ALBERT

Description: Intramuscular fat (IMF) is the most important evaluating indicator of chicken meat quality, the content of which is positively correlated with tenderness, flavor, and succulence of the meat. Chicken IMF deposition process is regulated by many factors, including genetic, nutrition, and environment. Although large number of omics’ studies focused on the IMF deposition process, the molecular mechanism of chicken IMF deposition is still poorly understood. In order to study the role of miRNAs in chicken intramuscular adipogenesis, the intramuscular adipocyte differentiation model (IMF-preadipocytes and IMF-adipocytes) was established and subject to miRNA-Seq. A total of 117 differentially expressed miRNAs between two groups were obtained. Target genes prediction and functional enrichment analysis revealed that eight pathways involved in lipid metabolism related processes, such as fatty acid metabolism and fatty acid elongation. Meanwhile a putative miRNA, gga-miR-18b-3p, was identified be served a function in the intramuscular adipocyte differentiation. Luciferase assay suggested that the gga-miR-18b-3p targeted to the 3′UTR of ACOT13. Subsequent functional experiments demonstrated that gga-miR-18b-3p acted as an inhibitor of intramuscular adipocyte differentiation by targeting ACOT13. Our findings laid a new theoretical foundation for the study of lipid metabolism, and also provided a potential target to improve the meat quality in the poultry industry.

Description: Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.