Development and application of mass spectrometric molecular networking for analyzing the ingredients of areca nut.

Areca nut (Areca catechu L.) is commonly consumed as a chewing food in the Asian region. However, the investigations into the components of areca nut are limited. In this study, we have developed an approach that combines mass spectrometry with feature-based molecular network to explore the chemical characteristics of the areca nut. In comparison to the conventional method, this technique demonstrates a superior capability in annotating unknown compounds present in areca nut. We annotated a total of 52 compounds, including one potential previously unreported alkaloid, one carbohydrate, and one phenol and confirmed the presence of 7 of them by comparing with commercial standards. The validated method was used to evaluate chemical features of areca nut at different growth stages, annotating 25 compounds as potential biomarkers for distinguishing areca nut growth stages. Therefore, this approach offers a rapid and accurate method for the component analysis of areca nut.

Development and application of a data processing method for food metabolomics analysis.

Food metabolomics is described as the implementation of metabolomics to food systems such as food materials, food processing, and food nutrition. These applications generally create large amounts of data, and although technologies exist to analyze these data and different tools exist for various ecosystems, downstream analysis is still a challenge and the tools are not integrated into a single method. In this article, we developed a data processing method for untargeted LC-MS data in metabolomics, derived from the integration of computational MS tools from OpenMS into the workflow system Konstanz Information Miner (KNIME). This method can analyze raw MS data and produce high-quality visualization. A MS1 spectra-based identification, two MS2 spectra-based identification workflows and a GNPSExport-GNPS workflow are included in this method. Compared with conventional approaches, the results of MS1&MS2 spectra-based identification workflows are combined in this approach via the tolerance of retention times and mass to charge ratios (m/z), which can greatly reduce the rate of false positives in metabolomics datasets. In our example, filtering with the tolerance removed more than 50% of the possible identifications while retaining 90% of the correct identification. The results demonstrated that the developed method is a rapid and reliable method for food metabolomics data processing.

Lipidome reveals the alleviation of acrylamide-induced impairment by krill oil.

Krill oil is rich in polyunsaturated fatty acids and has various biological functions. Previous research studies have demonstrated that krill oil is helpful to improve locomotion via antioxidation and regulation of energy metabolism, but the alteration of relevant lipids and mechanisms still remains unclear. In this study, we observed recovery in the impairment of Caenorhabditis elegans (C. elegans) caused by acrylamide using the krill oil. Locomotion assays indicated that the supplement of krill oil enhanced head thrashes and body bends by 16.4% and 19.3% respectively in impaired C. elegans. The altered expressions of genes related to neuron status (dat-1), antioxidants (sod-3 and gst-4) and energy metabolism (daf-2 and akt-1) in impaired C. elegans were also reversed by treatment with krill oil. Lipidomics analysis suggested that krill oil could restore the metabolic changes induced by acrylamide, including the downregulation of ceramides and fatty acids and the upregulation of glycerophospholipids containing polyunsaturated fatty acids. Some of these lipids also showed significant correlations with the varied genes. In conclusion, krill oil could ameliorate the locomotion of impaired C. elegans via suppressing the oxidative stress and promoting energy supply. Our findings facilitate the functional studies of krill oil.

Development and Application of Feature-Based Molecular Networking for Phospholipidomics Analysis.

Phospholipids are small but critical lipids in milk. Conventional lipidomics is a powerful method for the analysis of lipids in milk. Although the number of lipidomics software has drastically increased over the past five years, reducing false positives and obtaining structurally accurate annotations of phospholipids remain a significant challenge. In this study, we developed a rapid and accurate method for measuring a wide spectrum of phospholipids in milk. The developed approach that employed information-dependent acquisition (IDA) mode and feature-based molecular networking has exhibited better performance on data processing and lipid annotation when compared with sequential window acquisition of all theoretical mass spectra (SWATH) and MS-DIAL. This validated method was further evaluated using three kinds of sheep milk. A total of 150 phospholipids were identified, including rarely reported phospholipids containing ethers or vinyl ethers. The result indicated that phospholipids could be used as potential markers to distinguish sheep milk from different varieties and origins. The experimental and computational methods provide a rapid and reliable method for the investigation of phospholipids in milk.