350 Genome Informatics 14 350–351 (2003) Multiscale Bootstrap Analysis of Gene Networks Ba
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㊀山东农业科学㊀2024ꎬ56(4):172~180ShandongAgriculturalSciences㊀DOI:10.14083/j.issn.1001-4942.2024.04.022收稿日期:2023-03-30基金项目:山东省自然科学基金项目(ZR2021M055)ꎻ国家重点研发计划课题(2021YFB3901303)作者简介:曾世伟(2000 )ꎬ男ꎬ硕士研究生ꎬ主要从事农业遥感研究ꎮE-mail:1422180426@qq.com通信作者:侯学会(1985 )ꎬ女ꎬ博士ꎬ助理研究员ꎬ主要从事农业遥感研究ꎮE-mail:sxhouxh@126.com王宗良(1986 )ꎬ男ꎬ博士ꎬ副教授ꎬ主要从事光纤传感研究ꎮE-mail:wangzongliang@lcu.edu.cn基于无人机遥感的作物表型参数获取和应用研究进展曾世伟1ꎬ2ꎬ侯学会2ꎬ王宗良1ꎬ骆秀斌2ꎬ巫志雄1ꎬ2ꎬ王宏军1(1.聊城大学物理科学与信息工程学院ꎬ山东聊城㊀252000ꎻ2.山东省农业科学院农业信息与经济研究所ꎬ山东济南㊀250100)㊀㊀摘要:作物表型参数是由基因和环境因素决定或影响的作物生理㊁生化特征和性状ꎮ通过获取不同环境㊁不同生长时期的作物表型信息ꎬ可直观了解作物生长状况ꎬ以及时调整栽培管理措施ꎬ保障作物高效生产ꎮ无人机搭载RGB相机㊁光谱相机㊁激光雷达等传感器ꎬ可充分发挥灵活性好㊁获取数据效率高㊁成本相对较低等优势ꎬ实现作物表型参数信息的高效获取ꎬ同时ꎬ快速发展的图像处理和识别分类技术又为无人机遥感获取的作物表型参数信息提供了有效的处理和分析方法ꎬ从而使得作物监测更加便捷㊁高效ꎮ本文总结了无人机遥感获取作物表型参数信息的流程与方法ꎬ概括了基于无人机遥感开展作物株高㊁冠层覆盖度㊁叶面积指数㊁水分胁迫㊁生物量㊁产量等表型参数研究的现状ꎬ并对无人机遥感技术在作物表型参数信息解析方面的应用前景进行了展望ꎬ以期为充分发挥该技术在农业生产中的作用提供参考ꎮ关键词:无人机遥感ꎻ作物表型参数ꎻ作物监测中图分类号:S127㊀㊀文献标识号:A㊀㊀文章编号:1001-4942(2024)04-0172-09ResearchProgressofObtainingandUtilizingCropPhenotypicParametersBasedonUAVRemoteSensingZengShiwei1ꎬ2ꎬHouXuehui2ꎬWangZongliang1ꎬLuoXiubin2ꎬWuZhixiong1ꎬ2ꎬWangHongjun1(1.SchoolofPhysicalScienceandInformationTechnologyꎬLiaochengUniversityꎬLiaocheng252000ꎬChinaꎻ2.InstituteofInformationandEconomicResearchꎬShandongAcademyofAgriculturalSciencesꎬJinan250100ꎬChina)Abstract㊀Cropphenotypicparametersrefertocropphysiologicalandbiochemicalcharacteristicsthataredeterminedorinfluencedbygeneticandenvironmentalfactors.Throughobtainingcropphenotypicinformationunderdifferentenvironmentsandgrowthperiodsꎬthegrowthstatusofcropscouldbeknownintuitivelysothatcultivationmanagementstrategiescouldbeadjustedintimetoensurehighcropproductivity.ThroughcarryingdifferentsensorssuchasRGBcameraꎬspectrumcameraandLIDARꎬUAVremotesensinghasadvantagesofgoodflexibilityꎬhighefficiencyandrelativelylowcostinacquiringdataꎬwhichprovidesanefficientwaytoobtaincropsphenotypicinformation.Atthesametimeꎬfastdevelopingimageprocessingandrecognitionandclassificationtechnologiesprovideseffectiveprocessingandanalysismethodsforcropphenotypicparameterin ̄formationobtainedbyUAVremotesensing.Allthesemakecropmonitoringmoreconvenientandefficient.InthispaperꎬprocessandmethodsofobtainingphenotypicparameterinformationwereintroducedꎬandresearchstatusofcropphenotypicparametersbasedonUAVremotesensingsuchasplantheightꎬcanopycoverageꎬleafareaindexꎬwaterstressꎬbiomassandyieldweresummarizedꎬandtheapplicationforegroundofUAVremotesensingtechnologyincropphenotypicinformationanalysiswasprospectedꎬhopingtoprovidereferencesforbetterapplicationofthetechnologyinagriculturalproduction.Keywords㊀UAVremotesensingꎻCropphenotypicparametersꎻCropmonitoring㊀㊀随着世界人口快速增长㊁可耕地面积越来越少㊁全球气候急剧变化和资源短缺加剧ꎬ农业生产面临着严峻的挑战ꎬ粮食安全问题日益突出[1]ꎮ因此ꎬ培育优良品种以达到稳产㊁增产的目的ꎬ成为目前作物研究的热点方向之一ꎮ作物表型信息如株高㊁叶面积指数㊁生物量等影响着后期产量的形成ꎬ是育种过程中的重要参考指标ꎮ传统的作物表型信息获取多采用人工地面抽样调查法ꎬ费时㊁费力且观测数量有限ꎬ不能满足大面积作物信息调查需求ꎮ近年来ꎬ低空无人机遥感技术快速发展ꎬ通过无人机搭载RGB相机㊁光谱相机㊁激光雷达等构建无人机遥感平台ꎬ能够快速㊁高效获取一定范围内作物冠层的株高㊁叶面积指数㊁生物量等的连续动态信息ꎬ从而实现作物产量的动态预测[2]ꎮ目前ꎬ在田间作物表型遥感监测研究中应用的无人飞行器有无人直升机㊁飞艇㊁固定翼无人机㊁多旋翼无人机等ꎬ其中对起降条件要求不高且可以满足任何飞行轨迹要求的多旋翼无人机应用较为广泛ꎬ获取作物表型信息更加方便㊁快捷[3]ꎮ但由于无人机负载能力有限ꎬ其搭载的传感器需要满足高精度㊁轻质量和小尺寸的要求ꎬ目前适合无人机搭载的主要传感器有RGB数码相机㊁红外热成像仪㊁多光谱相机㊁高光谱相机㊁多谱段激光雷达等ꎮ不同的传感器性能不同ꎬ获取的作物表型参数信息也不同ꎬ导致最终得到的遥感监测结果不同[4-5]ꎮRGB相机[6]㊁热红外成像仪[7]㊁多光谱相机[8-9]和高光谱相机[10-11]成像原理相同ꎬ都是通过感测光谱波段来捕获图像信息ꎬ但它们感测光谱波段的种类和能力存在差异[12]ꎬ因此可用于测量不同的表型参数[13]ꎬ其中ꎬRGB相机可用于测量作物的株高㊁冠层覆盖度等ꎻ热红外成像仪可实现在生物和非生物胁迫条件下对作物表型参数的间接测定ꎬ尤其在测量作物的冠层温度时效果较好ꎻ多光谱相机和高光谱相机都能测量作物的叶面积指数㊁生物量㊁产量等表型参数ꎬ但高光谱相机的光谱分辨率更高ꎬ能获得更多的波段数据ꎬ可测量更多的作物表型参数ꎬ然而同时也存在数据处理过程更加复杂㊁仪器价格较高的问题ꎮ多谱段激光雷达能够分析作物的光谱特性和空间目标方位㊁距离㊁三维形貌和状态特征[14]ꎬ常用于对作物株高和生物量的测量研究ꎮ本文综述了无人机遥感监测农作物表型参数的信息获取流程㊁方法及研究进展ꎬ并对今后的研究方向进行展望ꎬ以期为深入研究和应用该技术提供参考ꎮ1㊀无人机遥感监测图像数据的处理及信息提取流程和方法1.1㊀图像处理遥感图像处理是利用无人机遥感研究作物表型的基础ꎮ因遥感图像存在由大气㊁传感器㊁无人机飞行状态等因素引起的几何畸变和辐射畸变ꎬ在提取作物表型参数之前必须对图像进行预处理ꎬ以有效改善提取表型参数信息的精度[15]ꎮ图像处理过程包括辐射定标㊁几何校正㊁数据质量检查㊁图像特征点提取㊁图像特征匹配㊁空中三角测量与区域网平差㊁生成数字高程模型(DEM)㊁正射校正生成数字正射影像(DOM)和拼接镶嵌等[16]ꎮ需根据无人机搭载的传感器类型选择合适的图像处理方法ꎮ如戴建国等[17]获取可见光图像后ꎬ使用Pix4Dmapper软件进行图像快速拼接检查ꎬ然后通过正射校正获得高质量㊁高精度的正射影像图ꎻ程雪等[18]获取高光谱影像后ꎬ除了使用Pix4DMapper软件进行拼接镶嵌外ꎬ还采用辐射定标以及大气校正等对图像进行了处理ꎮNäsi等[19]将得到的光谱图像依次进行了辐射标定㊁几何校正㊁图像融合和图像增强ꎬ然后使用371㊀第4期㊀㊀㊀㊀㊀㊀曾世伟ꎬ等:基于无人机遥感的作物表型参数获取和应用研究进展ArcGIS㊁ENVI等软件提取光谱反射率ꎬ用于建立研究作物表型性状的植被指数ꎮ1.2㊀特征集的选取作物特征包括植被指数特征㊁纹理特征等ꎬ在实际应用时需根据研究目的选择合适的特征来构成特征集ꎮ植被指数是通过多个波段数据计算得出的ꎬ能够有效度量作物株高㊁生物量和覆盖度等表型信息[20]ꎮ常用的植被指数有归一化差值植被指数(NDVI)㊁绿色归一化植被指数(GNDVI)㊁比值植被指数(RVI)㊁红绿蓝植被指数(RGBVI)㊁红边归一化植被指数(rNDVI)㊁优化土壤调节植被指数(OSAVI)㊁修正归一化植被指数(mNDVI)㊁可见光大气阻抗植被指数(VARI)㊁蓝绿色素指数(BGI2)㊁增强植被指数(EVI2)等ꎮ其中ꎬNDVI能够突出植被在图像中的显示ꎬ可准确估测植被的覆盖度[12]ꎻVARI㊁NDVI㊁RVI㊁rNDVI㊁mNDVI㊁GNDVI能有效预测叶面积指数[18]ꎻNDVI㊁OSA ̄VI㊁BGI2等常被用于预测植物叶片的叶绿素含量[21]ꎻVARI能有效预测作物的水分胁迫ꎻRDVI㊁RGBVI在估测作物生物量方面效果较好[22]ꎮ图像的灰度分布及其重复性是纹理特征的表现形式ꎬ可以反映地物的视觉粗糙程度ꎮ不同地物表现出的纹理特征不同ꎬ因此可根据该特征描述和识别地物[16]ꎮ另外ꎬ同一波段的图像有相同种类的纹理特征ꎬ可通过最小噪声分离变换和基于主成分分析方法等提取纹理滤波特征ꎬ选择最佳波段ꎬ作为最终纹理滤波特征[23]ꎮ1.3㊀特征筛选用于遥感图像估测表型参数的属性特征很多ꎬ若不经过筛选ꎬ则分析特征和训练模型所需要的时间会很长ꎬ模型也会很复杂ꎬ从而导致模型的泛化能力下降ꎬ不利于在实际生产中推广应用ꎮ因此ꎬ需在保证估测精度的前提下ꎬ选用最少的特征来构建模型ꎬ以避免特征变量过多引起的 维数灾难 ꎮ常用的特征筛选方法大致分为三类ꎬ分别是过滤式㊁包裹式㊁嵌入式筛选法[24]ꎮ过滤式特征筛选法先选定特征再进行学习ꎬ具有较强通用性ꎬ其典型方法有ReliefF算法ꎻ包裹式特征筛选法利用学习算法的性能来评价自身优劣ꎬ筛选得到的特征集分类性能较好ꎬ其典型方法有SVM-RFE算法ꎻ嵌入式特征筛选法将特征选择过程作为学习过程的一部分ꎬ在学习过程中自动进行特征筛选ꎬ特征筛选效果最好㊁速度最快且模式单调ꎬ其典型方法有Lasso算法[25]ꎮ特征选定后ꎬ还要根据估测能力强弱对其进行权重赋值ꎬ最终构建出最佳特征集ꎬ用于建立估测模型ꎮ1.4㊀模型的构建及精度评价构建估测模型能够表征遥感数据与作物特征的相关性ꎬ可为定量反演作物的表型参数奠定基础[1]ꎮ1.4.1㊀数据集的划分㊀估测模型的构建及其精度与样本数量和质量紧密相关ꎬ因此确保田间采样质量是保证构建模型估测效果的重要前提[18]ꎮ采集到的样本首先要采用适当的方法合理地划分成训练样本集和验证样本集ꎮ常见的划分方法有留出法㊁交叉验证法和自助法ꎬ其中交叉验证法是无人机遥感监测作物表型参数研究中最常用的方法ꎮk折交叉验证是典型的交叉验证法ꎬ其原理是将数据集分成k个样本数相等的子集ꎬ任选其中1个子集作为测试集ꎬ另外k-1个子集作为训练集ꎬ然后无重复地执行k次ꎬ使得每个子集都能作为训练集和测试集来训练模型ꎮ1.4.2㊀模型构建㊀除数字高程模型能够有效且快速获取作物株高信息外ꎬ其他表型参数的估测模型一般采用机器学习算法构建ꎮ根据训练数据是否拥有标记信息ꎬ可将机器学习算法分为监督式和非监督式两种[26]ꎮ分类和回归算法是典型的监督式算法ꎬ包括支持向量回归(SVR)㊁随机森林回归(RFR)㊁人工神经网络(ANN)㊁多元线性回归(MLR)等ꎬ其中回归算法更适用于数据具有连续性的叶面积指数㊁生物量㊁产量㊁水分胁迫等的监测ꎬ而分类算法更适用于作物分类和冠层覆盖度等的监测ꎮ另外还有一些表型参数研究没有足够的先验知识ꎬ很难对其进行人工标注且标注成本较高ꎬ通常采用无监督算法训练被标记的样本ꎬ以解决模式识别过程中的各种问题ꎮ聚类算法是非监督学习算法的代表ꎬ依据相似度进行分471山东农业科学㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第56卷㊀类ꎬ典型的聚类算法有K均值(K-means)聚类算法和K-中心点(K-medoids)聚类算法ꎮ1.4.3㊀模型估测精度评价㊀估测模型的精度评价ꎬ通常用决定系数(R2)和均方根误差(RMSE)作为评判预测值与实测值拟合效果的指标ꎬ其中ꎬR2值越接近1ꎬ说明模型的参考价值越高ꎻRMSE值越小ꎬ说明模型精度越高[27]ꎮ2㊀无人机遥感监测作物表型参数的研究进展2.1㊀作物株高株高能够反映作物的群体结构状况ꎬ植株过高易导致倒伏ꎬ而过矮会降低群体中下部的通风和透光ꎬ导致光合效率下降ꎬ进而影响作物产量ꎬ因此株高监测在作物生产调控中具有重要意义ꎮ作物株高监测通常利用获取可见光数据来测量ꎮ张宏鸣等[28]用无人机搭载数码相机获取作物的可见光图像ꎬ采用高清数码正射影像(DOM)和数字表面模型(DSM)相结合的骨架算法提取植株骨架ꎬ估测作物株高的精度较高(R2=0.923ꎬRMSE=11.493cmꎬMAE=8.927cm)ꎮ牛庆林等[29]利用无人机拍摄玉米的高清数码影像ꎬ将其与地面控制点(GCP)结合进行图像拼接处理ꎬ生成相应的DSM和DOMꎬ得到的株高预测值与实测值拟合性较高(R2=0.93ꎬRMSE=28.69cmꎬnRMSE=17.90%)ꎮ刘治开等[30]用无人机拍摄冬小麦的高清数码影像ꎬ通过构建作物DSM及作物高度模型(CHM)来测量小麦株高ꎬ最终得到的估测结果较好(R2和RMSE分别为0.82和4.31cm)ꎮKhan等[31]使用无人机遥感平台拍摄小麦的RGB图像ꎬ采用Pix4Dmapper软件处理后用于估测小麦株高ꎬ精度较高(R2=0.85ꎬRMSE=6.64cm)ꎮ此外ꎬ有研究者利用多光谱和高光谱成像技术获得多个波段和空间特征来测量作物株高ꎮ边琳等[32]使用无人机搭载多光谱传感器获得烤烟的遥感信息ꎬ捕捉到多个波段的反射光ꎬ通过构建光谱反射率与烤烟株高的拟合模型ꎬ估测烤烟株高的效果最佳(R2=0.785)ꎮAasen等[33]利用无人机采集三维高光谱图像来建立三维表面高光谱模型ꎬ实现株高可视化ꎬ株高估算效果也较好(R2=0.7)ꎮ但总体来说ꎬ利用高光谱成像技术测量作物株高的效果并不理想ꎬ而在估测作物覆盖度[34]㊁生物量[35]㊁叶面积指数[36]㊁产量[37]等表型参数时的精确度则较好ꎮ2.2㊀作物冠层覆盖度冠层覆盖度是反映作物生长状况的重要因素ꎬ可通过提取冠层覆盖度监测作物长势[38]ꎮ通过无人机遥感平台获取可见光图像和多光谱图像ꎬ然后利用计算机视觉方法或植被指数和光谱反射率建模反演等方法可快速得到作物的冠层覆盖信息[39]ꎮJin等[40]利用无人机遥感搭载数码相机获取研究区域的可见光成像数据ꎬ采用原始颜色特征作为模型输入ꎬ选用支持向量机算法训练作物分类模型ꎬ并选用粒子群优化算法(PSO)训练SVM模型参数(惩罚系数c㊁不敏感损失系数ε以及核函数功能γ)ꎬ最终监测结果的RMSE和rRMSE分别为34.05株/m2和14.31%ꎬ偏差为9.01株/m2ꎮ万亮等[41]利用无人机搭载多光谱相机获取多光谱图像ꎬ将各个波段的光谱反射率作为特征输入到随机森林回归模型ꎬ最终得到的结果较好(R2=0.93ꎬrRMSE=9.47%)ꎮ武威等[42]采用图像处理技术分析小麦图像的颜色特征 绿光标准化值(NDIG)ꎬ并提出叶片盖度(LCD)参数ꎬ将NDIG和LCD相结合作为多元逐步回归模型的输入特征ꎬ估测效果较好(R2=0.896)ꎮ周在明等[43]使用四旋翼无人机搭载ADCAir多光谱相机ꎬ通过NDVI指数模型获取多光谱植被覆盖度信息ꎬ以高精度可见光影像为真值进行验证ꎬ结果表明NDVI模型估算值与真实值之间的决定系数为0.92ꎬ具有较好的一致性ꎮ相比广泛应用的无人机可见光图像[23ꎬ44-46]ꎬ利用无人机多光谱图像反演植被覆盖度时图像的空间分辨率要求较低[47]ꎮ目前ꎬ主要通过计算机视觉方法或植被指数建模反演等手段获取作物的冠层覆盖度信息ꎮ然而ꎬ这些方法存在一定的局限性ꎮ今后还需寻找一种普遍适用的方法ꎬ以实现对不同作物冠层覆盖度的精确获取ꎬ从而完善作物冠层覆盖度提取技术[48]ꎮ571㊀第4期㊀㊀㊀㊀㊀㊀曾世伟ꎬ等:基于无人机遥感的作物表型参数获取和应用研究进展2.3㊀作物叶面积指数叶面积指数(LAI)是指单位面积内作物叶片面积的总和ꎮLAI是表征作物光合作用㊁呼吸作用以及蒸腾作用的重要指示因子ꎬ也是评价作物长势和产量的重要依据ꎬ因此快速且高效地获取作物LAI对于估测作物产量具有重要意义[36]ꎮ陶惠林等[35]利用无人机搭载高光谱仪获取高光谱图像ꎬ通过线性回归和指数回归挑选出最佳估测参数NDVIˑSR作为模型的输入特征ꎬ然后采用多元线性回归构建模型ꎬLAI估测精度较高(建模和验证的R2㊁RMSE㊁NRMSE分别为0.6788㊁0.69㊁19.79%及0.8462㊁0.47㊁16.04%)ꎮ杨雨薇等[49]使用无人机遥感平台获取作物的高光谱影像ꎬ对光谱数据预处理后计算出植被指数NDVIꎬ然后构建出三种类型的模型 线性回归模型㊁物理模型㊁回归模型与物理模型相结合的半经验模型ꎬ用来反演作物LAIꎬ其中半经验模型的反演精度最好(R2=0.89)ꎮ孙诗睿等[50]利用无人机搭载多光谱传感器获取冬小麦多光谱影像ꎬ通过多个植被指数构建随机森林模型对冬小麦的LAI进行反演ꎬ反演值与真实值之间的R2=0.822ꎬRMSE=1.218ꎮ李剑剑等[51]利用无人机遥感平台获取地表作物的高光谱数据ꎬ然后结合PROSPECT叶片光学模型和SAIL冠层二向性反射模型相耦合后生成的模型(PROSPECT+SAIL)来反演作物的LAIꎬR2=0.82ꎬRMSE=0.43m2/m2)ꎮ傅银贞等[52]利用IRS-P6(LISS-Ⅲ)获取多光谱数据并计算出DVI㊁EVI2㊁MSAVI㊁NDVI㊁RDVI㊁RVI㊁TNDVI共7种植被指数ꎬ建立了LAI与各植被指数的统计模型ꎬ其中NDVI㊁RDVI㊁TNDVI反演LAI的效果较好ꎬ决定系数R2均能够达到0.76以上ꎮ2.4㊀作物水分胁迫测量作物水分胁迫对于发展节水灌溉农业及提高水分利用效率有重要意义[53]ꎮ气孔导度和叶片水势是表征作物水分胁迫的重要指标ꎮ冠层温度可反映气孔导度ꎬ而作物水分胁迫指数(CW ̄SI)与气孔导度相关ꎬ因此可以基于冠层温度测量监测作物水分胁迫状况[54]ꎮ张智韬等[55]基于无人机搭载RGB相机和近红外相机采集的图像ꎬ采用Otsu-EXG-Kmeans算法对玉米冠层温度进行提取ꎬ用户精度为95.9%ꎬ精度较高ꎬ提取的冠层温度与实测温度更接近(r=0.788)ꎬ将冠层温度代入水分胁迫公式计算出CWSIꎬCWSI与土壤含水率的相关性较高(r=-0.738)ꎮBellvert等[56]基于无人机搭载热成像仪获取热成像图片ꎬ得到葡萄的冠层温度ꎬ并计算出相应的CWSIꎬ发现CWSI与叶片水势的相关性较高(R2=0.83)ꎮ除了利用可见光㊁近红外和热红外传感器监测作物水分胁迫的方法外ꎬ利用多光谱㊁高光谱遥感以及多种传感器获取单一或多个波段建立植被指数模型也是常用的方法[57]ꎮ王敬哲等[58]采用无人机搭载高光谱传感器获取影像数据ꎬ经过5种不同的预处理后ꎬ构建了干旱区绿洲农田土壤含水量(SMC)高光谱定量估算模型ꎬ其中通过吸光度(Abs)预处理得到的模型预测精度最好ꎬ其建模集Rc2和RMSE分别为0.84㊁2.16%ꎬ验证集Rp2与RMSE分别为0.91㊁1.71%ꎬ相对分析误差(RPD)为2.41ꎮ张智韬等[54]利用无人机遥感系统获得玉米冠层多光谱正射影像ꎬ并同步采集玉米根域不同深度土壤含水量(SMC)ꎬ通过灰度关联法筛选出对SMC敏感的植被指数ꎬ采用多元线性回归㊁反向传播神经网络(BPNN)㊁支持向量回归(SVR)等机器学习方法构建不同生育时期的敏感植被指数与SMC的关系模型ꎬ结果表明SVR模型在各生育期的建模与预测精度均最优(建模集R2=0.851ꎬRMSE=0.7%ꎬNRMSE=8.17%ꎻ验证集R2=0.875ꎬRMSE=0.7%ꎬNRMSE=8.32%)ꎮ2.5㊀作物生物量生物量是作物产量形成的重要基础ꎬ准确快速获取作物生物量对预测其产量意义重大[59]ꎬ同时ꎬ生物量的定量估算也可为碳循环研究提供重要参考[48]ꎮ根据传感器收集到的数据信息ꎬ将能够反映作物生物量的不同特征数据相结合ꎬ构建更有效且不相关的特征ꎬ然后将该特征输入到回归模型中ꎬ能够提高作物生物量估测的准确性[60]ꎮ万亮等[41]利用无人机同时搭载数码相机和多光谱相机获取研究区域的可见光和多光谱成像数据ꎬ将671山东农业科学㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第56卷㊀可见光图像的颜色特征和纹理特征与多光谱图像的光谱反射率融合后输入到随机森林回归模型(RFR)中ꎬ有效改善了穗生物量的评估精度(R2=0.84ꎬrRMSE=8.68%)ꎮWang等[61]评估了高光谱和激光雷达数据融合在玉米生物量估算中的应用ꎬ结果表明ꎬ与单独使用LiDAR或高光谱数据相比ꎬ高光谱和LiDAR数据相融合能够更好地估测玉米的生物量(R2=0.883ꎬRMSE=321.092g/m2ꎬRMSECV=337.653g/m2)ꎮ刘畅等[62]结合纹理特征与植被指数构建了一种 