. This is the first time such a rigorous test has been applied to this type of model. If validated, the established protocol provides a robust approach with a potentially wide application to metabolite profiling of human biofluids in health and disease.

| S-EPMC3688815 | biostudies-literature

Rapid determination of heterocyclic amines in ruminant meats using accelerated solvent extraction and ultra-high performance liquid chromatograph-mass spectrometry.

Project description:Cooking techniques such as grilling confer several benefits to meat during food preparation including improved palatability, digestibility, preservation, and safety, as well as enhancing the sensory characteristics and net nutritional gain. However, grilling can lead to the formation of harmful compounds such heterocyclic amines (HCAs). HCAs are potent carcinogenic and mutagenic nitrogen containing compounds produced during certain cooking conditions of protein rich foods. Dietary intake of HCAs is associated with increased risk factors for cancers in humans. As such, there is overwhelming interest in identifying improved methods for rapid and accurate determination of heterocyclic amines in food matrices that is sensitive and avoids exhaustive sample preparation steps. Herein, we describe an approach that involves first extracting HCAs by pressurized accelerated solvent extractor using methanol as solvent, followed by addition of internal standard and quantification of HCAs by ultra-high performance liquid chromatography-high resolution accurate mass spectrometric detection (UHPLC-HRAMS). This method is fast, accurate, reproducible and does not require exhaustive sample pre-treatments prior to UHPLC-HRAMS analysis compared to existing/traditional methods for HCA analysis. •The method is automated, fast and uses tunable pressurized liquid extractor to selectively extract HCAs•Method does not require exhaustive cleanup and preconcentration steps prior to UHPLC/HRAMS analysis of HCAs•Validation showed method to be accurate, precise, and useful for routine multi-sample HCA analyses.

| S-EPMC6883301 | biostudies-literature

Data preprocessing method for liquid chromatography-mass spectrometry based metabolomics.

Project description:A set of data preprocessing algorithms for peak detection and peak list alignment are reported for analysis of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics data. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the z-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC-MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers demonstrates that the developed data preprocessing method performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS(2), for peak picking, peak list alignment, and quantification.

| S-EPMC3471364 | biostudies-literature

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