Contents of Volume 53

Plant Molecular Biology -     

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.     

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.     

体外重构STUB1介导α-1抗胰蛋白酶突变体Z蛋白泛素化修饰反应体系

α-1抗胰蛋白酶Z型突变体蛋白(α-1 antitrypsin Z-mutant protein, ATZ)是引发α-1抗胰蛋白酶缺陷症(α-1 antitrypsin deficiency, AATD)的主要原因,研究ATZ蛋白的泛素化修饰和降解对于治疗AATD具有重要意义。STUB1是一种重要的E3泛素连接酶,参与调节多种蛋白质的泛素化修饰。然而,STUB1是否参与ATZ的泛素化修饰尚未明确。本研究首先将ATZ和STUB1的编码基因克隆到pET28a质粒,构建了这2个蛋白的表达质粒。随后,将重组质粒转入大肠杆菌表达系统,在优化诱导条件实现了重组蛋白的异源表达。通过金属螯合亲和层析技术纯化得到目的蛋白,并通过蛋白质谱分析验证了其氨基酸序列的准确性。利用纯化的ATZ和STUB1重组蛋白,构建了一个体外泛素化修饰反应体系。实验结果显示,在ATP、E1泛素激活酶和E2泛素结合酶的协同作用下,STUB1成功催化了ATZ的泛素化修饰。本研究提供了一种体外获得Z型突变体ATZ纯化蛋白的方法,并确认了STUB1介导ATZ的泛素化修饰功能,推进了对α-1抗胰蛋白酶Z型突变体蛋白在细胞内降解过程的调控机制的理解。     

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.