图-谱 融合指标ꎬ用该指标构建的生物量模型精度较高(R2=0.81ꎬRMSE=826.02kg hm-2)ꎬ明显高于用单一植被指数(R2=0.69)和单一纹理特征(R2=0.71)构建的生物量模型ꎮ综上所述ꎬ不同的作物具有不同的特征ꎬ即使同一作物在不同的生长条件下也会表现出不同的特征[63]ꎬ这就需要使用不同的传感器来全面收集作物信息ꎬ并筛选出一些与生物量相关性最好的特征ꎬ将其融合后输入到回归模型中ꎬ从而实现精准估测作物生物量和提高估算模型精度ꎮ当研究的作物生物量较大时ꎬ用常规的植被指数来估测生物量往往会受到饱和问题的限制ꎬ导致不能较好地估算作物生物量ꎮ付元元等[64]研究证实ꎬ将波段深度分析和偏最小二乘回归(PLSR)相结合ꎬ能够有效解决作物生物量过大导致的问题ꎬ并提高冬小麦生物量的估算精度ꎬ其中波段深度比(BDR)与PLSR结合的模型的估算精度较好(R2=0.792ꎬRMSE=0.164kg/m2)ꎮ2.6㊀作物产量作物产量关乎国家粮食安全ꎬ早期准确地监测预报作物产量对于后期田间管理及灾害评估等具有重要意义ꎮ通过无人机遥感提取作物产量的常规方法如下:使用无人机搭载多种传感器获取可见光㊁光谱数据ꎬ基于可见光图像提取纹理特征ꎬ根据光谱数据提取特征波段并计算植被指数ꎻ然后将纹理特征㊁植被指数等特征作为模型输入ꎬ使用机器学习算法构建产量估测模型ꎻ最后引入R2和RMSE评价产量估测模型ꎮ模型构建时ꎬ将多种特征变量相结合往往能够改善作物估测模型的精度ꎮElsayed等[65]利用偏最小二乘法将光谱指数㊁温度参数和植株含水量等数据融合ꎬ使得小麦产量的估测效果得到进一步改善(R2=0.97ꎬRMSE=26.48g/m2)ꎮMaim ̄aitijiang等[66]利用RGB信息㊁光谱反射率及温度参数等多模态数据ꎬ基于中间级特征融合的DNN(DNN-F2)方法ꎬ准确估测了大豆产量(R2=0.720ꎬrRMSE=15.9%)ꎮ严海军等[67]使用无人机搭载多光谱相机在苜蓿的分枝期㊁现蕾期和初花期进行遥感监测ꎬ将植被指数与株高组合作为输入变量并采用支持向量回归算法构建模型ꎬ产量估测精度最高(R2=0.90ꎬRMSE=500kg/hm2ꎬNRMSE=14.3%)ꎮ可见ꎬ选用多源数据融合构建模型的效果较好ꎮ另外ꎬ在构建模型时ꎬ使用的算法不同也会影响作物产量估测的精度ꎮ张少华等[68]利用低空无人机遥感平台搭载多光谱相机㊁热红外相机和RGB相机ꎬ同步获取小麦关键生育时期的无人机遥感影像ꎬ并提取光谱反射率㊁热红外温度和数字高程信息ꎬ选取并计算出相应的特征集ꎬ然后利用支持向量回归(SVR)㊁多元线性回归(MLR)㊁随机森林回归(RFR)㊁偏最小二乘回归(PLSR)等机器学习算法建立小麦产量的估测模型ꎬ最终结果表明采用RFR算法建立的模型效果最好(R2=0.724ꎬRMSE=614.72kg/hm2ꎬMAE=478.08kg/hm2)ꎮ申洋洋等[69]采集冬小麦多光谱数据ꎬ选取多光谱相机的5个特征波段计算各生育时期的72个植被指数ꎬ分别通过逐步多元线性回归㊁偏最小二乘回归㊁BP神经网络㊁支持向量机㊁随机森林构建不同生育时期的产量估算模型ꎬ其中基于随机森林算法建立的模型估算效果最优(R2=0.94ꎬRMSE=0.32ꎬRE=9%)ꎮ赵鑫[70]利用多旋翼无人机搭载数码相机拍摄小麦的可见光图像ꎬ经预处理后计算出植被指数和颜色特征ꎬ然后结合多种机器学习算法建立产量估测模型ꎬ其中随机森林算法模型精度最高(R2=0.74)ꎮ作物发育时期也会影响模型精度ꎮ刘昌华等[71]以无人机多光谱影像为基础ꎬ提取冬小麦在几个生长阶段下的冠层多光谱数据并建立产量估算模型ꎬ其中返青期估算效果较差ꎬ拔节期㊁孕穗期㊁扬花期估算效果相近且较好(R2分别为0.93㊁771㊀第4期㊀㊀㊀㊀㊀㊀曾世伟ꎬ等:基于无人机遥感的作物表型参数获取和应用研究进展0.96㊁0.94)ꎮ申洋洋等[69]以冬小麦拔节期㊁孕穗期㊁抽穗期㊁灌浆期㊁成熟期的无人机多光谱影像为数据源ꎬ利用随机森林算法构建模型的R2㊁RMSE㊁RE分别为拔节期0.92㊁0.35㊁11%ꎬ孕穗期0.93㊁0.33㊁10%ꎬ抽穗期0.94㊁0.32㊁9%ꎬ灌浆期0.92㊁0.36㊁9%ꎬ成熟期0.77㊁0.67㊁33%ꎬ可见ꎬ抽穗期的估算效果最好ꎬ拔节期㊁孕穗期㊁灌浆期估算效果接近㊁也较好ꎬ成熟期的估算精度最差ꎮ3㊀总结与展望本文综述了基于无人机遥感开展作物表型参数研究的过程和方法㊁无人机遥感平台及其在作物表型参数估测上的应用研究进展ꎮ无人机遥感平台凭借着工作效率高㊁灵活性好㊁成本低㊁分辨率高㊁适用于复杂野外环境等优点ꎬ成为研究作物表型参数的有利工具ꎬ为农业精细化管理及农田生态系统建模提供了技术支持ꎮ由于外界环境和作物自身因素影响以及研究方法的局限性ꎬ目前多数研究构建的表型参数模型的精确性㊁鲁棒性㊁泛化性等性能较差ꎬ缺乏能够较好估测不同作物类型的表型参数的通用模型和方法ꎬ而且目前无人机遥感监测表型参数信息的研究多集中于玉米㊁小麦㊁水稻㊁大豆等少数作物ꎬ其他作物类型鲜有研究ꎬ因此该技术研究在深度与广度上还有很大的发展空间ꎮ参㊀考㊀文㊀献:[1]㊀仇瑞承ꎬ魏爽ꎬ张漫ꎬ等.作物表型组学测量方法综述[J].中国农业文摘-农业工程ꎬ2019ꎬ31(1):23-36ꎬ55. 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Agricultural Sciences in China2010, 9(9): 1251-1262September 2010Received 30 October, 2009 Accepted 16 April, 2010Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of ChinaLIU Zhi-zhai 1, 2, GUO Rong-hua 2, 3, ZHAO Jiu-ran 4, CAI Yi-lin 1, W ANG Feng-ge 4, CAO Mo-ju 3, W ANG Rong-huan 2, 4, SHI Yun-su 2, SONG Yan-chun 2, WANG Tian-yu 2 and LI Y u 21Maize Research Institute, Southwest University, Chongqing 400716, P.R.China2Institue of Crop Sciences/National Key Facility for Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences,Beijing 100081, P.R.China3Maize Research Institute, Sichuan Agricultural University, Ya’an 625014, P.R.China4Maize Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100089, P.R.ChinaAbstractUnderstanding genetic diversity and population structure of landraces is important in utilization of these germplasm in breeding programs. In the present study, a total of 143 core maize landraces from the South Maize Region (SR) of China,which can represent the general profile of the genetic diversity in the landraces germplasm of SR, were genotyped by 54DNA microsatellite markers. Totally, 517 alleles (ranging from 4 to 22) were detected among these landraces, with an average of 9.57 alleles per locus. The total gene diversity of these core landraces was 0.61, suggesting a rather higher level of genetic diversity. Analysis of population structure based on Bayesian method obtained the samilar result as the phylogeny neighbor-joining (NJ) method. The results indicated that the whole set of 143 core landraces could be clustered into two distinct groups. All landraces from Guangdong, Hainan, and 15 landraces from Jiangxi were clustered into group 1, while those from the other regions of SR formed the group 2. The results from the analysis of genetic diversity showed that both of groups possessed a similar gene diversity, but group 1 possessed relatively lower mean alleles per locus (6.63) and distinct alleles (91) than group 2 (7.94 and 110, respectively). The relatively high richness of total alleles and distinct alleles preserved in the core landraces from SR suggested that all these germplasm could be useful resources in germplasm enhancement and maize breeding in China.Key words :maize, core landraces, genetic diversity, population structureINTRODUCTIONMaize has been grown in China for nearly 500 years since its first introduction into this second biggest pro-duction country in the world. Currently, there are six different maize growing regions throughout the coun-try according to the ecological conditions and farming systems, including three major production regions,i.e., the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, and the Southwest MaizeRegion, and three minor regions, i.e., the South Maize Region, the Northwest Maize Region, and the Qingzang Plateau Maize Region. The South Maize Region (SR)is specific because of its importance in origin of Chi-nese maize. It is hypothesized that Chinese maize is introduced mainly from two routes. One is called the land way in which maize was first brought to Tibet from India, then to Sichuan Province in southwestern China. The other way is that maize dispersed via the oceans, first shipped to the coastal areas of southeast China by boats, and then spread all round the country1252LIU Zhi-zhai et al.(Xu 2001; Zhou 2000). SR contains all of the coastal provinces and regions lie in southeastern China.In the long-term cultivation history of maize in south-ern China, numerous landraces have been formed, in which a great amount of genetic variation was observed (Li 1998). Similar to the hybrid swapping in Europe (Reif et al. 2005a), the maize landraces have been al-most replaced by hybrids since the 1950s in China (Li 1998). However, some landraces with good adapta-tions and yield performances are still grown in a few mountainous areas of this region (Liu et al.1999). Through a great effort of collection since the 1950s, 13521 accessions of maize landraces have been cur-rently preserved in China National Genebank (CNG), and a core collection of these landraces was established (Li et al. 2004). In this core collection, a total of 143 maize landrace accessions were collected from the South Maize Region (SR) (Table 1).Since simple sequence repeat ( SSR ) markers were firstly used in human genetics (Litt and Luty 1989), it now has become one of the most widely used markers in the related researches in crops (Melchinger et al. 1998; Enoki et al. 2005), especially in the molecular characterization of genetic resources, e.g., soybean [Glycine max (L.) Merr] (Xie et al. 2005), rice (Orya sativa L.) (Garris et al. 2005), and wheat (Triticum aestivum) (Chao et al. 2007). In maize (Zea mays L.), numerous studies focusing on the genetic diversity and population structure of landraces and inbred lines in many countries and regions worldwide have been pub-lished (Liu et al. 2003; Vegouroux et al. 2005; Reif et al. 2006; Wang et al. 2008). These activities of documenting genetic diversity and population structure of maize genetic resources have facilitated the under-standing of genetic bases of maize landraces, the utili-zation of these resources, and the mining of favorable alleles from landraces. Although some studies on ge-netic diversity of Chinese maize inbred lines were con-ducted (Yu et al. 2007; Wang et al. 2008), the general profile of genetic diversity in Chinese maize landraces is scarce. Especially, there are not any reports on ge-netic diversity of the maize landraces collected from SR, a possibly earliest maize growing area in China. In this paper, a total of 143 landraces from SR listed in the core collection of CNG were genotyped by using SSR markers, with the aim of revealing genetic diver-sity of the landraces from SR (Table 2) of China and examining genetic relationships and population struc-ture of these landraces.MATERIALS AND METHODSPlant materials and DNA extractionTotally, 143 landraces from SR which are listed in the core collection of CNG established by sequential strati-fication method (Liu et al. 2004) were used in the present study. Detailed information of all these landrace accessions is listed in Table 1. For each landrace, DNA sample was extracted by a CTAB method (Saghi-Maroof et al. 1984) from a bulk pool constructed by an equal-amount of leaves materials sampled from 15 random-chosen plants of each landrace according to the proce-dure of Reif et al. (2005b).SSR genotypingA total of 54 simple sequence repeat (SSR) markers covering the entire maize genome were screened to fin-gerprint all of the 143 core landrace accessions (Table 3). 