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.

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.

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.

Long noncoding RNAs (lncRNAs) regulate adipogenesis and other processes associated with metabolic tissue development and function. However, little is known about the function and profile of lncRNAs during preadipocyte differentiation in the chicken (Gallus gallus). Herein, lncRNA and mRNA expression in preadipocytes at different stages of differentiation were analyzed using RNA sequencing. A total of 1,300,074,528 clean reads and 27,023 novel lncRNAs were obtained from 12 samples. The number of genes (1336 lncRNAs and 1759 mRNAs; 3095 in total) differentially expressed across various stages declined as differentiation progressed. Differentially expressed genes were found to be involved in several pathways related to preadipocyte differentiation that have been extensively studied, including glycerolipid metabolism, and the mammalian target of rapamycin, peroxisome proliferator-activated receptor, and mitogen-activated protein kinase signaling pathways. To our knowledge, some pathways are being reported for the first time, including the propanoate metabolism, fatty acid metabolism, and oxidative phosphorylation pathways. Furthermore, 3095 differentially expressed genes were clustered into eight clusters, and their expression patterns were determined through K-means clustering. Genes involved in the K2 cluster likely play important roles in preadipocyte differentiation. Six stage-specific modules related to A0 (day 0), A2 (day 2), and A6 (day 6) stages were identified, using weighted coexpression network analysis. Nine central, highly connected .genes in stage-specific modules were subsequently identified, including XLOC_068731, XLOC_022661, XLOC_045161, XLOC_070302, CHD6, LLGL1, NEURL1B, KLHL38, and ACTR6. This study provides a valuable resource for further study of chicken lncRNA and facilitates a better understanding of preadipocyte differentiation in the chicken