链霉菌磷酸泛酰巯基乙胺基转移酶对载体蛋白的底物选择性的研究进展

磷酸泛酰巯基乙胺基转移酶(PPTase)催化脂肪酸合酶(FAS)、聚酮合酶(PKS)和非核糖体肽合成酶(NRPS)中载体蛋白从脱辅基形态转化为全辅基形态,对脂肪酸、PKS产物和NRPS产物的生物合成起着不可或缺的作用。本文介绍并总结了链霉菌PPTase对载体蛋白底物选择性的最新研究进展:Ⅲ型PPTase特异性催化同一个多肽链中ACP的辅基化;Ⅱ型PPTase倾向于催化Ⅰ型PKS中ACP和NRPS中PCP的辅基化;Ⅰ型PPTase倾向于催化Ⅱ型PKS中ACP和Ⅱ型FAS中ACP的辅基化;编码基因位于基因簇内的Ⅰ型/Ⅱ型PPTase倾向于催化编码基因位于同基因簇内的PKS/NRPS中ACP/PCP的辅基化;这些研究结果为阐明并改造链霉菌辅基化网络以提高特定次级代谢产物的产量提供了参考和借鉴。     

典型城市单元的土壤重金属溯源方法与实证研究

随着城市化进程的不断深入,土壤中重金属污染现状及其治理情况越来越受到重视,而查明污染源是有效治理污染的前提。源解析技术目前已广泛的应用于环境受体重金属来源解析实践中,总结了近年来土壤重金属成因分析的常用方法及原理,并提出了一种将多种方法相配合使用的方法体系。选取珠三角某市城郊农田作为研究对象,结果表明,Cd、Pb、Cu、Zn、As存在含量超过国家农用地筛选值的情况,其中Cd超标率高达60.1%。农业活动、工业生产、交通源和自然母质均对研究区土壤重金属的累积产生一定的贡献。正定矩阵因子分析法(PMF,Positive Matrix Factorization)模型模拟的Cd、Ni、Zn和Hg预测值与实测值线性拟合r~2均大于90%,其余元素r~2均大于60%,呈现出很好的相关性,满足研究需要。PMF模型和铅同位素比值法计算得到的交通及农业对土壤Pb累积的贡献率之和分别为86.0%和84.8%,PMF模型和物质流分析法计算得到的农业对土壤Cd的贡献率分别为86.7%和79.7%,结果均比较接近。结果表明正定矩阵因子法、同位素比值分析法,物质流分析法能很好的联用应用于土壤重金属源解析研究。     

基于土地利用及植被覆盖变化的黄河源区生境质量时空变化特征

位于青藏高原腹地的黄河源地区生态环境脆弱,面临生物多样性锐减、生态系统退化等问题,黄河源区生态系统保护及其高质量发展已成为国家的重点战略之一。土地利用与植被覆盖是影响生境质量的重要因素,定量化土地利用方式、强度及格局和植被覆盖格局对生态质量影响的研究越来越受到关注,但其对黄河源区生态质量的耦合效应尚不明确。基于2000年和2015年黄河源区土地利用类型及生长季归一化植被指数（NDVI）,采用InVEST模型探究了不同时期黄河源区生境质量时空变化,并采用地理加权回归（GWR）模型揭示了生境质量对土地利用和植被覆盖变化的空间响应特征。结果表明,2000年与2015年土地利用类型变化主要为未利用土地向草地的转移。植被覆盖变化方面,源区生长季NDVI整体上升。从生境质量的空间分布来看,黄河源区生境质量总体呈现南高北低的空间格局,高值分布在南部及中部地区,低值分布在北部布青山、东北部高海拔区及黄河乡的黄河沿岸。相较于2000年,2015年黄河源区生境质量平均提高11.47%。草地面积和NDVI与生境质量均呈显著正相关关系,其中NDVI是提高黄河源区生境质量的重要驱动因子。研究结果突出了NDVI对提高黄河源区生境质量的主导作用,可为未来源区生态保护提供借鉴。     

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。     

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,     

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.