5´ end of the left primer of each locus was tailed by an M13 sequence of 5´-CACGACGTTGTAAAACGAC-3´. PCR amplification was performed in a 15 L reac-tion containing 80 ng of template DNA, 7.5 mmol L-1 of each of the four dNTPs, 1×Taq polymerase buffer, 1.5 mmol L-1 MgCl2, 1 U Taq polymerase (Tiangen Biotech Co. Ltd., Beijing, China), 1.2 mol L-1 of forward primer and universal fluorescent labeled M13 primer, and 0.3 mol L-1 of M13 sequence tailed reverse primer (Schuelke 2000). The amplification was carried out in a 96-well DNA thermal cycler (GeneAmp PCR System 9700, Applied Biosystem, USA). PCR products were size-separated on an ABI Prism 3730XL DNA sequencer (HitachiHigh-Technologies Corporation, Tokyo, Japan) via the software packages of GENEMAPPER and GeneMarker ver. 6 (SoftGenetics, USA).Data analysesAverage number of alleles per locus and average num-ber of group-specific alleles per locus were identifiedAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1253Table 1 The detailed information about the landraces used in the present studyPGS revealed by Structure1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysis140-150tian 00120005AnH-06Jingde Anhui 0.0060.994Group 2170tian00120006AnH-07Jingde Anhui 0.0050.995Group 2Zixihuangyumi00120007AnH-08Zixi Anhui 0.0020.998Group 2Zixibaihuangzayumi 00120008AnH-09Zixi Anhui 0.0030.997Group 2Baiyulu 00120020AnH-10Yuexi Anhui 0.0060.994Group 2Wuhuazi 00120021AnH-11Yuexi Anhui 0.0030.997Group 2Tongbai 00120035AnH-12Tongling Anhui 0.0060.994Group 2Yangyulu 00120036AnH-13Yuexi Anhui 0.0040.996Group 2Huangli 00120037AnH-14Tunxi Anhui 0.0410.959Group 2Baiyumi 00120038AnH-15Tunxi Anhui 0.0030.997Group 2Dapigu00120039AnH-16Tunxi Anhui 0.0350.965Group 2150tianbaiyumi 00120040AnH-17Xiuning Anhui 0.0020.998Group 2Xiuning60tian 00120042AnH-18Xiuning Anhui 0.0040.996Group 2Wubaogu 00120044AnH-19ShitaiAnhui 0.0020.998Group 2Kuyumi00130001FuJ-01Shanghang Fujian 0.0050.995Group 2Zhongdouyumi 00130003FuJ-02Shanghang Fujian 0.0380.962Group 2Baixinyumi 00130004FuJ-03Liancheng Fujian 0.0040.996Group 2Hongxinyumi 00130005FuJ-04Liancheng Fujian 0.0340.966Group 2Baibaogu 00130008FuJ-05Changding Fujian 0.0030.997Group 2Huangyumi 00130011FuJ-06Jiangyang Fujian 0.0020.998Group 2Huabaomi 00130013FuJ-07Shaowu Fujian 0.0020.998Group 2Huangbaomi 00130014FuJ-08Songxi Fujian 0.0020.998Group 2Huangyumi 00130016FuJ-09Wuyishan Fujian 0.0460.954Group 2Huabaogu 00130019FuJ-10Jian’ou Fujian 0.0060.994Group 2Huangyumi 00130024FuJ-11Guangze Fujian 0.0010.999Group 2Huayumi 00130025FuJ-12Nanping Fujian 0.0040.996Group 2Huangyumi 00130026FuJ-13Nanping Fujian 0.0110.989Group 2Hongbaosu 00130027FuJ-14Longyan Fujian 0.0160.984Group 2Huangfansu 00130029FuJ-15Loangyan Fujian 0.0020.998Group 2Huangbaosu 00130031FuJ-16Zhangping Fujian 0.0060.994Group 2Huangfansu 00130033FuJ-17Zhangping Fujian0.0040.996Group 2Baolieyumi 00190001GuangD-01Guangzhou Guangdong 0.9890.011Group 1Nuomibao (I)00190005GuangD-02Shixing Guangdong 0.9740.026Group 1Nuomibao (II)00190006GuangD-03Shixing Guangdong 0.9790.021Group 1Zasehuabao 00190010GuangD-04Lechang Guangdong 0.9970.003Group 1Zihongmi 00190013GuangD-05Lechang Guangdong 0.9880.012Group 1Jiufengyumi 00190015GuangD-06Lechang Guangdong 0.9950.005Group 1Huangbaosu 00190029GuangD-07MeiGuangdong 0.9970.003Group 1Bailibao 00190032GuangD-08Xingning Guangdong 0.9980.002Group 1Nuobao00190038GuangD-09Xingning Guangdong 0.9980.002Group 1Jinlanghuang 00190048GuangD-10Jiangcheng Guangdong 0.9960.004Group 1Baimizhenzhusu 00190050GuangD-11Yangdong Guangdong 0.9940.006Group 1Huangmizhenzhusu 00190052GuangD-12Yangdong Guangdong 0.9930.007Group 1Baizhenzhu 00190061GuangD-13Yangdong Guangdong 0.9970.003Group 1Baiyumi 00190066GuangD-14Wuchuan Guangdong 0.9880.012Group 1Bendibai 00190067GuangD-15Suixi Guangdong 0.9980.002Group 1Shigubaisu 00190068GuangD-16Gaozhou Guangdong 0.9960.004Group 1Zhenzhusu 00190069GuangD-17Xinyi Guangdong 0.9960.004Group 1Nianyaxixinbai 00190070GuangD-18Huazhou Guangdong 0.9960.004Group 1Huangbaosu 00190074GuangD-19Xinxing Guangdong 0.9950.005Group 1Huangmisu 00190076GuangD-20Luoding Guangdong 0.940.060Group 1Huangmi’ai 00190078GuangD-21Luoding Guangdong 0.9980.002Group 1Bayuemai 00190084GuangD-22Liannan Guangdong 0.9910.009Group 1Baiyumi 00300001HaiN-01Haikou Hainan 0.9960.004Group 1Baiyumi 00300003HaiN-02Sanya Hainan 0.9970.003Group 1Hongyumi 00300004HaiN-03Sanya Hainan 0.9980.002Group 1Baiyumi00300011HaiN-04Tongshi Hainan 0.9990.001Group 1Zhenzhuyumi 00300013HaiN-05Tongshi Hainan 0.9980.002Group 1Zhenzhuyumi 00300015HaiN-06Qiongshan Hainan 0.9960.004Group 1Aiyumi 00300016HaiN-07Qiongshan Hainan 0.9960.004Group 1Huangyumi 00300021HaiN-08Qionghai Hainan 0.9970.003Group 1Y umi 00300025HaiN-09Qionghai Hainan 0.9870.013Group 1Accession name Entry code Analyzing code Origin (county/city)Province/Region1254LIU Zhi-zhai et al .Baiyumi00300032HaiN-10Tunchang Hainan 0.9960.004Group 1Huangyumi 00300051HaiN-11Baisha Hainan 0.9980.002Group 1Baihuangyumi 00300055HaiN-12BaishaHainan 0.9970.003Group 1Machihuangyumi 00300069HaiN-13Changjiang Hainan 0.9900.010Group 1Hongyumi00300073HaiN-14Dongfang Hainan 0.9980.002Group 1Xiaohonghuayumi 00300087HaiN-15Lingshui Hainan 0.9980.002Group 1Baiyumi00300095HaiN-16Qiongzhong Hainan 0.9950.005Group 1Y umi (Baimai)00300101HaiN-17Qiongzhong Hainan 0.9980.002Group 1Y umi (Xuemai)00300103HaiN-18Qiongzhong Hainan 0.9990.001Group 1Huangmaya 00100008JiangS-10Rugao Jiangsu 0.0040.996Group 2Bainian00100012JiangS-11Rugao Jiangsu 0.0080.992Group 2Bayebaiyumi 00100016JiangS-12Rudong Jiangsu 0.0040.996Group 2Chengtuohuang 00100021JiangS-13Qidong Jiangsu 0.0050.995Group 2Xuehuanuo 00100024JiangS-14Qidong Jiangsu 0.0020.998Group 2Laobaiyumi 00100032JiangS-15Qidong Jiangsu 0.0050.995Group 2Laobaiyumi 00100033JiangS-16Qidong Jiangsu 0.0010.999Group 2Huangwuye’er 00100035JiangS-17Hai’an Jiangsu 0.0030.997Group 2Xiangchuanhuang 00100047JiangS-18Nantong Jiangsu 0.0060.994Group 2Huangyingzi 00100094JiangS-19Xinghua Jiangsu 0.0040.996Group 2Xiaojinhuang 00100096JiangS-20Yangzhou Jiangsu 0.0010.999Group 2Liushizi00100106JiangS-21Dongtai Jiangsu 0.0030.997Group 2Kangnandabaizi 00100108JiangS-22Dongtai Jiangsu 0.0020.998Group 2Shanyumi 00140020JiangX-01Dexing Jiangxi 0.9970.003Group 1Y umi00140024JiangX-02Dexing Jiangxi 0.9970.003Group 1Tianhongyumi 00140027JiangX-03Yushan Jiangxi 0.9910.009Group 1Hongganshanyumi 00140028JiangX-04Yushan Jiangxi 0.9980.002Group 1Zaoshuyumi 00140032JiangX-05Qianshan Jiangxi 0.9970.003Group 1Y umi 00140034JiangX-06Wannian Jiangxi 0.9970.003Group 1Y umi 00140038JiangX-07De’an Jiangxi 0.9940.006Group 1Y umi00140045JiangX-08Wuning Jiangxi 0.9740.026Group 1Chihongyumi 00140049JiangX-09Wanzai Jiangxi 0.9920.008Group 1Y umi 00140052JiangX-10Wanzai Jiangxi 0.9930.007Group 1Huayumi 00140060JiangX-11Jing’an Jiangxi 0.9970.003Group 1Baiyumi 00140065JiangX-12Pingxiang Jiangxi 0.9940.006Group 1Huangyumi00140066JiangX-13Pingxiang Jiangxi 0.9680.032Group 1Nuobaosuhuang 00140068JiangX-14Ruijin Jiangxi 0.9950.005Group 1Huangyumi 00140072JiangX-15Xinfeng Jiangxi 0.9960.004Group 1Wuningyumi 00140002JiangX-16Jiujiang Jiangxi 0.0590.941Group 2Tianyumi 00140005JiangX-17Shangrao Jiangxi 0.0020.998Group 2Y umi 00140006JiangX-18Shangrao Jiangxi 0.0310.969Group 2Baiyiumi 00140012JiangX-19Maoyuan Jiangxi 0.0060.994Group 260riyumi 00140016JiangX-20Maoyuan Jiangxi 0.0020.998Group 2Shanyumi 00140019JiangX-21Dexing Jiangxi 0.0050.995Group 2Laorenya 00090002ShangH-01Chongming Shanghai 0.0050.995Group 2Jinmeihuang 00090004ShangH-02Chongming Shanghai 0.0020.998Group 2Zaobaiyumi 00090006ShangH-03Chongming Shanghai 0.0020.998Group 2Chengtuohuang 00090007ShangH-04Chongming Shanghai 0.0780.922Group 2Benyumi (Huang)00090008ShangH-05Shangshi Shanghai 0.0020.998Group 2Bendiyumi 00090010ShangH-06Shangshi Shanghai 0.0040.996Group 2Baigengyumi 00090011ShangH-07Jiading Shanghai 0.0020.998Group 2Huangnuoyumi 00090012ShangH-08Jiading Shanghai 0.0040.996Group 2Huangdubaiyumi 00090013ShangH-09Jiading Shanghai 0.0440.956Group 2Bainuoyumi 00090014ShangH-10Chuansha Shanghai 0.0010.999Group 2Laorenya 00090015ShangH-11Shangshi Shanghai 0.0100.990Group 2Xiaojinhuang 00090016ShangH-12Shangshi Shanghai 0.0050.995Group 2Gengbaidayumi 00090017ShangH-13Shangshi Shanghai 0.0020.998Group 2Nongmeiyihao 00090018ShangH-14Shangshi Shanghai 0.0540.946Group 2Chuanshazinuo 00090020ShangH-15Chuansha Shanghai 0.0550.945Group 2Baoanshanyumi 00110004ZheJ-01Jiangshan Zhejiang 0.0130.987Group 2Changtaixizi 00110005ZheJ-02Jiangshan Zhejiang 0.0020.998Group 2Shanyumibaizi 00110007ZheJ-03Jiangshan Zhejiang 0.0020.998Group 2Kaihuajinyinbao 00110017ZheJ-04Kaihua Zhejiang 0.0100.990Group 2Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group1 Group2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/RegoinAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1255Liputianzi00110038ZheJ-05Jinhua Zhejiang 0.0020.998Group 2Jinhuaqiuyumi 00110040ZheJ-06Jinhua Zhejiang 0.0050.995Group 2Pujiang80ri 00110069ZheJ-07Pujiang Zhejiang 0.0210.979Group 2Dalihuang 00110076ZheJ-08Yongkang Zhejiang 0.0140.986Group 2Ziyumi00110077ZheJ-09Yongkang Zhejiang 0.0020.998Group 2Baiyanhandipinzhong 00110078ZheJ-10Yongkang Zhejiang 0.0030.997Group 2Duosuiyumi00110081ZheJ-11Wuyi Zhejiang 0.0020.998Group 2Chun’an80huang 00110084ZheJ-12Chun’an Zhejiang 0.0020.998Group 2120ribaiyumi 00110090ZheJ-13Chun’an Zhejiang 0.0020.998Group 2Lin’anliugu 00110111ZheJ-14Lin’an Zhejiang 0.0030.997Group 2Qianhuangyumi00110114ZheJ-15Lin’an Zhejiang 0.0030.997Group 2Fenshuishuitianyumi 00110118ZheJ-16Tonglu Zhejiang 0.0410.959Group 2Kuihualiugu 00110119ZheJ-17Tonglu Zhejiang 0.0030.997Group 2Danbaihuang 00110122ZheJ-18Tonglu Zhejiang 0.0020.998Group 2Hongxinma 00110124ZheJ-19Jiande Zhejiang 0.0030.997Group 2Shanyumi 00110136ZheJ-20Suichang Zhejiang 0.0030.997Group 2Bai60ri 00110143ZheJ-21Lishui Zhejiang 0.0050.995Group 2Zeibutou 00110195ZheJ-22Xianju Zhejiang 0.0020.998Group 2Kelilao00110197ZheJ-23Pan’an Zhejiang 0.0600.940Group 21)The figures refered to the proportion of membership that each landrace possessed.Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/Regoin Table 2 Construction of two phylogenetic groups (SSR-clustered groups) and their correlation with geographical locationsGeographical location SSR-clustered groupChi-square testGroup 1Group 2Total Guangdong 2222 χ2 = 124.89Hainan 1818P < 0.0001Jiangxi 15621Anhui 1414Fujian 1717Jiangsu 1313Shanghai 1515Zhejiang 2323Total5588143by the software of Excel MicroSatellite toolkit (Park 2001). Average number of alleles per locus was calcu-lated by the formula rAA rj j¦1, with the standarddeviation of1)()(12¦ r A AA rj jV , where A j was thenumber of distinct alleles at locus j , and r was the num-ber of loci (Park 2001).Unbiased gene diversity also known as expected heterozygosity, observed heterozygosity for each lo-cus and average gene diversity across the 54 SSR loci,as well as model-based groupings inferred by Struc-ture ver. 2.2, were calculated by the softwarePowerMarker ver.3.25 (Liu et al . 2005). Unbiased gene diversity for each locus was calculated by˅˄¦ 2ˆ1122ˆi x n n h , where 2ˆˆ2ˆ2¦¦z ji ijij i X X x ,and ij X ˆwas the frequency of genotype A i A jin the sample, and n was the number of individuals sampled.The average gene diversity across 54 loci was cal-culated as described by Nei (1987) as follows:rh H rj j ¦1ˆ, with the variance ,whereThe average observed heterozygosity across the en-tire loci was calculated as described by (Hedrick 1983)as follows: r jrj obsobs n h h ¦1, with the standard deviationrn h obs obsobs 1V1256LIU Zhi-zhai et al.Phylogenetic analysis and population genetic structureRelationships among all of the 143 accessions collected from SR were evaluated by using the unweighted pair group method with neighbor-joining (NJ) based on the log transformation of the proportion of shared alleles distance (InSPAD) via PowerMarker ver. 3.25 (FukunagaTable 3 The PIC of each locus and the number of alleles detected by 54 SSRsLocus Bin Repeat motif PIC No. of alleles Description 2)bnlg1007y51) 1.02AG0.7815Probe siteumc1122 1.06GGT0.639Probe siteumc1147y41) 1.07CA0.2615Probe sitephi961001) 2.00ACCT0.298Probe siteumc1185 2.03GC0.7215ole1 (oleosin 1)phi127 2.08AGAC0.577Probe siteumc1736y21) 2.09GCA T0.677Probe sitephi453121 3.01ACC0.7111Probe sitephi374118 3.03ACC0.477Probe sitephi053k21) 3.05A TAC0.7910Probe sitenc004 4.03AG0.4812adh2 (alcohol dehydrogenase 2)bnlg490y41) 4.04T A0.5217Probe sitephi079 4.05AGATG0.495gpc1(glyceraldehyde-3-phosphate dehydrogenase 1) bnlg1784 4.07AG0.6210Probe siteumc1574 4.09GCC0.719sbp2 (SBP-domain protein 2)umc1940y51) 4.09GCA0.4713Probe siteumc1050 4.11AA T0.7810cat3 (catalase 3)nc130 5.00AGC0.5610Probe siteumc2112y31) 5.02GA0.7014Probe sitephi109188 5.03AAAG0.719Probe siteumc1860 5.04A T0.325Probe sitephi085 5.07AACGC0.537gln4 (glutamine synthetase 4)phi331888 5.07AAG0.5811Probe siteumc1153 5.09TCA0.7310Probe sitephi075 6.00CT0.758fdx1 (ferredoxin 1)bnlg249k21) 6.01AG0.7314Probe sitephi389203 6.03AGC0.416Probe sitephi299852y21) 6.07AGC0.7112Probe siteumc1545y21)7.00AAGA0.7610hsp3(heat shock protein 3)phi1127.01AG0.5310o2 (opaque endosperm 2)phi4207018.00CCG0.469Probe siteumc13598.00TC0.7814Probe siteumc11398.01GAC0.479Probe siteumc13048.02TCGA0.335Probe sitephi1158.03A TAC0.465act1(actin1)umc22128.05ACG0.455Probe siteumc11218.05AGAT0.484Probe sitephi0808.08AGGAG0.646gst1 (glutathione-S-transferase 1)phi233376y11)8.09CCG0.598Probe sitebnlg12729.00AG0.8922Probe siteumc20849.01CTAG0.498Probe sitebnlg1520k11)9.01AG0.5913Probe sitephi0659.03CACCT0.519pep1(phosphoenolpyruvate carboxylase 1)umc1492y131)9.04GCT0.2514Probe siteumc1231k41)9.05GA0.2210Probe sitephi1084119.06AGCT0.495Probe sitephi4488809.06AAG0.7610Probe siteumc16759.07CGCC0.677Probe sitephi041y61)10.00AGCC0.417Probe siteumc1432y61)10.02AG0.7512Probe siteumc136710.03CGA0.6410Probe siteumc201610.03ACAT0.517pao1 (polyamine oxidase 1)phi06210.04ACG0.337mgs1 (male-gametophyte specific 1)phi07110.04GGA0.515hsp90 (heat shock protein, 90 kDa)1) These primers were provided by Beijing Academy of Agricultural and Forestry Sciences (Beijing, China).2) Searched from Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China1257et al. 2005). The unrooted phylogenetic tree was finally schematized with the software MEGA (molecular evolu-tionary genetics analysis) ver. 3.1 (Kumar et al. 2004). Additionally, a chi-square test was used to reveal the correlation between the geographical origins and SSR-clustered groups through FREQ procedure implemented in SAS ver. 9.0 (2002, SAS Institute, Inc.).In order to reveal the population genetic structure (PGS) of 143 landrace accessions, a Bayesian approach was firstly applied to determine the number of groups (K) that these materials should be assigned by the soft-ware BAPS (Bayesian Analysis of Population Structure) ver.5.1. By using BAPS, a fixed-K clustering proce-dure was applied, and with each separate K, the num-ber of runs was set to 100, and the value of log (mL) was averaged to determine the appropriate K value (Corander et al. 2003; Corander and Tang 2007). Since the number of groups were determined, a model-based clustering analysis was used to assign all of the acces-sions into the corresponding groups by an admixture model and a correlated allele frequency via software Structure ver.2.2 (Pritchard et al. 2000; Falush et al. 2007), and for the given K value determined by BAPS, three independent runs were carried out by setting both the burn-in period and replication number 100000. The threshold probability assigned individuals into groupswas set by 0.8 (Liu et al. 2003). The PGS result carried out by Structure was visualized via Distruct program ver. 1.1 (Rosenberg 2004).RESULTSGenetic diversityA total of 517 alleles were detected by the whole set of54 SSRs covering the entire maize genome through all of the 143 maize landraces, with an average of 9.57 alleles per locus and ranged from 4 (umc1121) to 22 (bnlg1272) (Table 3). Among all the alleles detected, the number of distinct alleles accounted for 132 (25.53%), with an av-erage of 2.44 alleles per locus. The distinct alleles dif-fered significantly among the landraces from different provinces/regions, and the landraces from Guangdong, Fujian, Zhejiang, and Shanghai possessed more distinct alleles than those from the other provinces/regions, while those from southern Anhui possessed the lowest distinct alleles, only counting for 3.28% of the total (Table 4).Table 4 The genetic diversity within eight provinces/regions and groups revealed by 54 SSRsProvince/Region Sample size Allele no.1)Distinct allele no.Gene diversity (expected heterozygosity)Observed heterozygosity Anhui14 4.28 (4.19) 69 (72.4)0.51 (0.54)0.58 (0.58)Fujian17 4.93 (4.58 80 (79.3)0.56 (0.60)0.63 (0.62)Guangdong22 5.48 (4.67) 88 (80.4)0.57 (0.59)0.59 (0.58)Hainan18 4.65 (4.26) 79 (75.9)0.53 (0.57)0.55 (0.59)Jiangsu13 4.24 700.500.55Jiangxi21 4.96 (4.35) 72 (68.7)0.56 (0.60)0.68 (0.68)Shanghai15 5.07 (4.89) 90 (91.4)0.55 (0.60)0.55 (0.55)Zhejiang23 5.04 (4.24) 85 (74)0.53 (0.550.60 (0.61)Total/average1439.571320.610.60GroupGroup 155 6.63 (6.40) 91 (89.5)0.57 (0.58)0.62 (0.62)Group 2887.94 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Total/Average1439.571320.610.60Provinces/Regions within a groupGroup 1Total55 6.69 (6.40) 910.57 (0.58)0.62 (0.62)Guangdong22 5.48 (4.99) 86 (90.1)0.57 (0.60)0.59 (0.58)Hainan18 4.65 (4.38) 79 (73.9)0.53 (0.56)0.55 (0.59)Jiangxi15 4.30 680.540.69Group 2Total887.97 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Anhui14 4.28 (3.22) 69 (63.2)0.51 (0.54)0.58 (0.57)Fujian17 4.93 (3.58) 78 (76.6)0.56 (0.60)0.63 (0.61)Jiangsu13 4.24 (3.22) 71 (64.3)0.50 (0.54)0.55 (0.54)Jiangxi6 3.07 520.460.65Shanghai15 5.07 (3.20) 91 (84.1)0.55 (0.60)0.55 (0.54)Zhejiang23 5.04 (3.20) 83 (61.7)0.53 (0.54)0.60 (0.58)1258LIU Zhi-zhai et al.Among the 54 loci used in the study, 16 (or 29.63%) were dinucleotide repeat SSRs, which were defined as type class I-I, the other 38 loci were SSRs with a longer repeat motifs, and two with unknown repeat motifs, all these 38 loci were defined as the class of I-II. In addition, 15 were located within certain functional genes (defined as class II-I) and the rest were defined as class II-II. The results of comparison indicated that the av-erage number of alleles per locus captured by class I-I and II-II were 12.88 and 10.05, respectively, which were significantly higher than that by type I-II and II-I (8.18 and 8.38, respectively). The gene diversity re-vealed by class I-I (0.63) and II-I (0.63) were some-what higher than by class I-II (0.60) and II-II (0.60) (Table 5).Genetic relationships of the core landraces Overall, 143 landraces were clustered into two groups by using neighbor-joining (NJ) method based on InSPAD. All the landraces from provinces of Guangdong and Hainan and 15 of 21 from Jiangxi were clustered together to form group 1, and the other 88 landraces from the other provinces/regions formed group 2 (Fig.