Regulatory effect of Yinchenhao decoction on bile acid metabolism to improve the inflammatory microenvironment of hepatocellular carcinoma in mice.

Hepatocellular carcinoma (HCC) is a malignant tumor with extremely high mortality. The tumor microenvironment is the "soil" of its occurrence and development, and the inflammatory microenvironment is an important part of the "soil". Bile acid is closely related to the occurrence of HCC. Bile acid metabolism disorder is not only directly involved in the occurrence and development of HCC but also affects the inflammatory microenvironment of HCC. Yinchenhao decoction, a traditional Chinese medicine formula, can regulate bile acid metabolism and may affect the inflammatory microenvironment of HCC. To determine the effect of Yinchenhao decoction on bile acid metabolism in mice with HCC and to explore the possible mechanism by which Yinchenhao decoction improves the inflammatory microenvironment of HCC by regulating bile acid metabolism, we established mice model of orthotopic transplantation of hepatocellular carcinoma. These mice were treated with three doses of Yinchenhao decoction, then liver samples were collected and tested. Yinchenhao decoction can regulate the disorder of bile acid metabolism in liver cancer mice. Besides, it can improve inflammatory reactions, reduce hepatocyte degeneration and necrosis, and even reduce liver weight and the liver index. Taurochenodeoxycholic acid, hyodeoxycholic acid, and taurohyodeoxycholic acid are important molecules in the regulation of the liver inflammatory microenvironment, laying a foundation for the regulation of the liver tumor inflammatory microenvironment based on bile acids. Yinchenhao decoction may improve the inflammatory microenvironment of mice with HCC by ameliorating hepatic bile acid metabolism.

Characteristics of Shisa Family Genes in Zebrafish.

Shisa represents a type of single-transmembrane adaptor protein containing an N-terminal cysteine-rich domain and a proline-rich C-terminal region. Nine shisa subfamily genes have been proposed in most vertebrates; however, some might be species-specific. The number of shisa genes present in zebrafish remains unclear. This study aimed to investigate the evolutionary relationships among shisa family genes in zebrafish (TU strain) using phylogenetic and syntenic analyses. The function of shisa-2 was preliminarily examined via CRISPR/Cas13d-mediated knockdown. Following identification in zebrafish, 10 shisa family genes, namely shisa-1, 2, 3, 4, 5, 6, 7, 8, 9a, and 9b, were classified into three main clades and six subclades. Their encoding proteins contained a cysteine-rich N-terminal domain and a proline-rich C-terminal region containing different motifs. A specific syntenic block containing atp8a2 and shisa-2 was observed to be conserved across all species. Furthermore, all these genes were expressed during embryogenesis. Shisa-2 was expressed in the presomitic mesoderm, somites, and so on. Shisa-2 was identified as a regulator of the expression of the somite formation marker mesp-ab. Overall, our study provides new insights into the evolution of shisa family genes and the control of shisa-2 over the convergent extension cells of somitic precursors in zebrafish.

Characterization, evolution and expression analysis of Toll-like receptor 7 (TLR7) in turbot (Scophthalmus maximus L.).

The pattern recognition receptors (PRRs) can recognize the conserved molecular structures of pathogens to active the innate immune responses, and subsequently induce the antigen-specific adaptive immune responses for the clearance of infected pathogen. Among the PRRs, Toll-like receptors (TLRs) are the first and best characterized PRRs across all the species. Among the TLR members, TLR7 showed significant conservation across the vertebrates, with the lowest rate of evolution for its LRR domains from primates to fishes. In the current study, one TLR7 (SmTLR7) gene was captured in turbot, with a 3144 bp open reading frame (ORF), that encoding 1047 amino acid residues. Following multiple sequence comparison, SmTLR7 was found to have the highest similarity and identity both to Paralichthys olivaceus with 91.9% and 85.9%, respectively. In phylogenetic analysis, SmTLR7 was firstly clustered with Japanese flounder, and then clustered with fugu, rainbow trout, and zebrafish. In addition, SmTLR7 was widely expressed in all the examined tissues with the highest expression level in spleen, followed by skin, while the lowest expression level was detected in blood. Following both Edwardsiella tarda and Vibrio anguillarum challenge, SmTLR7 was significantly down-regulated in gill and intestine, and up-regulated in skin. Moreover, SmTLR7 was significantly up-regulated in head kidney macrophages following LPS, LTA, PGN and polyI:C stimulation, as well as showed the strongest binding ability to LPS, followed by PGN, LTA, and polyI:C in a dose-dependent manner. Finally, following RNAi of SmTLR7, MyD88 and IL-1ß were slightly up-regulated, while TRAF6 and IL-8 were significantly down-regulated. The characterization of TLR7 can expand our understanding of the PRRs in teleost fishes, and eventually aid the exploration of interactions between host and pathogen.

Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations.

MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of miRNA (mir-15) may affect the expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we aim to predict multi-type miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triples as a tensor and introduce tensor decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to $38\%$ in Top-1F1). We then propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient.

Predicting drug-disease associations through layer attention graph convolutional network.

BACKGROUND: Determining drug-disease associations is an integral part in the process of drug development. However, the identification of drug-disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug-disease associations is of great significance. RESULTS: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug-disease association prediction. Specifically, LAGCN first integrates the known drug-disease associations, drug-drug similarities and disease-disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug-disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision-recall curve of 0.3168 and an area under the receiver-operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset. CONCLUSION: LAGCN is a useful tool for predicting drug-disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.

Characterization of a novel lncRNA (SETD3-OT) in turbot (Scophthalmus maximus L.).

LncRNAs have been demonstrated to play pivotal roles in virous biological processes, especially the gene expression regulation, including transcriptional regulation, posttranscriptional control and epigenetic processes. However, most of the current studies of lncRNAs are still limited in mammalian species, the investigations of functional roles of lncRNAs in teleost species are still lacking. In current study, we identified a novel lncRNA (SETD3-OT) in turbot, with 2,504 bp full-length obtained by 5' and 3' RACE, located in turbot chromosome 17, ranged from 20,933,835 to 20,936,302 bp. In addition, 8 neighboring genes of SETD3-OT were identified within 100 kbp in genome location. From the annotation of the neighboring adjacent genes, SETD3-OT might involve in regulation of cell apoptosis and cycle, the immune cell development, and the immune response against infection, and its expression pattern is similar to majority of the neighboring genes following Aeromonas salmonicida challenge. Intriguingly, SETD3-OT showed significant high expression levels in mucosal surfaces (intestine, gill and skin), and was dramatically down-regulated in these mucosal tissues following Vibrio anguillarum challenge, especially in gill and skin. In addition, SETD3-OT was distributed in nucleus, it might regulate the neighboring genes in cis or in trans. Taken together, our results provide insights for lncRNA in fish innate immunity, further studies should be conduct to explore the detailed molecular mechanism of the gene regulation between SETD3-OT and its neighboring genes.