Poly(I:C) exposure during in vitro fertilization disrupts first cleavage of mouse embryos and subsequent blastocyst development.

The reproductive system can be infected by a variety of double-stranded RNA viruses, which disrupt ovary function and pregnancy. However, whether viral infection directly affects early embryonic development remains unknown. Here we show that Poly(I:C), which mimics a double-stranded RNA virus, significantly impaired mouse early embryonic development in vitro, and up-regulated TLR3 and IFNα at the two cells embryo stage. Further studies indicated that Poly(I:C)-treatment caused DNA damage and abnormal spindle morphology at the first cleavage. Moreover, CDX2 and SOX2 expression was decreased while blastocyst cell apoptosis was increased. Altogether, Poly(I:C) decreased the rate of successful in vitro fertilization via DNA damage and abnormal spindle morphology at the first cleavage and inhibited early embryonic development by inducing immune response and promoting blastocyst cell apoptosis. This study provides an implication for exploring the causes of reproductive disorders in mammals and humans caused by infection of double-stranded RNA virus.

Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model.

Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.