-B). The geographical origins of all these 143 landraces with the clustering results were schematized in Fig.-D. Revealed by the chi-square test, the phylogenetic results (SSR-clustered groups) of all the 143 landraces from provinces/regions showed a significant correlation with their geographical origin (χ2=124.89, P<0.0001, Table 2).Revealed by the phylogenetic analysis based on the InSPAD, the minimum distance was observed as 0.1671 between two landraces, i.e., Tianhongyumi (JiangX-03) and Hongganshanyumi (JiangX-04) collected from Jiangxi Province, and the maximum was between two landraces of Huangbaosu (FuJ-16) and Hongyumi (HaiN-14) collected from provinces of Fujian and Hainan, respectively, with the distance of 1.3863 (data not shown). Two landraces (JiangX-01 and JiangX-21) collected from the same location of Dexing County (Table 1) possessing the same names as Shanyumi were separated to different groups, i.e., JiangX-01 to group1, while JiangX-21 to group 2 (Table 1). Besides, JiangX-01 and JiangX-21 showed a rather distant distance of 0.9808 (data not shown). These results indicated that JiangX-01 and JiangX-21 possibly had different ances-tral origins.Population structureA Bayesian method was used to detect the number of groups (K value) of the whole set of landraces from SR with a fixed-K clustering procedure implemented in BAPS software ver. 5.1. The result showed that all of the 143 landraces could also be assigned into two groups (Fig.-A). Then, a model-based clustering method was applied to carry out the PGS of all the landraces via Structure ver. 2.2 by setting K=2. This method as-signed individuals to groups based on the membership probability, thus the threshold probability 0.80 was set for the individuals’ assignment (Liu et al. 2003). Accordingly, all of the 143 landraces were divided into two distinct model-based groups (Fig.-C). The landraces from Guangdong, Hainan, and 15 landraces from Jiangxi formed one group, while the rest 6 landraces from the marginal countries of northern Jiangxi and those from the other provinces formed an-other group (Table 1, Fig.-D). The PGS revealed by the model-based approach via Structure was perfectly consistent with the relationships resulted from the phy-logenetic analysis via PowerMarker (Table 1).DISCUSSIONThe SR includes eight provinces, i.e., southern Jiangsu and Anhui, Shanghai, Zhejiang, Fujian, Jiangxi, Guangdong, and Hainan (Fig.-C), with the annual maize growing area of about 1 million ha (less than 5% of theTable 5 The genetic diversity detected with different types of SSR markersType of locus No. of alleles Gene diversity Expected heterozygosity PIC Class I-I12.880.630.650.60 Class I-II8.180.600.580.55 Class II-I8.330.630.630.58。
常用的生物信息学网址大全,非常全面时间:2006-12-26 13:42:58 来源:点击:3398 用生物信息学数据库和分析工具网址数据库因特网网址网上生物信息学教程 EMBL biocomputing tutorials/Embnetut/Gcg/index.html Plant genome dababase tutorial /pgdic生物信息学机构NCBI/International Nucleotide Sequence Database Collaboration./collab/ EBI/ USDA/ Sanger Centre/ 北京大学生物信息学中心数据库信息发布及其它GenBank Release Notesftp:///genbank/gbrel.txtdbEST summary report/dbEST/dbESTsummarv.html EMBL release noteshttp://www.genome.ad.jp/dbget-bin/show man?embl DDBJ release noteshttp://www.ddbj.nig.ac.jp/ddbjnew/ddbj relnote.html Eukaryotic promoter database release noteshttp://www.genome.ad.jp/dbget/dbget2.htmlSwissProt release noteshttp://www.genome.ad.jp/dbget-bin/show man?swissprot PIR release noteshttp://www.genome.ad.jp/dbget-bin/show man?pirPRF release noteshttp://www.genome.ad.jp/dbget-bin/show man?prf PDBSTR release noteshttp://www.genome.ad.jp/dbget-bin/show man?pdbstr Prosite release noteshttp://www.genome.ad.jp/dbget-bin/show man?prosite PDB release noteshttp://www.genome.ad.jp/dbget-bin/show man?pdb KEGG release noteshttp://www.genome.ad.jp/dbget-bin/show man?pathway核苷酸数据库GenBank/dbEST/dbEST/index.htmldbSTS/dbSTS/index.html dbGSS /dbGSS/index.html Genome (NCBI)/Entrez/Genome/org.html dbSNP/SNP/HTGS/HTGS/UniGene/UniGene/ EMBL核苷酸数据库/embl Genome (EBI)/genomes/ 向EMBL数据库提交序列/embl/Submission/webin.html DDBJ http://www.ddbj.nig.ac.jp/ Plant R gene database /rgenes启动子数据库Eukaryotic promoter databasehttp://www.epd.isb-sib.chhttp://www.genome.ad.jp/dbget/dbget2.html转录因子数据库 FRANSFAChttp://transfac.gbf.de ooTFD 蛋白质数据库 SWISS-PROT或TrEMBL/swissprot/http://www.expasy.ch/sprot/ PIR/pir/ PRFhttp://www.prf.or.jp/ PDBSTRhttp://www.genome.ad.jp/dbget-bin/www bfind?pdbstr-today Prositehttp://www.expasy.ch/sprot/prosite.html结构数据库 PDB/pdb NDB/NDB/ndb.html/ DNA-Binding Protein Database/NDB/structure-finder/dnabind/index.html NMR Nucleic Acids Database/NDB/structure-finder/nmr/index.html Protein Plus Database/NDB/structure-finder/protein/index.html Swiss3Dimagehttp://www.expasy.ch/sw3d/ SCOP/scop/ CATH/bsm/cath/ 酶、代谢和调控路径数据库 KEGG http://www.genome.ad.jp/kegg/kegg2.html Enzyme Nomenclature Database http://expasy.hcuge.ch/sprot/enzyme.html Protein Kinase Resource (PKR) /kinases/ LIGANDhttp://www.genome.ad.jp/dbget/ligand.html WIT/WIT/ EcoCyc/ecocyc/ UM-BBD/umbbd/多种代谢路径数据库/stc-95/ResTools/biotools/biotools8.html基因调控路径数据库(TRANSPATH)http://transfac.gbf.de基因组数据库日本水稻基因组数据库(RGP)http://rgp.dna.affrc.go.jp 华大水稻基因组框架图 欧洲水稻测序(第12染色体)s.fr 拟南芥基因组数据库 USDA Database/ Demeter’s Genomes RiceGenes/cgi-bin/WebAce/webace?db=ricegenes RiceBlastDB/cgi-bin/WebAce/webace?db=riceblastdb FlyBase/.bin/fbidq.html?FBgn0003075 Mouse Genome Informatics/bin/query_accession?id=MGI:97555 Saccharomyces Genome Database/cgi-bin/dbrun/SacchDB?find+Locus+%22PGK1%22 多种基因组数据库/GenomeWeb 文献数据库 PubMed/PubMed/ OMIM/Omim/ Agricola/ag98/关键词为基础的数据库检索 Entrez/Entrez/ Entrez Nucleotide Sequence Search/Entrez/nucleotide.html Entrez Protein Sequence Search/Entrez/protein.html Batch Entrez/Entrez/batch.html Sequence Retrieval System, Indiahttp://bioinfo.ernet.in:80/srs5/ Sequence Retrieval System, Singapore.sg:80/srs5/ Sequence Retrieval System, US:80/srs/srsc Sequence Retrieval System, UK/ GetEntry Nucleotide & Protein Sequence Searchhttp://ftp2.ddbj.nig.ac.jp:8000/getstart-e.html Database Search with Key Wordshttp://ftp2.ddbj.nig.ac.jp:8080/dbsearch-e-new.html DBGET/LinkDBhttp://www.genome.ad.jp/dbget/dbget2.html序列为基础的数据库检索 BLAST/BLAST/ FASTA/fasta3/ BLITZ/bicsw/ SSearchrs.fr/bin/ssearch-guess.cgi Electronic PCR/STS/ Proteome analysis/proteome/多序列分析 Clustal multiple sequence alignment:9331/multi-align/Options/clustalw. html BCM:9331/multi-align/multi-align.html EBI ClustalW analysis 系谱分析 PAUP/PAUP/ EBI ClustalW analysis GCG package/ PHYLIP/phylip.html MEGA/METREE/imeg Hennig86/~mes/hennig/software.html GAMBIT/mcdbio/Faculty/Lake/Research/Programs/ MacClade /macclade/macclade.html Phylogenetic analysis /stc-95/ResTools/biotools/biotools2.html基因结构预测分析 GENSCAN/GENSCAN.html GeneFinder/gf/gf.shtml/nucleo.html Gene Feature Searches:9331/ Grail/Grail-1.3/ GrailEXP/grailexp/ GeneMark/GeneMark/hmmchoice.html Veil/labs/compbio/veil.html AAT/aat.html GENEIDhttp://www.imim.es/GeneIdentification/Geneid/geneid_input.html Genlang/~sdong/genlang_home.html GeneParser/~eesnyder/GeneParser.html Glimmer/labs/compbio/glimmer.html MZEF/genefinder Procrustes/software/procrustes/蛋白质结构预测分析 Expasyhttp://www.expasy.ch/ Predicting protein secondary structure:9331/pssprediction/pssp.html Predicting protein 3D Structureshttp://dove.embl-heidelberg.de/3D/ Predicting protein structures:9331/seq-search/struc-predict.html其它分析工具和软件 Putative DNA Sequencing Errors Checkhttp://www.bork.embl-heidelberg.de/Frame/ MatInspectorhttp://www.gsf.de/cgi-bin/matsearch.pl FastMhttp://www.gsf.de/cgi-bin/fastm.pl Web Signal Scanhttp://www.dna.affrc.go.jp/htdocs/sigscan/signal.html BCM Search Launcher:9331/seq-util/seq-util.html Webcutter/cutter/cut2.html Translate DNA to proteinhttp://www.expasy.ch/tools/dna.html ABIMhttp://www-biol.univ-mrs.fr/english/logligne.html sequence motifs: Pfam/Pfam// ProDomhttp://protein.toulouse.inra.fr/prodom.html PRINTS/bsm/dbbrowser/PRINTS/其它多种数据库、分析工具和生物信息学机构/stc-95/Restools/biotools 多种数据库和分析工具/Tools/ Comparative sequence analysishttp://www.bork.embl-heidelberg.de/ 功能基因组分析 Transcription profiling technologies/ncicgap/expression_tech_info.html Protocols for cDNA array technology/pbrown/array.html Data management and analysis of gene expression arrays/DIR/LCG/15k/HTML/Examples of commercially available filter arrays: GeneFiltersTM (Research Genetics) Gene Discovery Arrays (Genome Systems) AtlasTM Arrays (CLONTECH)。
2013,42(1):86-90.Subtropical Plant Science非洲加纳籽生物学特性及其化学成分(综述) 谌迪1,2,林德钦2,郑志忠2,3,童庆宣2,林河通1,明艳林2(1.福建农林大学食品科学学院,福建福州350002;2.厦门华侨亚热带植物引种园药用植物与植物药研发中心,福建厦门361002;3.天医堂(厦门)生物工程有限公司,福建厦门361022)摘 要:现代植物化学研究发现,非洲加纳籽的主要化学成分为5-羟基色氨酸;现代药理研究表明,该植物具有抗抑郁、抑制肥胖和抗氧化等各种药理活性。
本文系统介绍非洲加纳籽的生物学特性及化学成分与药理药效的国内外研究进展,为其引种驯化与深度开发提供理论依据。
