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The chemical compounds found in propolis vary according to plant sources, species, and geographical regions. To date, Indonesian propolis has not yet become standardized in terms of its chemical constituents. Thus, this study aimed to identify the presence of marker compounds and determine whether different classes of Indonesian propolis exist. In this study, yields, total polyphenol content (TPC), total flavonoid content (TFC), and antioxidants were measured. Identification of chemical compounds was carried out with Fourier-transform infrared (FTIR) spectroscopy and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Metaboanalyst 6.0 was employed in conducting principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using the results of the FTIR and LC-MS/MS. The propolis with the highest TFC, TPC, and antioxidant activity was Geniotrigona thoracica from North Sumatra. The results of propolis compound mapping based on region with discriminant analysis revealed that types of propolis from Java have similar characteristics. Then, based on species, the types of propolis from Tetragonula laeviceps and Heterotrigona itama have special characteristics; the samples from these species can be grouped according to similar characteristics. In conclusion, 10 potential marker compounds were identified in Indonesian propolis, enabling regional and species-specific varieties of Indonesian propolis to be classified based on chemical composition mapping. [ABSTRACT FROM AUTHOR]

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography–tandem mass spectrometry (GC-MS/MS), liquid chromatography–tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease. [ABSTRACT FROM AUTHOR]

Aspergillus cristatus is a crucial edible fungus used in tea fermentation. In the industrial fermentation process, the fungus experiences a low to high osmotic pressure environment. To explore the law of material metabolism changes during osmotic pressure changes, NaCl was used here to construct different osmotic pressure environments. Liquid chromatography–mass spectrometry (LC–MS) combined with multivariate analysis was performed to analyze the distribution and composition of A. cristatus under different salt concentrations. At the same time, the in vitro antioxidant activity was evaluated. The LC–MS metabolomics analysis revealed significant differences between three A. cristatus mycelium samples grown on media with and without NaCl concentrations of 8% and 18%. The contents of gibberellin A3, A124, and prostaglandin A2 related to mycelial growth and those of arabitol and fructose-1,6-diphosphate related to osmotic pressure regulation were significantly reduced at high NaCl concentrations. The biosynthesis of energy-related pantothenol and pantothenic acid and antagonism-related fluvastatin, aflatoxin, and alternariol significantly increased at high NaCl concentrations. Several antioxidant capacities of A. cristatus mycelia were directly related to osmotic pressure and exhibited a significant downward trend with an increase in environmental osmotic pressure. The aforementioned results indicate that A. cristatus adapts to changes in salt concentration by adjusting their metabolite synthesis. At the same time, a unique set of strategies was developed to cope with high salt stress, including growth restriction, osmotic pressure balance, oxidative stress response, antioxidant defense, and survival competition. [ABSTRACT FROM AUTHOR]

Berberine is a natural isoquinoline alkaloid with low toxicity, which exists in a wide variety of medicinal plants. Berberine has been demonstrated to exhibit potent prevention of indomethacin-induced gastric injury (GI) but the related mechanism remains unclear. In the present study, liquid chromatography-mass spectrometry (LC-MS)-based metabolomics was applied for the first time to investigate the alteration of serum metabolites in the protection of berberine against indomethacin-induced gastric injury in rats. Subsequently, bioinformatics was utilized to analyze the potential metabolic pathway of the anti-GI effect of berberine. The pharmacodynamic data indicated that berberine could ameliorate gastric pathological damage, inhibit the level of proinflammatory factors in serum, and increase the level of antioxidant factors in serum. The LC-MS-based metabolomics analysis conducted in this study demonstrated the presence of 57 differential metabolites in the serum of rats with induced GI caused by indomethacin, which was associated with 29 metabolic pathways. Moreover, the study revealed that berberine showed a significant impact on the differential metabolites, with 45 differential metabolites being reported between the model group and the group treated with berberine. The differential metabolites were associated with 24 metabolic pathways, and berberine administration regulated 14 of the 57 differential metabolites, affecting 14 of the 29 metabolic pathways. The primary metabolic pathways affected were glutathione metabolism and arachidonic acid metabolism. Based on the results, it can be concluded that berberine has a gastroprotective effect on the GI. This study is particularly significant since it is the first to elucidate the mechanism of berberine's action on GI. The results suggest that berberine's action may be related to energy metabolism, oxidative stress, and inflammation regulation. These findings may pave the way for the development of new therapeutic interventions for the prevention and management of NSAID-induced GI disorders. [ABSTRACT FROM AUTHOR]

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography–Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets. [ABSTRACT FROM AUTHOR]