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.     

AvrRps4 effector family processing and recognition in lettuce

During pathogenesis, effector proteins are secreted from the pathogen to the host plant to provide virulence activity for invasion of the host. However, once the host plant recognizes one of the delivered effectors, effector‐triggered immunity activates a robust immune and hypersensitive response (HR). In planta, the effector AvrRps4 is processed into the N‐terminus (AvrRps4^N) and the C‐terminus (AvrRps4^C). AvrRps4^C is sufficient to trigger HR in turnip and activate AtRRS1/AtRPS4‐mediated immunity in Arabidopsis; on the other hand, AvrRps4^N induces HR in lettuce. Furthermore, AvrRps4^N‐mediated HR requires a conserved arginine at position 112 (R112), which is also important for full‐length AvrRps4 (AvrRps4^F) processing. Here, we show that effector processing and effector recognition in lettuce are uncoupled for the AvrRps4 family. In addition, we compared effector recognition by lettuce of AvrRps4 and its homologues, HopK1 and XopO. Interestingly, unlike for AvrRps4 and HopK1, mutation of the conserved R111 in XopO by itself was insufficient to abolish recognition. The combination of amino acid substitutions arginine 111 to leucine with glutamate 114 to lysine abolished the XopO‐mediated HR, suggesting that AvrRps4 family members have distinct structural requirements for perception by lettuce. Together, our results provide an insight into the processing and recognition of AvrRps4 and its homologues.     

不同血清型肉毒毒素受体结合域研究进展

肉毒毒素(botulinum neurotoxin, BoNT)是人类已知毒性最强的蛋白质之一,可以引起肌肉松弛麻痹,严重时可导致死亡。肉毒毒素共分为7种血清型(BoNT/A-BoNT/G),根据氨基酸序列差异可进一步分为40多种亚型。肉毒毒素分子结构由3个基本结构域组成：重链羧基端细胞受体结合域、氨基端的易位域和轻链催化域。在运动神经元表面,受体结合域首先与聚唾液酸神经节苷脂结合,随后与突触囊泡蛋白2或突触囊泡结合蛋白结合形成双受体复合物。每种血清型的受体结合域都必须与其相应受体结合才能发挥作用。肉毒毒素的结构功能及其对宿主的作用一直都是研究热点。近年来,因受体结合域可以促进肉毒毒素与运动神经元膜特异性结合,而成为新的研究方向。本综述将概述不同血清型肉毒毒素与受体结合过程中受体结合域结构变化和结合位点差异。通过分析不同血清型及亚型的序列以及受体结合域结构特征,可以更好地了解细胞受体结合域的序列差异和功能,并为肉毒毒素的治疗策略提供新思路。     

水土保持林土壤改良效益评价研究

森林效益评价是目前林业研究的热点,也是难点问题,通过评价水土保持林土壤改良效益,对效益评价的方法和思路进行了探索。指出水土保持林土壤改良效益指标体系的总目标层为效益评价综合指数,准则层包括土壤肥力、抗蚀性能和抗冲性能３个方面,指标层由水稳性团聚体含量、有机质含量、土壤酶活性、ｐＨ值、平均重量直径、团聚度、分散率、土壤硬度和渗透系数构成。在计算效益评价综合指数时,首先要对各项指标的实测数据进行处理,     

Hemin with Peroxidase Activity Can Inhibit the Oxidative Damage Induced by Ultraviolet A

Excessive reactive oxygen species (ROS), a highly reactive substance that contains oxygen, induced by ultraviolet A (UVA) cause oxidative damage to skin. We confirmed that hemin can catalyze the reaction of tyrosine (Tyr) and hydrogen peroxide (H₂O₂). Catalysis was found to effectively reduce or eliminate oxidative damage to cells induced by H₂O₂ or UVA. The scavenging effects of hemin for other free-radical ROS were also evaluated through pyrogallol autoxidation, 1,1-diphenyl-2-picrylhydrazyl radical (DPPH·)-scavenging assays, and phenanthroline–Fe²⁺ assays. The results show that a mixture of hemin and tyrosine exhibits strong scavenging activities for H₂O₂, superoxide anion (O₂⁻·), DPPH·, and the hydroxyl radical (·OH). Furthermore, the inhibition of oxidative damage to human skin keratinocyte (HaCaT) cells induced by H₂O₂ or UVA was evaluated. The results show that catalysis can significantly reduce the ratio of cell apoptosis and death and inhibit the release of lactate dehydrogenase (LDH), as well as accumulation of malondialdehyde (MDA). Furthermore, the resistance to apoptosis was found to be enhanced. These results show that the mixture of hemin and tyrosine has a significantly protective effect against oxidative damage to HaCaT cells caused by UVA, suggesting it as a protective agent for combating UVA damage.     

