a b s t r a c tWe developed an adenovirus-based CRISPR/Cas9 system for gene editing in vivo. In the liver, we demonstrated that the system could reach the level of tissue-specific gene knockout, resulting in phenotypic changes. Given the wide spectrum of cell types susceptible to adenoviral infection, and the fact that adenoviral genome rarely integrates into its host cell genome, we believe the adenovirus-based CRISPR/Cas9 system will find applications in a variety of experimental settings.

The move from reading to writing the human genome offers new opportunities to improve human health. The United States National Institutes of Health (NIH) Somatic Cell Genome Editing (SCGE) Consortium aims to accelerate the development of safer and more-effective methods to edit the genomes of disease-relevant somatic cells in patients, even in tissues that are difficult to reach. Here we discuss the consortium’s plans to develop and benchmark approaches to induce and measure genome modifications, and to define downstream functional consequences of genome editing within human cells. Central to this effort is a rigorous and innovative approach that requires validation of the technology through third-party testing in small and large animals. New genome editors, delivery technologies and methods for tracking edited cells in vivo, as well as newly developed animal models and human biological systems, will be assembled—along with validated datasets—into an SCGE Toolkit, which will be disseminated widely to the biomedical research community. We visualize this toolkit—and the knowledge generated by its applications—as a means to accelerate the clinical development of new therapies for a wide range of conditions.

SummaryAlphaviruses are a group of icosahedral, positive-strand RNA, enveloped viruses. The membrane bi-layer, which surrounds the ~ 400 Å diameter nucleocapsid, is penetrated by 80 spikes arranged in a T = 4 lattice. Each spike is a trimer of heterodimers consisting of glycoproteins E1 and E2. Cryoelectron microscopy and image reconstruction of Ross River virus showed that the T = 4 quaternary structure of the nucleocapsid consists of pentamer and hexamer clusters of the capsid protein, but not dimers, as have been observed in several crystallographic studies. The El-E2 heterodimers form one-to-one associations with the nucleocapsid monomers across the lipid bilayer. Knowledge of the atomic structure of the capsid protein and our reconstruction allows us to identify capsid-protein residues that interact with the RNA, the glycoproteins, and adjacent capsid-proteins.

Background Intestinal inflammation is prevalent in chicken, which results in decreased growth performance and considerable economic losses. Accumulated findings established the close relationship between gut microbiota and chicken growth performance. However, whether gut microbiota impacts chicken growth performance by lessening intestinal inflammation remains elusive. Results Seven-weeks-old male and female chickens with the highest or lowest body weights were significantly different in breast and leg muscle indices and average cross-sectional area of muscle cells. 16S rRNA gene sequencing indicated Gram-positive bacteria, such as Lactobacilli, were the predominant species in high body weight chickens. Conversely, Gram-negative bacteria, such as Comamonas, Acinetobacter, Brucella, Escherichia-Shigella, Thermus, Undibacterium, and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium were significantly abundant in low body weight chickens. Serum lipopolysaccharide (LPS) level was significantly higher in low body weight chickens (101.58 ± 5.78 ng/mL) compared with high body weight chickens (85.12 ± 4.79 ng/mL). The expression of TLR4, NF-κB, MyD88, and related inflammatory cytokines in the jejunum was significantly upregulated in low body weight chickens, which led to the damage of gut barrier integrity. Furthermore, transferring fecal microbiota from adult chickens with high body weight into 1-day-old chicks reshaped the jejunal microbiota, mitigated inflammatory response, and improved chicken growth performance. Conclusions Our findings suggested that jejunal microbiota could affect chicken growth performance by mitigating intestinal inflammation.

It has been established that gut microbiota influences chicken growth performance and fat metabolism. However, whether gut microbiota affects chicken growth performance by regulating fat metabolism remains unclear. Therefore, seven-week-old chickens with high or low body weight were used in the present study. There were significant differences in body weight, breast and leg muscle indices, and cross-sectional area of muscle cells, suggesting different growth performance. The relative abundance of gut microbiota in the caecal contents at the genus level was compared by 16S rRNA gene sequencing. The results of LEfSe indicated that high body weight chickens contained Microbacterium and Sphingomonas more abundantly (P < 0.05). In contrast, low body weight chickens contained Slackia more abundantly (P < 0.05). The results of H & E, qPCR, IHC, WB and blood analysis suggested significantly different fat metabolism level in serum, liver, abdominal adipose, breast and leg muscles between high and low body weight chickens. Spearman correlation analysis revealed that fat metabolism positively correlated with the relative abundance of Microbacterium and Sphingomonas while negatively correlated with the abundance of Slackia. Furthermore, faecal microbiota transplantation was performed, which verified that transferring faecal microbiota from adult chickens with high body weight into oneday-old chickens improved growth performance and fat metabolism in liver by remodelling the gut microbiota. Overall, these results suggested that gut microbiota could affect chicken growth performance by regulating fat metabolism.