The early growth pattern, especially the age of peak growth, of broilers affects the time to market and slaughter weight, which in turn affect the profitability of the poultry industry. However, the underlying mechanisms regulating chicken growth and development have rarely been studied. This study aimed to identify candidate genes involved in chicken growth and investigated the potential regulatory mechanisms of early growth in chicken. RNA sequencing was applied to compare the transcriptomes of chicken muscle tissues at three developmental stages during early growth. In total, 978 differentially expressed genes (DEGs) (fold change ≥ 2; false discovery rate < 0.05) were detected by pairwise comparison. Functional analysis showed that the DEGs are mainly involved in the processes of cell growth, muscle development, and cellular activities (such as junction, migration, assembly, differentiation, and proliferation). Many of the DEGs are well known to be related to chicken growth, such as MYOD1, GH, IGF2BP2, IGFBP3, SMYD1, CEBPB, FGF2, and IGFBP5. KEGG pathway analysis identified that the DEGs were significantly enriched in five pathways (P < 0.1) related to growth and development: extracellular matrix–receptor interaction, focal adhesion, tight junction, insulin signaling pathway, and regulation of the actin cytoskeleton. A total of 42 DEGs assigned to these pathways are potential candidate genes inducing the difference in growth among the three developmental stages, such as MYH10, FGF2, FGF16, FN1, CFL2, MAPK9, IRS1, PHKA1, PHKB, and PHKG1. Thus, our study identified a series of genes and several pathways that may participate in the regulation of early growth in chicken. These results should serve as an important resource revealing the molecular basis of chicken growth and development.

Circular RNA (circRNA) is a type of noncoding RNA involved in a variety of biological processes, especially in post-transcriptional regulation. The granulosa cells of follicles play a determining role in ovarian development. However, the function of circRNA in chicken follicles is unclear. To better understand the molecular mechanism underlying follicular development and granulosa cell function, we performed a strategy of second-generation sequencing and linear RNA depletion for granulosa cells from small yellow follicles (SYF, 5–8 mm), the smallest hierarchal follicles (F6, 9–12 mm), and the largest hierarchal follicles (F1, ~ 40 mm). We predicted a total of 11,642 circRNAs that distributed on almost all chromosomes. The majority of the splice lengths of circRNAs were 200–500 nt and mainly produced from intron and CDS regions. During follicle growth, differentially expressed (DE) circRNAs showed dynamic changes which were tissue- and stage-specific. The host genes of DE circRNAs were functionally enriched in GTPase activity and several pathways involved in reproduction. Moreover, bioinformatic prediction analysis for circRalGPS2 demonstrated that circRNAs from the same genes may share common miRNA to act as a sponge. The predicted target genes were enriched in various biological processes including cognition, cell communication, and regulation of signaling, and several pathways related to reproduction such as tight junction, oocyte meiosis, progesterone-mediated oocyte maturation, and GnRH signaling. This study provides a starting point for further experimental investigations into chicken circRNAs and casts a light on the understanding of follicle development.

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.

A method based on accelerated solvent extraction (ASE) coupled with gas chromatography tandem mass spectrometry (GC-MS/MS) was developed for the quantitative analysis of spectinomycin and lincomycin in poultry egg (whole egg, albumen and yolk) samples. In this work, the samples were extracted and purified using an ASE350 instrument and solid-phase extraction (SPE) cartridges, and the parameters of the ASE method were experimentally optimized. The appropriate SPE cartridges were selected, and the conditions for the derivatization reaction were optimized. After derivatization, the poultry egg (whole egg, albumen and yolk) samples were analyzed by GC-MS/MS. This study used blank poultry egg (whole egg, albumen and yolk) samples to evaluate the specificity, sensitivity, linearity, recovery and precision of the method. The linearity (5.6–2000 μg/kg for spectinomycin and 5.9–200 μg/kg for lincomycin), correlation coefficient (≥0.9991), recovery (80.0%–95.7%), precision (relative standard deviations, 1.0%–3.4%), limit of detection (2.3–4.3 μg/kg) and limit of quantification (5.6–9.5 μg/kg) of the method met the requirements for EU parameter verification. Compared with traditional liquid–liquid extraction methods, the proposed method is fast and consumes less reagents, and 24 samples can be processed at a time. Finally, the feasibility of the method was evaluated by testing real samples, and spectinomycin and lincomycin residues in poultry eggs were successfully detected.