Oxidized high-density lipoprotein promotes CD36 palmitoylation and increases lipid uptake in macrophages

Oxidized high-density lipoprotein (oxHDL) reduces the ability of cells to mediate reverse cholesterol transport and also shows atherogenic properties. Palmitoylation of cluster of differentiation 36 (CD36), an important receptor mediating lipoprotein uptake, is required for fatty acid endocytosis. However, the relationship between oxHDL and CD36 has not been described in mechanistic detail. Here, we demonstrate using acyl-biotin exchange analysis that oxHDL activates CD36 by increasing CD36 palmitoylation, which promotes efficient uptake in macrophages. This modification increased CD36 incorporation into plasma lipid rafts and activated downstream signaling mediators, such as Lyn, Fyn, and c-Jun N-terminal kinase, which elicited enhanced oxHDL uptake and foam cell formation. Furthermore, blocking CD36 palmitoylation with the pharmacological inhibitor 2-bromopalmitate decreased cell surface translocation and lowered oxHDL uptake in oxHDL-treated macrophages. We verified these results by transfecting oxHDL-induced macrophages with vectors expressing wildtype or mutant CD36 (mCD36) in which the cytoplasmic palmitoylated cysteine residues were replaced. We show that cells containing mCD36 exhibited less palmitoylated CD36, disrupted plasma membrane trafficking, and reduced protein stability. Moreover, in ApoE^−/−CD36^−/− mice, lipid accumulation at the aortic root in mice receiving the mCD36 vector was decreased, suggesting that CD36 palmitoylation is responsible for lipid uptake in vivo. Finally, our data indicated that palmitoylation of CD36 was dependent on DHHC6 (Asp-His-His-Cys) acyltransferase and its cofactor selenoprotein K, which increased the CD36/caveolin-1 interaction and membrane targeting in cells exposed to oxHDL. Altogether, our study uncovers a causal link between oxHDL and CD36 palmitoylation and provides insight into foam cell formation and atherogenesis.     

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.     

一株产絮凝剂不动杆菌的筛选及其絮凝特性

从活性污泥中筛选出一株高效的微生物絮凝剂产生菌,鉴定为鲍曼不动杆菌.蚕豆根尖细胞微核试验未显示该菌株所产絮凝剂具有遗传毒性.该菌产絮凝剂的最佳碳源和氮源分别为葡萄糖和酵母浸出汁,培养时间为24 h.在絮凝体系中加入Ca2 能明显提高发酵液的絮凝率.在pH为8.0时对高岭土悬浊液和污水具有良好的絮凝效果.     

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。     

云南主要有害实蝇种类及区划

采用诱剂诱捕辅以受害瓜果调查的方法,系统梳理了云南有害实蝇类群,确定主要有害种类25种。基于云南自然地理特征,采用聚类分析法深入探讨了云南实蝇区系,提出云南实蝇地理区划可分为德宏、版纳盆谷区,南部边缘中低山宽谷区,滇西南山原峡谷区,中东部高原、岩溶山原区以及北部高原山地区等5大区域。研究发现,受热量条件制约,云南主要有害实蝇类群呈南北向梯度递减;受纵向岭谷区"通道-阻隔"综合作用影响,云南西南部河谷地区是实蝇危害较为严重的区域。首次系统揭示了云南主要有害实蝇的地理分布规律。     

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,     

Depression compromises antiviral innate immunity via the AVP-AHI1-Tyk2 axis

Depression is a serious public-health issue. Recent reports have suggested higher susceptibility to viral infections in depressive patients. However, how depression affects antiviral innate immune signaling remains unknown. Here, we revealed a reduction in expression of Abelson helper integration site 1 (AHI1) in the peripheral blood mononuclear cells (PBMCs) and macrophages from the patients with major depressive disorder (MDD), which leads to attenuated antiviral immune response. We found that depression-related arginine vasopressin (AVP) induces reduction of AHI1 in macrophages. Further studies demonstrated that AHI1 is a critical stabilizer of basal type-I-interferon (IFN-I) signaling. Mechanistically, AHI1 recruits OTUD1 to deubiquitinate and stabilize Tyk2, while AHI1 reduction downregulates Tyk2 and IFN-I signaling activity in macrophages from both MDD patients and depression model mice. Interestingly, we identified a clinical analgesic meptazinol that effectively stimulates AHI1 expression, thus enhancing IFN-I antiviral defense in depression model mice. Our study promotes the understanding of the signaling mechanisms of depression-mediated antiviral immune dysfunction, and reveals meptazinol as an enhancer of antiviral innate immunity in depressive patients.Subject terms: Innate immunity, Ubiquitylation, Cell signalling     

三种回归分析方法在Hyperion影像LAI反演中的比较

借助GPS进行地面精确定位,利用LAI-2000冠层分析仅在攸县黄丰桥林场开展130个样地(60m×60m)的叶面积指数(Leaf Area Index,LAI)测量.采用FLAASH模块对Hyperion数据进行大气校正并与地面同步冠层观测数据进行拟合,通过研究地面实测LAI与Hyperion影像波段及其衍生的系列植被指数(NDVI、RVI等)的相关性,筛选出估算叶面积指数的植被指数因子.应用曲线估计、逐步回归及偏最小二乘三种回归分析技术分别建立叶面积指数的最优估算模型.结果表明:参与建模的因子中,比值植被指数(RVI)与LAI的相关性最大,敏感性最高,其次是SARVI0.1,NDVI705,NDVI,SARVI0.1,SARVI0.25;曲线估计、逐步回归分析和偏最小二乘回归三种分析方法所建的6个回归模型中,偏最小二乘回归的拟合效果最好,预测值与实测值的决定系数R2为0.84、曲线估计的拟合效果最低,预测值与实测值的决定系数R2为0.64;建模精度分析表明,选用5-6个自变量因子进行LAI建模是可靠的,以6个植被因子建立的偏最小二乘回归模型预测精度最高.