关键词:非洲加纳籽;引种驯化;化学成分;5-羟基色氨酸Doi: 10.3969/j.issn.1009-7791.2013.01.019中图分类号:Q949.95 文献标识码:A 文章编号:1009-7791(2013)01-0086-05Biological Characteristics of Griffonia simplicifolia and its Chemical Composition CHEN Di1,2, LIN De-qin2, ZHENG Zhi-zhong2,3, TONG Qing-xuan2, LIN He-tong1, MING Yan-lin2 (1.College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian China; 2.Research Center for Medicinal Plants and Plant Medicine, Xiamen Overseas Chinese Subtropical Plant Introduction Garden, Xiamen 361002, Fujian China; 3.Enjoye C&G (Xiamen) Bioengineering Co., Ltd, Xiamen 361022, Fujian China)Abstract: The modern phytochemistry study found that 5-hydroxytryptophan was the main chemical composition of Griffonia simplicifolia, and modern pharmacology researches showed that it had some pharmacological activities including depression, inhibition of obesity, antioxidation and so on. This article systematically introduced the biological characteristics of Griffonia simplicifolia and some correlative researches about its chemical composition and pharmacological efficacy, which provides a theoretical basis for its introduction, domestication and further development.Key words: Griffonia simplicifolia; introduction and domestication; chemical composition;5-hydroxytryptophan非洲加纳籽(Griffonia simplicifolia)为云实亚科加纳籽属植物,原产于加纳、科特迪瓦和多哥等西非国家[1-2]。
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Leading EdgeReviewDevelopment and Applications ofCRISPR-Cas9for Genome EngineeringPatrick D.Hsu,1,2,3Eric nder,1and Feng Zhang1,2,*1Broad Institute of MIT and Harvard,7Cambridge Center,Cambridge,MA02141,USA2McGovern Institute for Brain Research,Department of Brain and Cognitive Sciences,Department of Biological Engineering, Massachusetts Institute of Technology,Cambridge,MA02139,USA3Department of Molecular and Cellular Biology,Harvard University,Cambridge,MA02138,USA*Correspondence:zhang@/10.1016/j.cell.2014.05.010Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9are enabling the systematic interrogation of mammalian genome function.Analogous to the search function in modern word processors,Cas9can be guided to specific locations within complex genomes by a short RNA search ing this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice.Cas9-mediated genetic perturbation is simple and scalable,empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review,we describe the development and applications of Cas9for a variety of research or translational applications while highlighting challenges as well as future directions.Derived from a remarkable microbial defense system,Cas9is driving innovative applications from basic biology to biotechnology and medicine.IntroductionThe development of recombinant DNA technology in the1970s marked the beginning of a new era for biology.For thefirst time,molecular biologists gained the ability to manipulate DNA molecules,making it possible to study genes and harness them to develop novel medicine and biotechnology.Recent advances in genome engineering technologies are sparking a new revolution in biological research.Rather than studying DNA taken out of the context of the genome,researchers can now directly edit or modulate the function of DNA sequences in their endogenous context in virtually any organism of choice, enabling them to elucidate the functional organization of the genome at the systems level,as well as identify causal genetic variations.Broadly speaking,genome engineering refers to the process of making targeted modifications to the genome,its contexts (e.g.,epigenetic marks),or its outputs(e.g.,transcripts).The ability to do so easily and efficiently in eukaryotic and especially mammalian cells holds immense promise to transform basic sci-ence,biotechnology,and medicine(Figure1).For life sciences research,technologies that can delete,insert, and modify the DNA sequences of cells or organisms enable dis-secting the function of specific genes and regulatory elements. Multiplexed editing could further allow the interrogation of gene or protein networks at a larger scale.Similarly,manipu-lating transcriptional regulation or chromatin states at particular loci can reveal how genetic material is organized and utilized within a cell,illuminating relationships between the architecture of the genome and its functions.In biotechnology,precise manipulation of genetic building blocks and regulatory machin-ery also facilitates the reverse engineering or reconstruction of useful biological systems,for example,by enhancing biofuel production pathways in industrially relevant organisms or by creating infection-resistant crops.Additionally,genome engi-neering is stimulating a new generation of drug development processes and medical therapeutics.Perturbation of multiple genes simultaneously could model the additive effects that un-derlie complex polygenic disorders,leading to new drug targets, while genome editing could directly correct harmful mutations in the context of human gene therapy(Tebas et al.,2014). Eukaryotic genomes contain billions of DNA bases and are difficult to manipulate.One of the breakthroughs in genome manipulation has been the development of gene targeting by homologous recombination(HR),which integrates exogenous repair templates that contain sequence homology to the donor site(Figure2A)(Capecchi,1989).HR-mediated targeting has facilitated the generation of knockin and knockout animal models via manipulation of germline competent stem cells, dramatically advancing many areas of biological research.How-ever,although HR-mediated gene targeting produces highly pre-cise alterations,the desired recombination events occur extremely infrequently(1in106–109cells)(Capecchi,1989),pre-senting enormous challenges for large-scale applications of gene-targeting experiments.To overcome these challenges,a series of programmable nuclease-based genome editing technologies havebeen1262Cell157,June5,2014ª2014Elsevier Inc.developed in recent years,enabling targeted and efficient modi-fication of a variety of eukaryotic and particularly mammalian species.Of the current generation of genome editing technolo-gies,the most rapidly developing is the class of RNA-guided endonucleases known as Cas9from the microbial adaptive im-mune system CRISPR (clustered regularly interspaced short palindromic repeats),which can be easily targeted to virtually any genomic location of choice by a short RNA guide.Here,we review the development and applications of the CRISPR-associated endonuclease Cas9as a platform technology for achieving targeted perturbation of endogenous genomic ele-ments and also discuss challenges and future avenues for inno-vation.Programmable Nucleases as Tools for Efficient and Precise Genome EditingA series of studies by Haber and Jasin (Rudin et al.,1989;Plessis et al.,1992;Rouet et al.,1994;Choulika et al.,1995;Bibikova et al.,2001;Bibikova et al.,2003)led to the realization that tar-geted DNA double-strand breaks (DSBs)could greatly stimulate genome editing through HR-mediated recombination events.Subsequently,Carroll and Chandrasegaran demonstrated the potential of designer nucleases based on zinc finger proteins for efficient,locus-specific HR (Bibikova et al.,2001,2003).Moreover,it was shown in the absence of an exogenous homol-ogy repair template that localized DSBs can induce insertions or deletion mutations (indels)via the error-prone nonhomologous end-joining (NHEJ)repair pathway (Figure 2A)(Bibikova et al.,2002).These early genome editing studies established DSB-induced HR and NHEJ as powerful pathways for the versatileand precise modification of eukaryotic genomes.To achieve effective genome editing via introduction of site-specific DNA DSBs,four major classes of customizable DNA-binding proteins have been engineered so far:meganucleases derived from microbial mobile genetic elements (Smith et al.,2006),zinc finger (ZF)nucleases based on eukaryotic transcrip-tion factors (Urnov et al.,2005;Miller et al.,2007),transcription activator-like effectors (TALEs)from Xanthomonas bacteria (Christian et al.,2010;Miller et al.,2011;Boch et al.,2009;Mos-cou and Bogdanove,2009),and most recently the RNA-guided DNA endonuclease Cas9from the type II bacterial adaptive im-mune system CRISPR (Cong et al.,2013;Mali et al.,2013a ).Meganuclease,ZF,and TALE proteins all recognize specific DNA sequences through protein-DNA interactions.Although meganucleases integrate its nuclease and DNA-binding domains,ZF and TALE proteins consist of individual modules targeting 3or 1nucleotides (nt)of DNA,respectively (Figure 2B).ZFs and TALEs can be assembled in desired combi-nations and attached to the nuclease domain of FokI to direct nucleolytic activity toward specific genomic loci.Each of these platforms,however,has unique limitations.Meganucleases have not been widely adopted as a genome engineering platform due to lack of clear correspondence between meganuclease protein residues and their target DNA sequence specificity.ZF domains,on the other hand,exhibit context-dependent binding preference due to crosstalk between adjacent modules when assembled into a larger array (Maeder et al.,2008).Although multiple strategies have been developed to account for these limitations (Gonzaelz et al.,2010;Sander et al.,2011),assembly of functional ZFPs with the desired DNA binding specificity remains a major challenge that requires an extensive screening process.Similarly,although TALE DNA-binding monomers are for the most part modular,they can still suffer from context-dependent specificity (Juillerat et al.,2014),and their repetitive sequences render construction of novel TALE arrays labor intensive and costly.Given the challenges associated with engineering of modular DNA-binding proteins,new modes of recognition would signifi-cantly simplify the development of custom nucleases.The CRISPR nuclease Cas9is targeted by a short guide RNA that recognizes the target DNA via Watson-Crick base pairing (Figure 2C).The guide sequence within these CRISPR RNAs typically corresponds to phage sequences,constituting the nat-ural mechanism for CRISPR antiviral defense,but can be easily replaced by a sequence of interest to retarget the Cas9nuclease.Multiplexed targeting by Cas9can now be achieved at unprecedented scale by introducing a battery of short guideFigure 1.Applications of Genome EngineeringGenetic and epigenetic control of cells with genome engineering technologies is enabling a broad range of applications from basic biology to biotechnology and medicine.