Identification of the key mechanisms of action of Si-Ni-San in uveitis using bioinformatics and network pharmacology.

BACKGROUND: Uveitis is an eye disease with a high rate of blindness, whose pathogenesis is not completely understood. Si-Ni-San (SNS) has been used as a traditional medicine to treat uveitis in China. However, its mechanism of action remains unclear. This study explored the potential mechanisms of SNS in the treatment of uveitis through network pharmacology and bioinformatics. METHODS: Using R language and Perl software, the active components and predicted targets of SNS, as well as the related gene targets of uveitis, were mined through the Traditional Chinese Medicine Systems Pharmacology, Therapeutic Target, Gene Expression Omnibus, GeneCards, and DrugBank databases. The network diagram of active components and intersection targets was constructed using Cytoscape software and the String database. The CytoNCA plug-in was used to conduct topological analysis on the network diagram and screen out the core compounds and key targets. The genes were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. Chemoffice, Pymol, AutoDock, and Vina were used to analyze the molecular docking of key targets and core compounds of diseases through the PubChem database. RESULTS: JUN, RELA, and MAPK may play important roles in the treatment of uveitis by SNS. Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that core genes were mainly concentrated in MAPK, toll-like receptor, tumor necrosis factor, and nucleotide oligomerization domain-like receptor signaling pathways. In addition, molecular docking results showed that the bioactive compounds (kaempferol, luteolin, naringin, and quercetin) exhibited good binding ability to JUN, RELA, and MAPK. CONCLUSION: Based on these findings, SNS exhibits multi-component and multi-target synergistic action in the treatment of uveitis, and its mechanism may be related to anti-inflammatory and immune regulation.

Genetic association analyses and meta-analysis of Dynorphin-Kappa Opioid system potential functional variants with heroin dependence.

Prodynorphin (PDYN) binds to k-opioid receptors (KOPr; encoded by OPRK1) and is known to regulate dopaminergic tone, making this system important for drugs addiction. Dynorphin (Dyn)/KORr system are powerful effectors of stress-induced alterations in reward processing and dysphoric states. Thus, We identified 11 potential functional SNPs and one variable number of tandem repeat (VNTR) in this system, performed a case-control association analysis, investigated particular disease phenotypes, assessed the joint effect of variants in two genes, carried out a meta-analysis to analyze the association between this VNTR and Heroin dependence (HD) risk. Eleven single-nucleotide polymorphisms (SNPs) were genotyped using SNaPshot SNP technology. Participants included 566 healthy controls and 541 patients with HD. We found that PDYN polymorphisms modulate the susceptibility to HD. An increased risk of HD was significantly associated with H alleles of PDYN VNTR (χ2 = 10.824, p = 0.001, OR = 1.419, 95% CI = 1.151-1.748). In addition, the results revealed the patients with the HH genotype showed greater number of withdrawal instances (F(2538) = 7.987, p = 0.0004) compared to the patients with the LL genotype. The Meta-analysis showed the pooled effect of the H allele at this locus is a risk factor for HD in Chinese Han. Gene-gene interaction analysis indicated strong interactions between PDYN rs3830064, 68-bp VNTR and OPRK1 rs16918842, rs3802279. These findings support the important role of PDYN polymorphism in HD, and may guide future studies to identify genetic risk factors for HD.

The comprehensive liver transcriptome of two cattle breeds with different intramuscular fat content.

Intramuscular fat (IMF) content is an important determinant factor of meat quality in cattle. There is significant difference in IMF content between Jinnan and Simmental cattle. Here, to identify candidate genes and networks associated with IMF deposition, we deeply explored the transcriptome architecture of liver in these two cattle breeds. We sequenced the liver transcriptome of five Jinnan and three Simmental cattle, yielding about 413.9 million sequencing reads. 124 differentially expressed genes (DEGs) were detected, of which 53 were up-regulated and 71 were down-regulated in Jinnan cattle. 1282 potentially novel genes were also identified. Gene ontology analysis revealed these DEGs (including CYP21A2, PC, ACACB, APOA1, and FADS2) were significantly enriched in lipid biosynthetic process, regulation of cholesterol esterification, reverse cholesterol transport, and regulation of lipoprotein lipase activity. Genes involved in pyruvate metabolism pathway were also significantly overrepresented. Moreover, we identified an interaction network which related to lipid metabolism, which might be contributed to the IMF deposition in cattle. We concluded that the DEGs involved in the regulation of lipid metabolism could play an important role in IMF deposition. Overall, we proposed a new panel of candidate genes and interaction networks that can be associated with IMF deposition and used as biomarkers in cattle breeding.

The research progress of genomic selection in livestock.

With the development of gene chip and breeding technology, genomic selection in plants and animals has become research hotspots in recent years. Genomic selection has been extensively applied to all kinds of economic livestock, due to its high accuracy, short generation intervals and low breeding costs. In this review, we summarize genotyping technology and the methods for genomic breeding value estimation, the latter including the least square method, RR-BLUP, GBLUP, ssGBLUP, BayesA and BayesB. We also cover basic principles of genomic selection and compare their genetic marker ranges, genomic selection accuracy and operational speed. In addition, we list common indicators, methods and influencing factors that are related to genomic selection accuracy. Lastly, we discuss latest applications and the current problems of genomic selection at home and abroad. Importantly, we envision future status of genomic selection research, including multi-trait and multi-population genomic selection, as well as impact of whole genome sequencing and dominant effects on genomic selection. This review will provide some venues for other breeders to further understand genome selection.