Contents of Volume 53

Plant Molecular Biology -     

Maternal Transfer and Protective Role of the Alternative Complement Components in Zebrafish Danio rerio

Embryos of most fish develop externally and are exposed to an aquatic environment full of potential pathogens, whereas they have little or only limited ability to mount an efficient and protective response. How fish embryos survive pathogenic attacks remains poorly defined. Here we demonstrate that the maternal immunization of female zebrafish with formalin-killed Aeromonas hydrophila causes a significant increase in C3 and Bf contents in the mother, a corresponding rise in the offspring, and induces a remarkable increase in the hemolytic activities in both the mother and offspring. In addition, the embryos derived from the immunized mother are significantly more tolerant to A. hydrophila challenge than those from the unimmunized fish, and blocking C3 and Bf activities by injection of the antibodies against C3 and Bf into the embryos render them more susceptible to A. hydrophila. These results clearly show that the protection of zebrafish embryos against A. hydrophila can be achieved by the maternally-transferred immunity of the complement system operating via the alternative pathway. This appears to be the first report providing in vivo evidences for the protective role of the alternative complement components in the early embryos of zebrafish, paving the way for insights into the in vivo function of other maternally-transferred factors in fish.     

基因定点诱变删除及天然α型心钠素的分泌型表达

用基因定点诱变技术,删除了pO_1α ANF表达质粒中的33对碱基,使人α型心钠素结构基因直接与大肠杆菌分泌型表达质粒pIN-Ⅲ-OmPA中的信号肽酶切位点编码区相连,构成天然人α型心钠素的表达质粒pANF,在IPTG诱导下表达28肽的天然人α型心钠素。纯化后的表达产物具有天然心钠素的放免活性和很强的舒张血管的生物活性。     

Chromosome-level genome assembly and characterization of Sophora Japonica

Sophora japonica is a medium-size deciduous tree belonging to Leguminosae family and famous for its high ecological, economic and medicinal value. Here, we reveal a draft genome of S. japonica, which was ∼511.49 Mb long (contig N50 size of 17.34 Mb) based on Illumina, Nanopore and Hi-C data. We reliably assembled 110 contigs into 14 chromosomes, representing 91.62% of the total genome, with an improved N50 size of 31.32 Mb based on Hi-C data. Further investigation identified 271.76 Mb (53.13%) of repetitive sequences and 31,000 protein-coding genes, of which 30,721 (99.1%) were functionally annotated. Phylogenetic analysis indicates that S. japonica separated from Arabidopsis thaliana and Glycine max ∼107.53 and 61.24 million years ago, respectively. We detected evidence of species-specific and common-legume whole-genome duplication events in S. japonica. We further found that multiple TF families (e.g. BBX and PAL) have expanded in S. japonica, which might have led to its enhanced tolerance to abiotic stress. In addition, S. japonica harbours more genes involved in the lignin and cellulose biosynthesis pathways than the other two species. Finally, population genomic analyses revealed no obvious differentiation among geographical groups and the effective population size continuously declined since 2 Ma. Our genomic data provide a powerful comparative framework to study the adaptation, evolution and active ingredients biosynthesis in S. japonica. More importantly, our high-quality S. japonica genome is important for elucidating the biosynthesis of its main bioactive components, and improving its production and/or processing.     

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.     

Discovery and SAR analysis of 5-chloro-4-((substituted phenyl)amino)pyrimidine bearing histone deacetylase inhibitors

Histone deacetylases (HDACs) are validated targets for the development of anticancer drugs in epigenetics. In the discovery of novel HDAC inhibitors with anticancer potency, the 5-chloro-4-((substituted phenyl)amino)pyrimidine fragment is assembled as a cap group into the structure of HDAC inhibitors. The SAR revealed that presence of small groups (such as methoxy substitution) is beneficial for the HDAC inhibitory activity. In the enzyme inhibitory selectivity test, compound L20 exhibited class I selectivity with IC₅₀ values of 0.684 µM (selectivity index of >1462), 2.548 µM (selectivity index of >392), and 0.217 µM (selectivity index of >4608) against HDAC1, HDAC2 and HDAC3 compared with potency against HDAC6 (IC₅₀ value of >1000 µM), respectively. In the antiproliferative assay, compound L20 showed both hematological and solid cancer inhibitory activities. In the flow cytometry, L20 promoted G0/G1 phase cell cycle arrest and apoptosis of K562 cells.