In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

Emerging evidence indicates that long noncoding RNAs (lncRNAs) play important roles in the regulation of cell differentiation by acting as competing endogenous RNA (ceRNA). However, the regulatory mechanisms of lncRNA and the lncRNA-associated ceRNA network involved in adipogenic differentiation of chicken preadipocytes remain elusive. Here, we first constructed the chicken preadipocyte in vitro induction model. Then, we identified differentially expressed lncRNAs (DELs), miRNAs (DEMis), and mRNAs (DEMs) between differentiated and undifferentiated preadipocytes. Furthermore, we constructed the lncRNA associated ceRNA network by gene expression correlation analysis and target prediction of DELs, DEMis, and DEMs. Finally, we determined twelve candidate lncRNA-miRNA-mRNA interactions from the lncRNA associated ceRNA network. Eight out of the twelve interactions were validated by RT-qPCR, indicating their potential role in the regulation of chicken preadipocytes differentiation. Among the eight interactions, TCONS_00026544-gga-miR-128-1-5p-RASD1, TCONS_00055280-gga-miR-135a-5p-JAM3, TCONS_00055280-gga-miR-135a-5p-GPR133, TCONS_00055280-gga-miR-135a-5p-CLDN1, and TCONS_00055280-gga-miR-135a-5p-TMEM123 may promote adipogenic differentiation of chicken preadipocytes while TCONS_00057272-gga-miR-146a-3p-FOXO6, TCONS_00057242-gga-miR-6615-3p-FOXO6, and TCONS_00057242-gga-miR-6615-3p-ENSGALT00000043224 have the opposite effects. Our results not only provide novel insights into ceRNA roles of lncRNAs in chicken preadipocytes differentiation and but also contribute to a better understanding of chicken fat deposition.