Separation of Chromatographic Co-Eluted Compounds by Clustering and by Functional Data Analysis

Identification of Xanthine Oxidase Inhibitors from Celery Seeds Using Affinity Ultrafiltration-Liquid Chromatography-Mass Spectrometry

Celery seeds have been used as an effective dietary supplement to manage hyperuricemia and diminish gout recurrence. Xanthine oxidase (XOD), the critical enzyme responsible for uric acid production, represents the most promising target for anti-hyperuricemia in clinical practice. In this study, we aimed to establish a method based on affinity ultrafiltration-liquid chromatography-mass spectrometry (UF-LC-MS) to directly and rapidly identify the bioactive compounds contributing to the XOD-inhibitory effects of celery seed crude extracts. Chemical profiling of celery seed extracts was performed using UPLC-TOF/MS. The structure was elucidated by matching the multistage fragment ion data to the database and publications of high-resolution natural product mass spectrometry. Thirty-two compounds, including fourteen flavonoids and six phenylpeptides, were identified from celery seed extracts. UF-LC-MS showed that luteolin-7-O-apinosyl glucoside, luteolin-7-O-glucoside, luteolin-7-O-malonyl apinoside, luteolin-7-O-6'-malonyl glucoside, luteolin, apigenin, and chrysoeriol were potential binding compounds of XOD. A further enzyme activity assay demonstrated that celery seed extract (IC50 = 1.98 mg/mL), luteolin-7-O-apinosyl glucoside (IC50 = 3140.51 μmol/L), luteolin-7-O-glucoside (IC50 = 975.83 μmol/L), luteolin-7-O-6'-malonyl glucoside (IC50 = 2018.37 μmol/L), luteolin (IC50 = 69.23 μmol/L), apigenin (IC50 = 92.56 μmol/L), and chrysoeriol (IC50 = 40.52 μmol/L) could dose-dependently inhibit XOD activities. This study highlighted UF-LC-MS as a useful platform for screening novel XOD inhibitors and revealed the chemical basis of celery seed as an anti-gout dietary supplement.

Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data

Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features. In such cases, for statistical analyzing, standard methods cannot be applied or lead to ill-conditioned estimators of the covariance matrix. To analyze the data, we need an estimator of the covariance matrix with good properties (e.g., positive definiteness), and therefore covariance matrix identification is crucial. The paper presents an approach to determine the block-structured estimator of the covariance matrix based on an example of metabolomic data on the drought resistance of barley. This method can be used in many fields of science, e.g., in agriculture, medicine, food and nutritional sciences, toxicology, functional genomics and nutrigenomics.