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.

The T cell-mediated immune response plays an important role in coccidiosis. To reveal the host T cell immune response following Eimeria tenella (E. tenella) infection in chickens, we performed quantitative real-time PCR to analyze the dynamic expression of the Th1-related cytokines IFN-γ, IL-2, and IL-12; the Th17-related cytokines IL-17A, IL-17F, and IL-22; and the Treg-related cytokines IL-10, TGF-β, and CTLA-4 in the cecum and spleen at 0, 2, 4, 6, 8, and 10 d postinfection (dpi). In the cecal tissue, the expression of the Th1-related cytokine IFN-γ was significantly higher at 6 and 8 dpi than at other time points (11.97-fold and 39.78-fold, respectively, compared with 0 dpi, P < 0.05). IL-2 and IL-12 expression was significantly higher at 6 and 8 dpi than at 0, 2 and 10 dpi (P < 0.05). The expression of the Th17-related cytokines IL-17A and IL-17F at 2 and 4 dpi and IL-22 expression at 4 dpi were significantly higher than those at 0, 6, 8 and 10 dpi (P < 0.05). The expression of the Treg-related cytokines IL-10, TGF-β and CTLA-4 was significantly higher at 6 and 8 dpi than at 0, 2 and 4 dpi (P < 0.05). In the spleen, IFN-γ and IL-12 expression peaked at 4 dpi, while IL-2 expression peaked at 10 dpi. IL-17A, IL-17F and IL-22 expression was significantly higher at 2 and 4 dpi than at 0, 6, 8 and 10 dpi (P < 0.05). Treg-related cytokine TGF-β expression was almost unchanged and significantly decreased at only 4 dpi (P < 0.05), while CTLA-4 expression showed an overall decreasing trend from 0 to 8 dpi but increased significantly at 10 dpi (P < 0.05). The expression patterns of three T cell subset-related cytokines were different in the cecum and spleen. Furthermore, Th1 and Treg cells participate in the immune response mainly in the latter stage of coccidia infection (6 and 8 dpi), while Th17 cells play a role mainly in the early stages of infection (2 and 4 dpi). Our data will help to deepen the understanding of the complex T cell immune response after coccidia infection.

Background: Coccidiosis is an intestinal parasitic disease caused by Eimeria protozoa, which endangers the health and growth of animals, and causes huge economic losses to the poultry industry worldwide every year. Studies have shown that poultry gut microbiota plays an important role in preventing the colonization of pathogens and maintaining the health of the host. Coccidia infection also affects host gene expression. However, the underlying potential relationship between gut microbiome and host transcriptome during E. tenella infection in chickens remain unclear.Methods: In this study, metagenomic and transcriptome sequencing were applied to identify microbiota and genes in cecal contents and cecal tissues of infected (JS) and control (JC) chickens on day 4.5 postinfection (pi), respectively.Results: First, microbial sequencing results of cecal contents showed that the abundance of Lactobacillus, Roseburia sp. and Faecalibacterium sp decreased significantly after E. tenella infection (P < 0.05), while the abundance of Alistipes and Prevotella pectinovora increased significantly (P < 0.05). Second, transcriptome sequencing results showed that a total of 434 differentially expressed mRNAs were identified, including 196 up-regulated and 238 down-regulated genes. These differentially expressed genes related to inflammation and immunity, such as GAMA, FABP1, F2RL1 and RSAD2, may play an important role in the process of host resistance to coccidia infection. Functional studies showed that the enriched pathways of differentially expressed genes included the TGF-beta signaling pathway and the ErbB signaling pathways. Finally, the integrated analysis of gut microbiome and host transcriptome suggested that Prevotella pectinovora associated with FABP1, Butyricicoccus porcorum and Colidextribacter sp. associated with RSAD2 were involved in the immune response upon E. tenella infection.Conclusion: In conclusion, this study provides valuable information on the microbiota and key immune genes after chicken E. tenella infection, with the aim of providing reference for the impact of coccidia infection on cecal microbiome and host.

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.