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.     

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.     

RUS家族对植物生长发育的调控作用

叶绿体基因组编码许多参与光合作用和其他代谢过程的关键蛋白质,在叶绿体中合成的代谢物对于植物正常的生长发育至关重要。根对紫外线-B辐射敏感[Root-UVB (ultraviolet radiation B)-sensitive, RUS]蛋白属于叶绿体蛋白,由高度保守的DUF647结构域组成,在参与植物形态发生、物质运输和能量代谢等多种生命活动的调控中发挥作用。本文就近年来关于RUS家族在植物的胚胎发育、光形态建成、维生素B6稳态、生长素转运和花药发育等生长发育过程中的相关研究进行回顾和总结,为深入研究其在植物生长发育中的分子调控机制提供了参考。     

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。     

Specific endotoxic lipopolysaccharide-binding receptors on murine splenocytes. III. Binding specificity and characterization.

In previously published studies, we employed a photoreactive radioiodinated derivative of LPS from Escherichia coli 0111:B4 to identify and characterize a membrane-localized specific LPS binding protein of approximately 80-kDa molecular mass. Our more recent studies demonstrating that mAb with specificity for this 80-kDa protein will act as an agonist in mediating macrophage activation have established that this protein serves as a specific receptor for LPS. In the experiments reported here, we have more accurately determined the apparent molecular mass of this protein to be 73 kDa (p73). We have also extended the sources of LPS-derivatized photo-cross-linking preparations (including Re-LPS) to determine generality of LPS binding to this receptor. Binding to the p73 LPS receptor is demonstrated with all of the LPS derivatives synthesized in our laboratory, as well as probes synthesized by other investigators. Binding of S-LPS is readily inhibited by Re chemotype LPS, and we have shown that this competitive inhibition is most likely not the result of formation of LPS aggregates. These results confirm and extend our earlier studies suggesting that the binding of LPS to the p73 receptor is lipid A specific. We further demonstrate that, in contrast to results published in a recent report, the p73 LPS receptor has no significant binding specificity for a variety peptidoglycan polymer preparations. Finally, we show that this LPS receptor can be detected on murine fibroblast, macrophage, and mastocytoma cell lines. Differences have been observed in the level of expression of LPS receptors on the various cell lines studied.     

Engineering mouse apolipoprotein A-I into a monomeric, active protein useful for structural determination

Apolipoprotein AI (apoAI), the major protein component of HDL, is one of the best predictors of coronary artery disease (CAD), with high apoAI and HDL levels being correlated with low occurrences of CAD. The primary function of apoAI is to recruit phospholipid and cholesterol for assembly of HDL particles. Like other exchangeable apolipoproteins, lipid-free apoAI forms a mixture of different oligomers even at 1.0 mg/mL. This self-association property of the exchangeable apolipoproteins is closely associated with the lipoprotein-binding activity of this protein family. It is unclear if the self-association property of apolipoprotein is required for its lipoprotein-binding activity. We developed a novel method for engineering an oligomeric protein to a monomeric, biologically active protein. Using this method, we generated a monomeric mouse apoAI mutant that is active. This mutant contains the first 216 residues of mouse apoAI and replaces six hydrophobic residues with either polar or smaller hydrophobic residues at the defined positions (V118A/A119S/L121Q/T191S/T195S/T199S). Cross-linking results show that this mutant is greater than 90% monomeric at 8 mg/mL. CD, DSC, and NMR results indicate that the mutant maintains an identical secondary, tertiary structure and stability as those of the wild-type mouse apoAI. Lipid-binding assays suggest that the mutant shares an equal lipoprotein-binding activity as that of the wild-type apoAI. In addition, both the monomeric mutant and the wild-type protein make nearly identical rHDL particles. With this monomeric mouse apoAI, high-quality NMR data has been collected, allowing for the NMR structural determination of lipid-free apoAI. On the basis of these results, we conclude that this apoAI mutant is a monomeric, active apoAI useful for structural determination.