Integrated proteomics and metabolomics analysis revealed the mechanisms underlying the effect of irradiation on the fat quality of Chinese bacon

MS based foodomics: An edge tool integrated metabolomics and proteomics for food science

Foodomics has become a popular methodology in food science studies. Mass spectrometry (MS) based metabolomics and proteomics analysis played indispensable roles in foodomics research. So far, several methodologies have been developed to detect the metabolites and proteins in diets and consumers, including sample preparation, MS data acquisition, annotation and interpretation. Moreover, multiomics analysis integrated metabolomics and proteomics have received considerable attentions in the field of food safety and nutrition, because of more comprehensive and deeply. In this context, we intended to review the emerging strategies and their applications in MS-based foodomics, as well as future challenges and trends. The principle and application of multiomics were also discussed, such as the optimization of data acquisition, development of analysis algorithm and exploration of systems biology.

Exploring the formation of a transparent fat portion in bacon after heating based on physicochemical characteristics and microstructure

Bacons, which possess a transparent fat tissue after heating, have high commercial value in China owing to their good sensory quality. This study was performed to explore the formation of transparent fat tissue by comparing the physicochemical characteristics and microstructures of transparent and non-transparent fat tissues. The physicochemical characteristics and microstructure of fat tissue were found to be significantly affected by drying, which increased the saturated fatty acid content and oxidation level, and decreased the moisture content and water activity (p < 0.05). Shrivelled adipocytes were observed in fat tissue after drying. Transparent and non-transparent fat tissues differed significantly in terms of moisture, fat content, texture, and fatty acid composition (p < 0.05). Multivariate statistical analysis indicated that low moisture content might be the major factor in the formation of transparent tissue, while the destruction of adipocytes also contributed to such formation.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.