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.     

TEB/POLQ plays dual roles in protecting Arabidopsis from NO-induced DNA damage

Nitric oxide (NO) is a key player in numerous physiological processes. Excessive NO induces DNA damage, but how plants respond to this damage remains unclear. We screened and identified an Arabidopsis NO hypersensitive mutant and found it to be allelic to TEBICHI/POLQ, encoding DNA polymerase θ. The teb mutant plants were preferentially sensitive to NO- and its derivative peroxynitrite-induced DNA damage and subsequent double-strand breaks (DSBs). Inactivation of TEB caused the accumulation of spontaneous DSBs largely attributed to endogenous NO and was synergistic to DSB repair pathway mutations with respect to growth. These effects were manifested in the presence of NO-inducing agents and relieved by NO scavengers. NO induced G2/M cell cycle arrest in the teb mutant, indicative of stalled replication forks. Genetic analyses indicate that Polθ is required for translesion DNA synthesis across NO-induced lesions, but not oxidation-induced lesions. Whole-genome sequencing revealed that Polθ bypasses NO-induced base adducts in an error-free manner and generates mutations characteristic of Polθ-mediated end joining. Our experimental data collectively suggests that Polθ plays dual roles in protecting plants from NO-induced DNA damage. Since Polθ is conserved in higher eukaryotes, mammalian Polθ may also be required for balancing NO physiological signaling and genotoxicity.     

抑郁症易感基因和抗抑郁药物靶点相关[]神经细胞类型及相互作用网络分析

基于抑郁症的全基因组关联分析研究(GWAS),对于获得的单核苷酸多态性位点(SNP)使用Haploreg软件进行基因注释,得到SNP注释的102个易感基因.。使用MAGMA软件对GWAS的汇总统计数据做基因水平的分析,获得了270个校正之后显著的基因,两者合并共得到320个抑郁症易感基因。通过药物数据库Drugbank获取133个抗抑郁药物靶点基因。使用EWCE包对抑郁症易感基因和抗抑郁药物靶点在三套脑组织单细胞测序数据中,分别进行神经细胞类型富集分析。结果发现大脑皮质的GABA神经元(抑制性神经元)和谷氨酸能神经元(兴奋性神经元)是抑郁症易感基因和抗抑郁药物靶点共同的神经元。这两种类型的神经细胞可能是抗抑郁药物与抑郁症易感基因相互作用的神经细胞,另外少突胶质前体细胞可能是抑郁症特有的易感神经细胞。使用Network Calculator软件构建网络并进行进行网络拓扑学参数分析。结果表明抑郁症易感基因与抗抑郁药物靶点组成了一个具有显著的相互连接的网络。本研究从单细胞层面揭示抑郁症的遗传机制,在网络层面为寻找新的抗抑郁药物靶点提供了一定的启示。     

土壤不同水分条件对长春花(Catharanthus roseus)生活史型的影响

为了研究土壤中不同水分条件对长春花生活史型形成及生理代谢的影响,设置对照、轻度干旱、中度干旱和重度干旱等土壤水分梯度,对长春花(Catharanthus roseus(L.)G.Don)幼苗进行处理。对长春花形态指标进行聚类分析发现选择的20个聚类实体被分为2组,第1组为对照(CK)和轻度干旱(LD)处理的植株,第2组为中度干旱(MD)和重度干旱(HD)处理的植株。运用主成分分析(Principal component analysis,PCA)方法对不同土壤水分条件下长春花营养生长(Vegetative growth,V)、有性生殖(Sexual reproduction,S)和无性繁殖(Clone reproduction,C)等3类15种性状进行统计。结果显示,长春花在对照条件下生活史型为V0.39S0.54C0.07,轻度干旱为V0.36S0.50C0.14,中度干旱为V0.53S0.27C0.20,重度干旱为V0.45S0.09C0.46,干旱程度加强显著提高了无性繁殖的比重,降低了有性生殖的比例。同时,对长春花中文朵灵、长春质碱和脱水长春碱等生物碱的含量进行了动态测定,发现重度干旱下的文朵灵、长春质碱和脱水长春碱的含量在16d时分别是对照水平的1.5倍、2.3倍和3.1倍,表明干旱胁迫诱导生物碱积累,为长春花高效栽培提供了理论依据。