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.     

Intravital lipid droplet labeling and imaging reveals the phenotypes and functions of individual macrophages in vivo

Macrophages play pivotal roles in the maintenance of tissue homeostasis. However, the reactivation of macrophages toward proinflammatory states correlates with a plethora of inflammatory diseases, including atherosclerosis, obesity, neurodegeneration, and bone marrow (BM) failure syndromes. The lack of methods to reveal macrophage phenotype and function in vivo impedes the translational research of these diseases. Here, we found that proinflammatory macrophages accumulate intracellular lipid droplets (LDs) relative to resting or noninflammatory macrophages both in vitro and in vivo, indicating that LD accumulation serves as a structural biomarker for macrophage phenotyping. To realize the staining and imaging of macrophage LDs in vivo, we developed a fluorescent fatty acid analog-loaded poly(lactic-co-glycolic acid) nanoparticle to label macrophages in mice with high efficiency and specificity. Using these novel nanoparticles, we achieved in situ functional identification of single macrophages in BM, liver, lung, and adipose tissues under conditions of acute or chronic inflammation. Moreover, with this intravital imaging platform, we further realized in vivo phenotyping of individual macrophages in the calvarial BM of mice under systemic inflammation. In conclusion, we established an efficient in vivo LD labeling and imaging system for single macrophage phenotyping, which will aid in the development of diagnostics and therapeutic monitoring. Moreover, this method also provides new avenues for the study of lipid trafficking and dynamics in vivo.Supplementary key words: macrophage, inflammation, lipid droplet, nanoparticle delivery, in vivo imaging, fatty acid analog, bone marrow, systemic inflammation, lipid trafficking, biomarker

Macrophages, a type of immune cells, almost reside in all tissues of body, from the skin to the bone marrow (BM) (1). Macrophages have remarkable plasticity, and they can be activated into specific subtypes by modifying their physiology and functions in response to local environmental cues. Activated macrophages are commonly divided into proinflammatory killing subtype and anti-inflammatory repairing subtype. Proinflammatory macrophages responding to bacteria, IFN-γ, and lipopolysaccharide (LPS) are involved in host defense and inflammation, whereas anti-inflammatory macrophages responding to interleukin-4 (IL-4), IL-10, and IL-13 play a pivotal role in tissue homeostasis and remodeling (2). Increasing evidence indicates that the reactivation of macrophages toward proinflammatory states under diverse kinds of stress is correlated with a plethora of inflammatory diseases, such as atherosclerosis, diabetes, obesity, rheumatoid arthritis, neurodegeneration, and BM failure syndromes (3, 4). Thus, characterization of macrophage activation status and the underlying molecular mechanism in situ will help elucidate their functions in these diseases; however, in vivo analysis of the macrophage activation status in their native multicellular microenvironment is challenging.Although lipid droplets (LDs) have been initially described as intracellular fat storage organelles in adipocytes, increasing studies indicate that myeloid cells also form LDs under inflammation and stress (5, 6). Macrophages, as the effector cells of innate immunity, are found to form LDs to support their host defense when exposed to pathogens, such as parasites, bacteria, and viruses (7, 8, 9, 10, 11). However, abnormal LD accumulation in tissue-resident macrophages correlates with the pathogenesis of various inflammatory diseases. For instance, foam cells in atherosclerotic lesions can maintain the local inflammatory response by secreting proinflammatory cytokines (12, 13, 14). Moreover, LD-accumulating microglia contribute to neurodegeneration by producing high levels of reactive oxygen species (ROS) and secreting proinflammatory cytokines (15). These findings indicate that LD accumulation might be a hallmark of macrophages with proinflammatory functions.In this study, based on the typical activation of in vitro BM-derived macrophages, we find that proinflammatory M_{(LPS + IFN-γ)} macrophages are characterized by LD accumulation, whereas resting macrophages and anti-inflammatory M_(IL-4) and M_(IL-10) macrophages do not contain any LDs. These features also hold for Matrigel plug-recruited macrophages and tissue-resident macrophages in mice. These findings demonstrate that LD accumulation could serve as a morphological index to distinguish proinflammatory macrophages from others.It is feasible to distinguish LD-containing cells using imaging techniques, which has translational potential for identification of proinflammatory macrophages in vivo. However, current techniques for LD visualization are traditional in vitro staining method, and in vivo staining and imaging of LD in individual macrophages remains a challenge. Through nanocarrier screening, we selected the poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) as nanocarrier to deliver the lipophilic carbocyanine dye (DiI_C18(5) solid (1,1''-dioctadecyl-3,3,3'',3''-tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate salt) [DiD]) and lipid staining dye (C1-BODIPY 500/510-C12) into macrophages. Using these dual fluorescence-labeled PLGA NPs, we achieved in situ and in vivo functional identification of single macrophages in various tissues under systemic or local inflammatory stress. Collectively, this study establishes an efficient in vivo labeling and imaging system of intracellular LDs for phenotyping the activation status and functions of individual macrophages in their dynamic niche, which is pivotal for disease diagnosis and preclinical research.     

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.     

天山林区六种灌木生物量的建模及其器官分配的适应性

灌木全株生物量估算模型的构建仍存在一定困难,对灌木生物量在器官分配上所体现的适应性研究也不够充分。以天山林区6种常见灌木为研究对象,在天山的东段、中段、西段林区分别设置样地进行群落调查,由此以全株收获法取得6种常见灌木若干标准株的全株、根、枝、叶及各径级根的生物量,将D~2H(地径平方与高度的乘积)与V(冠幅面积与高度的乘积)分别选为估测模型的自变量,通过回归分析法建立了各种灌木全株生物量的最优估算模型,然后比较了此6种灌木全株生物量在营养器官上分配差异以及根系生物量在径级上的分配差异。结果表明:(1)天山林区6种常见灌木中,小檗(Berberis heteropoda Schrenk)、忍冬(Lonicera hispida Pall.ex Roem.et Schuet.)、栒子(Cotoneaster melanocarpus Lodd.)的全株生物量约为8.48—9.01 kg,蔷薇(Rosa spinosissima L.)、绣线菊(Spiraea hypericifolia L.)、方枝柏(Juniperus pseudosabina Fisch.et Mey.)的全株生物量约为2.71—3.20 kg;(2)蔷薇、绣线菊、栒子的全株生物量最优估测模型是以V为自变量的函数,小檗、忍冬、方枝柏的全株生物量最优估测模型是以D~2H为自变量的函数,各模型R~2值均在0.850以上,且在P0.05水平上达到显著,模型模拟结果达到了较高的准确度;(3)6种灌木全株生物量在根、枝上的分配比重差异不显著,仅在叶上的分配比重有差异(P0.05);根系生物量在径级上的分配均呈现随根系径级下降而减少的规律,6种灌木在径级大于2 mm根上的分配比重存在差异(P0.05,径级大于20 mm根为P0.01水平);(4)6种灌木全株生物量在营养器官上的分配差异以及根系生物量在径级上的分配差异均体现了各物种对其生境选择的适应策略。     

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。