Lysophosphatidic Acid Acyltransferase from Coconut Endosperm Mediates the Insertion of Laurate at the sn-2 Position of Triacylglycerols in Lauric Rapeseed Oil and Can Increase Total Laurate Levels

Expression of a California bay laurel (Umbellularia californica) 12:0-acyl-carrier protein thioesterase, bay thioesterase (BTE), in developing seeds of oilseed rape (Brassica napus) led to the production of oils containing up to 50% laurate. In these BTE oils, laurate is found almost exclusively at the sn-1 and sn-3 positions of the triacylglycerols (T.A. Voelker, T.R. Hayes, A.C. Cranmer, H.M. Davies [1996] Plant J 9: 229–241). Coexpression of a coconut (Cocos nucifera) 12:0-coenzyme A-preferring lysophosphatitic acid acyltransferase (D.S. Knutzon, K.D. Lardizabal, J.S. Nelsen, J.L. Bleibaum, H.M. Davies, J.G. Metz [1995] Plant Physiol 109: 999–1006) in BTE oilseed rape seeds facilitates efficient laurate deposition at the sn-2 position, resulting in the acccumulation of trilaurin. The introduction of the coconut protein into BTE oilseed rape lines with laurate above 50 mol % further increases total laurate levels.     

PNAS-4 expression and its relationship to p53 in colorectal cancer

PNAS-4 is a novel pro-apoptotic protein activated during the early response to DNA damage; however, the molecular mechanisms and pathways regulating PNAS-4 expression in tumors are not well understood. We hypothesized that PNAS-4 is a p53 down-stream target gene and designed this study. We searched online for putative p53-binding sites in the entire PNAS-4 gene and did not find any corresponding information. In HCT116 colon cancer cells, after being transfected with small interfering RNA to silence p53, the expressions of PNAS-4 and other known p53 target gene (Apaf1, Bax, Fas and Dr5) were determined by real-time PCR. We found that PNAS-4 was up-regulated while Apaf1, Bax, Fas and Dr5 were down-regulated. We then examined the expression of PNAS-4 and p53 mutation in colorectal cancer patients. PNAS-4 expressed both in colorectal cancers and normal tissues, but compared with paired control, PNAS-4 was up-regulated in cancers (P = 0.018). PNAS-4 overexpression ratios were correlated to the p53 mutant status (P = 0.001). The mean PNAS-4 expression levels of p53 mutant homozygote group and heterozygote group were higher than that of p53 wild type group (P = 0.013). The expression ratios of PNAS-4 (every sample in relative to its paired normal mucosa) were different between negative lymph node metastasis (66% up-regulated, 34% down-regulated) and positive metastasis (42% up-regulated, 58% down-regulated). Taken together, these findings suggested that PNAS-4 was not a p53 target, but overexpression of PNAS-4 was correlated to p53 inactivity in colorectal cancer.     

cⅠ857基因的体外定位同义突变

 ｃⅠ８５７基因的体外定位同义突变陈南春,高辉,陈苏民,杨萍,刘新平（西安第四军医大学分子生物学研究所,西安７１００３２）外源基因要在大肠杆菌中获得高表达,需要合适的ＳＤ序列和可调控的强启动子［１］。ＰＬ启动子在原核启动子中属强启动子,它受ｃⅠ基因产物...     

非嗜食植物乙醇提取物对小菜蛾种群控制作用评价

通过建立实验种群生命表和自然种群生命表,应用种群趋势指数(indexofpopulationtrend,I)和干扰作用控制指数(interferenceindexofpopulationcontrol,IIPC),评价花椒(Zanthoxylumbungeanum)、细叶桉(Eucalyptustereticornis)、烟草(Nicotianatabacum)、构树(Broussonetiapapyrifera)、羊蹄甲(Bauhiniavariegata)、假莲翘(Durantarepens)、飞扬草(Euphorbiahirta)、茶枯(Camelliaoleifera)8种非嗜食植物乙醇提取物对小菜蛾实验种群的控制作用,以及细叶桉、烟草、茶枯3种非嗜食植物乙醇提取物及其混合液对小菜蛾自然种群的控制作用.室内试验结果表明,在各种植物乙醇提取物作用下,I值从小到大的顺序为4.4842(细叶桉)、5.3702(花椒)、5.5199(飞扬草)、6.1609(假莲翘)、6.8937(羊蹄甲)8.0945(烟草)、9.8052(茶枯)、11.1382(构树),对照的I值为69.8964;IIPC值从小到大的顺序为0.0642(细叶桉)、0.0768(花椒)、0.0790(飞扬草)、0.0881(假莲翘)、0.0986(羊蹄甲)、0.1158(烟草)、0.1403(茶枯)、0.1594(构树),说明供试植物提取物对小菜蛾实验种群增长都有一定的抑制和干扰作用.小菜蛾自然种群生命表研究结果表明,在各种植物乙醇提取物作用下,I值从小到大的顺序为5.1997(细叶桉)、7.4160(烟草)、7.3644(茶枯)和3.1399(混合液),对照的I值为21.6232;IIPC值从小到大的顺序为混合液(0.1608)、细叶桉(0.2405)、茶枯(0.3549)、烟草(0.3695),说明供试植物提取物都能明显降低种群趋势指数,在一定程度上抑制和干扰小菜蛾自然种群增长,在生产中有一定的应用前景.