(Clockwise from top)Causal genetic mutations or epigenetic variants associated with altered biological function or disease phenotypes can now be rapidly and efficiently recapitulated in animal or cellular models (Animal models,Genetic variation).Manipulating biological circuits could also facilitate the generation of useful synthetic materials,such as algae-derived,silica-based diatoms for oral drug delivery (Materials).Additionally,precise genetic engineering of important agricultural crops could confer resistance to envi-ronmental deprivation or pathogenic infection,improving food security while avoiding the introduction of foreign DNA (Food).Sustainable and cost-effec-tive biofuels are attractive sources for renewable energy,which could be achieved by creating efficient metabolic pathways for ethanol production in algae or corn (Fuel).Direct in vivo correction of genetic or epigenetic defects in somatic tissue would be permanent genetic solutions that address the root cause of genetically encoded disorders (Gene surgery).Finally,engineering cells to optimize high yield generation of drug precursors in bacterial factories could significantly reduce the cost and accessibility of useful therapeutics (Drug development).Cell 157,June 5,2014ª2014Elsevier Inc.1263RNAs rather than a library of large,bulky proteins.The ease of Cas9targeting,its high efficiency as a site-specific nuclease,and the possibility for highly multiplexed modifications have opened up a broad range of biological applications across basic research to biotechnology and medicine.The utility of customizable DNA-binding domains extends far beyond genome editing with site-specific endonucleases.Fusing them to modular,sequence-agnostic functional effector domains allows flexible recruitment of desired perturbations,such as transcriptional activation,to a locus of interest (Xu and Bestor,1997;Beerli et al.,2000a;Konermann et al.,2013;Maeder et al.,2013a;Mendenhall et al.,2013).In fact,any modular enzymatic component can,in principle,be substituted,allowing facile additions to the genome engineering toolbox.Integration of genome-and epigenome-modifying enzymes with inducible protein regulation further allows precise temporal control of dynamic processes (Beerli et al.,2000b;Konermann et al.,2013).CRISPR-Cas9:From Yogurt to Genome EditingThe recent development of the Cas9endonuclease for genome editing draws upon more than a decade of basic research into understanding the biological function of the mysterious repetitive elements now known as CRISPR (Figure 3),which are found throughout the bacterial and archaeal diversity.CRISPR loci typically consist of a clustered set of CRISPR-associated (Cas)genes and the signature CRISPR array—a series of repeat sequences (direct repeats)interspaced by variable sequences (spacers)corresponding to sequences within foreign genetic elements (protospacers)(Figure 4).Whereas Cas genes are translated into proteins,most CRISPR arrays are first tran-scribed as a single RNA before subsequent processing into shorter CRISPR RNAs (crRNAs),which direct the nucleolytic activity of certain Cas enzymes to degrade target nucleic acids.The CRISPR story began in 1987.While studying the iap enzyme involved in isozyme conversion of alkaline phosphatase in E.coli ,Nakata and colleagues reported a curious set of 29nt repeats downstream of the iap gene (Ishino et al.,1987).Unlike most repetitive elements,which typically take the form of tandem repeats like TALE repeat monomers,these 29nt repeats were interspaced by five intervening 32nt nonrepetitive sequences.Over the next 10years,as more microbial genomes were sequenced,additional repeat elements were reported from genomes of different bacterial and archaeal strains.Mojica and colleagues eventually classified interspaced repeat sequences as a unique family of clustered repeat elements present in >40%of sequenced bacteria and 90%of archaea (Mojica et al.,2000).These early findings began to stimulate interest in such micro-bial repeat elements.By 2002,Jansen and Mojica coined the acronym CRISPR to unify the description of microbial genomic loci consisting of an interspaced repeat array (Jansen et al.,2002;Barrangou and van der Oost,2013).At the same time,several clusters of signature CRISPR-associated (cas )genes were identified to be well conserved and typically adjacent to the repeat elements (Jansen et al.,2002),serving as a basis for the eventual classification of three different types of CRISPR systems (types I–III)(Haft et al.,2005;Makarova et al.,2011b ).Types I and III CRISPR loci contain multiple Cas proteins,now known to form complexes with crRNA (CASCADE complex for type I;Cmr or Csm RAMP complexes for type III)to facilitate the recognition and destruction of target nucleic acids (BrounsFigure 2.Genome Editing Technologies Exploit Endogenous DNA Repair Machinery(A)DNA double-strand breaks (DSBs)are typically repaired by nonhomologous end-joining (NHEJ)or homology-directed repair (HDR).In the error-prone NHEJ pathway,Ku heterodimers bind to DSB ends and serve as a molecular scaffold for associated repair proteins.Indels are introduced when the complementary strands undergo end resection and misaligned repair due to micro-homology,eventually leading to frameshift muta-tions and gene knockout.Alternatively,Rad51proteins may bind DSB ends during the initial phase of HDR,recruiting accessory factors that direct genomic recombination with homology arms on an exogenous repair template.Bypassing the matching sister chromatid facilitates the introduction of precise gene modifications.(B)Zinc finger (ZF)proteins and transcription activator-like effectors (TALEs)are naturally occurring DNA-binding domains that can be modularly assembled to target specific se-quences.ZF and TALE domains each recognize 3and 1bp of DNA,respectively.Such DNA-binding proteins can be fused to the FokI endonuclease to generate programmable site-specific nucleases.(C)The Cas9nuclease from the microbial CRISPR adaptive immune system is localized to specific DNA sequences via the guide sequence on its guide RNA (red),directly base-pairing with the DNA target.Binding of a protospacer-adjacent motif (PAM,blue)downstream of the target locus helps to direct Cas9-mediated DSBs.1264Cell 157,June 5,2014ª2014Elsevier Inc.et al.,2008;Hale et al.,2009)(Figure 4).In contrast,the type II system has a significantly reduced number of Cas proteins.However,despite increasingly detailed mapping and annotation of CRISPR loci across many microbial species,their biological significance remained elusive.A key turning point came in 2005,when systematic analysis of the spacer sequences separating the individual direct repeats suggested their extrachromosomal and phage-associated ori-gins (Mojica et al.,2005;Pourcel et al.,2005;Bolotin et al.,2005).This insight was tremendously exciting,especially given previous studies showing that CRISPR loci are transcribed (Tang et al.,2002)and that viruses are unable to infect archaeal cells carrying spacers corresponding to their own genomes (Mojica et al.,2005).Together,these findings led to the specula-tion that CRISPR arrays serve as an immune memory and defense mechanism,and individual spacers facilitate defense against bacteriophage infection by exploiting Watson-Crick base-pairing between nucleic acids (Mojica et al.,2005;Pourcel et al.,2005).Despite these compelling realizations that CRISPR loci might be involved in microbial immunity,the specific mech-anism of how the spacers act to mediate viral defense remained a challenging puzzle.Several hypotheses were raised,including thoughts that CRISPR spacers act as small RNA guides to degrade viral transcripts in a RNAi-like mechanism (Makarova et al.,2006)or that CRISPR spacers direct Cas enzymes to cleave viral DNA at spacer-matching regions (Bolotin et al.,2005).Working with the dairy production bacterial strain Strepto-coccus thermophilus at the food ingredient company Danisco,Horvath and colleagues uncovered the first experimental evidence for the natural role of a type II CRISPR system as an adaptive immunity system,demonstrating a nucleic-acid-based immune system in which CRISPR spacers dictate target speci-ficity while Cas enzymes control spacer acquisition and phage defense (Barrangou et al.,2007).A rapid series of studies illumi-nating the mechanisms of CRISPR defense followed shortly and helped to establish the mechanism as well as function of all three types of CRISPR loci in adaptive immunity.By studying the type I CRISPR locus of Escherichia coli ,van der Oost and colleagues showed that CRISPR arrays are transcribed