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The accuracy of sixteen commonly used internal reference genes was assessed in skeletal muscle-derived satellite cells of Qinchuan cattle at different stages of proliferation and induction of differentiation to determine the most suitable ones. Quantitative real-time PCR and three commonly used algorithmic programs, GeNorm, NormFinder and BestKeeper, were used to evaluate the stability of expression of the candidate internal reference genes (GAPDH, ACTB, PPIA, LRP10, HPRT1, YWHAZ, B2M, TBP, EIF3K , RPS9, UXT, 18S rRNA, RPLP0, MARVELD, EMD and RPS15A) in skeletal muscle-derived satellite cells at 0, 12, 24, 36 and 48 h of growth and after differentiation for 0, 2, 4, 6 and 8 days. The expression of two satellite cell marker genes, CCNA2 and MYF5, was used for validation analysis. The results of the software analyses showed that GAPDH and RPS15A were the most stable reference gene combinations during in vitro proliferation of bovine skeletal muscle-derived satellite cells, RPS15A and RPS9 were the most stable reference gene combinations during in vitro induction of differentiation of the cells, and PPIA was the least stable reference gene during proliferation and differentiation and was not recommended. This study lays the foundation for the selection of reference genes for qRT-PCR during the proliferation and induction of differentiation of bovine skeletal muscle-derived satellite cells. [ABSTRACT FROM AUTHOR]

SIX1 mRNA expression was normalized to the housekeeping gene GAPDH and the expression levels were calculated relative to the gene expression in the liver and 24 months, respectively. Graph: Figure 1 Expression pattern analysis of the bovine SIX1. [Extracted from the article]

E. coli is the main causative agent of mastitis in dairy cows, but the mechanism of molecular regulation underlying the occurrence and development of mastitis has not yet been fully elucidated. In this study, an E. coli-induced mastitis model was created and RNASeq technology was used to measure the miRNA expression profiles at different times post-infection (0, 1, 3, 5, 7 dpi), as well as to screen for differentially expressed miRNA. The results show detection of 2416 miRNAs, including 628 known miRNAs and 1788 newly discovered miRNAs. A total of 200 differentially expressed miRNAs were found at different time points. Bioinformatics analysis showed that these differentially expressed miRNAs may regulate the occurrence and development of mastitis in dairy cows through seven signal transduction pathways, namely cytokine-cytokine receptor interaction, MAPK signaling pathway, chemokine signaling pathway, leukocyte transendothelial migration, T cell receptor signaling pathway, Toll-like receptor signaling pathway, and cell adhesion molecules. In addition, bta-miR-200a, bta-miR-205, bta-miR-122, bta-miR-182 and the newly discovered conservative_15_7229 might be involved in immune process in late stage of E. coli-induced mastitis. The results of this study lay the foundation for molecular network analysis of mastitis and molecular breeding of dairy cows. [ABSTRACT FROM AUTHOR]

Adipogenesis is the process of cell differentiation through which preadipocytes become adipocytes. Lots of research is currently ongoing to identify genes, including their gene products and microRNAs, that correlate with fat cell development. However, information fragmentation hampers the identification of key regulatory genes and pathways. Here, we present a database of literature-curated adipogenesis-related regulatory interactions, designated the Adipogenesis Regulation Network (ARN, http://210.27.80.93/arn/), which currently contains 3101 nodes (genes and microRNAs), 1863 regulatory interactions, and 33,969 expression records associated with adipogenesis, based on 1619 papers. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 37,000 PubMed abstracts. Additionally, we further determined 13,103 possible node relationships by searching miRGate, BioGRID, PAZAR and TRRUST. ARN also has several useful features: i) regulatory map information; ii) tests to examine the impact of a query node on adipogenesis; iii) tests for the interactions and modes of a query node; iv) prediction of interactions of a query node; and v) analysis of experimental data or the construction of hypotheses related to adipogenesis. In summary, ARN can store, retrieve and analyze adipogenesis-related information as well as support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways. [ABSTRACT FROM AUTHOR]