and converted into small crRNAs containing individual spacers to guide Cas nuclease activity (Brouns et al.,2008).In the same year,CRISPR-mediated defense by a type III-A CRISPR system from Staphylococcus epidermidis was demonstrated to block plasmid conjugation,establishing the target of Cas enzyme activity as DNA rather than RNA (Marraffini andSontheimer,Figure 3.Key Studies Characterizing and Engineering CRISPR SystemsCas9has also been referred to as Cas5,Csx12,and Csn1in literature prior to 2012.For clarity,we exclusively adopt the Cas9nomenclature throughout this Review.CRISPR,clustered regularly interspaced short palindromic repeats;Cas,CRISPR-associated;crRNA,CRISPR RNA;DSB,double-strand break;tracrRNA,trans -activating CRISPR RNA.Cell 157,June 5,2014ª2014Elsevier Inc.12652008),although later investigation of a different type III-B system from Pyrococcus furiosus also revealed crRNA-directed RNA cleavage activity(Hale et al.,2009,2012).As the pace of CRISPR research accelerated,researchers quickly unraveled many details of each type of CRISPR system (Figure4).Building on an earlier speculation that protospacer-adjacent motifs(PAMs)may direct the type II Cas9nuclease to cleave DNA(Bolotin et al.,2005),Moineau and colleagues high-lighted the importance of PAM sequences by demonstrating that PAM mutations in phage genomes circumvented CRISPR inter-ference(Deveau et al.,2008).Additionally,for types I and II,the lack of PAM within the direct repeat sequence within the CRISPR array prevents self-targeting by the CRISPR system.In type III systems,however,mismatches between the50end of the crRNA and the DNA target are required for plasmid interference(Marraf-fini and Sontheimer,2010).By2010,just3years after thefirst experimental evidence for CRISPR in bacterial immunity,the basic function and mecha-nisms of CRISPR systems were becoming clear.A variety of groups had begun to harness the natural CRISPR system for various biotechnological applications,including the generation of phage-resistant dairy cultures(Quiberoni et al.,2010)and phylogenetic classification of bacterial strains(Horvath et al., 2008,2009).However,genome editing applications had not yet been explored.Around this time,two studies characterizing the functional mechanisms of the native type II CRISPR system elucidated the basic components that proved vital for engineering a simple RNA-programmable DNA endonuclease for genome editing. First,Moineau and colleagues used genetic studies in Strepto-coccus thermophilus to reveal that Cas9(formerly called Cas5,Csn1,or Csx12)is the only enzyme within the cas gene cluster that mediates target DNA cleavage(Garneau et al.,2010).Next,Charpentier and colleagues revealed a key component in the biogenesis and processing of crRNA in type II CRISPR systems—a noncoding trans-activating crRNA(tracrRNA)that hybridizes with crRNA to facilitate RNA-guided targeting of Cas9(Deltcheva et al.,2011).This dual RNA hybrid,together with Cas9and endogenous RNase III,is required for processing the CRISPR array transcript into mature crRNAs(Deltcheva et al.,2011).These two studies suggested that there are at least three components(Cas9, the mature crRNA,and tracrRNA)that are essential for recon-stituting the type II CRISPR nuclease system.Given the increasing importance of programmable site-specific nucleases based on ZFs and TALEs for enhancing eukaryotic genome editing,it was tantalizing to think that perhaps Cas9could be developed into an RNA-guided genome editing system. From this point,the race to harness Cas9for genome editing wason.Figure4.Natural Mechanisms of Microbial CRISPR Systems in Adaptive Immunity Following invasion of the cell by foreign genetic elements from bacteriophages or plasmids(step 1:phage infection),certain CRISPR-associated (Cas)enzymes acquire spacers from the exoge-nous protospacer sequences and install them into the CRISPR locus within the prokaryotic genome (step2:spacer acquisition).These spacers are segregated between direct repeats that allow the CRISPR system to mediate self and nonself recognition.The CRISPR array is a noncoding RNA transcript that is enzymatically maturated through distinct pathways that are unique to each type of CRISPR system(step3:crRNA biogenesis and processing).In types I and III CRISPR,the pre-crRNA transcript is cleaved within the repeats by CRISPR-asso-ciated ribonucleases,releasing multiple small crRNAs.Type III crRNA intermediates are further processed at the30end by yet-to-be-identified RNases to produce the fully mature transcript.In type II CRISPR,an associated trans-activating CRISPR RNA(tracrRNA)hybridizes with the direct repeats,forming an RNA duplex that is cleaved and processed by endogenous RNase III and other unknown nucleases.Maturated crRNAs from type I and III CRISPR systems are then loaded onto effector protein complexes for target recognition and degradation.In type II systems, crRNA-tracrRNA hybrids complex with Cas9to mediate interference.Both type I and III CRISPR systems use multi-protein interference modules to facilitate target recognition.In type I CRISPR,the Cascade com-plex is loaded with a crRNA molecule,constituting a catalytically inert surveillance complex that rec-ognizes target DNA.The Cas3nuclease is then recruited to the Cascade-bound R loop,mediatingtarget degradation.In type III CRISPR,crRNAs associate either with Csm or Cmr complexes that bind and cleave DNA and RNA substrates,respectively.In contrast,the type II system requires only the Cas9nuclease to degrade DNA matching its dual guide RNA consisting of a crRNA-tracrRNA hybrid.1266Cell157,June5,2014ª2014Elsevier Inc.In2011,Siksnys and colleaguesfirst demonstrated that the type II CRISPR system is transferrable,in that transplantation of the type II CRISPR locus from Streptococcus thermophilus into Escherichia coli is able to reconstitute CRISPR interference in a different bacterial strain(Sapranauskas et al.,2011).By 2012,biochemical characterizations by the groups of Charpent-ier,Doudna,and Siksnys showed that purified Cas9from Strep-tococcus thermophilus or Streptococcus pyogenes can be guided by crRNAs to cleave target DNA in vitro(Jinek et al., 2012;Gasiunas et al.,2012),in agreement with previous bacte-rial studies(Garneau et al.,2010;Deltcheva et al.,2011;Sapra-nauskas et al.,2011).Furthermore,a single guide RNA(sgRNA) can be constructed by fusing a crRNA containing the targeting guide sequence to a tracrRNA that facilitates DNA cleavage by Cas9in vitro(Jinek et al.,2012).In2013,a pair of studies simultaneously showed how to suc-cessfully engineer type II CRISPR systems from Streptococcus thermophilus(Cong et al.,2013)and Streptococcus pyogenes (Cong et al.,2013;Mali et al.,2013a)to accomplish genome editing in mammalian cells.Heterologous expression of mature crRNA-tracrRNA hybrids(Cong et al.,2013)as well as sgRNAs (Cong et al.,2013;Mali et al.,2013a)directs Cas9cleavage within the mammalian cellular genome to stimulate NHEJ or HDR-mediated genome editing.Multiple guide RNAs can also be used to target several genes at once.Since these initial studies,Cas9has been used by thousands of laboratories for genome editing applications in a variety of experimental model systems(Sander and Joung,2014).The rapid adoption of the Cas9technology was also greatly accelerated through a com-bination of open-source distributors such as Addgene,as well as a number of online user forums such as http://www. and . Structural Organization and Domain Architecture ofCas9The family of Cas9proteins is characterized by two signature nuclease domains,RuvC and HNH,each named based on homology to known nuclease domain structures(Figure2C). Though HNH is a single nuclease domain,the full RuvC domain is divided into three subdomains across the linear protein sequence,with RuvC I near the N-terminal region of Cas9and RuvC II/IIIflanking the HNH domain near the middle of the pro-tein.Recently,a pair of structural studies shed light on the struc-tural mechanism of RNA-guided DNA cleavage by Cas9. First,single-particle EM reconstructions of the Streptococcus pyogenes Cas9(SpCas9)revealed a large structural rearrange-ment between apo-Cas9unbound to nucleic acid and Cas9in complex with crRNA and tracrRNA,forming a central channel to accommodate the RNA-DNA heteroduplex(Jinek et al., 2014).Second,a high-resolution structure of SpCas9in complex with sgRNA and the complementary strand of target DNA further revealed the domain organization to comprise of an a-helical recognition(REC)lobe and a nuclease(NUC)lobe consisting of the HNH domain,assembled RuvC subdomains,and a PAM-interacting(PI)C-terminal region(Nishimasu et al.,2014) (Figure5A and Movie S1).Together,these two studies support the model that SpCas9 unbound to target DNA or guide RNA exhibits an autoinhibited conformation in which the HNH domain active site is blocked by the RuvC domain and is positioned away from the REC lobe (Jinek et al.,2014).Binding of the RNA-DNA heteroduplex would additionally be sterically inhibited by the orientation of the C-ter-minal domain.As a result,apo-Cas9likely cannot bind nor cleave target DNA.Like many ribonucleoprotein complexes,the guide RNA serves as a scaffold around which Cas9can fold and orga-nize its various domains(Nishimasu et al.,2014).The crystal structure of SpCas9in complex with an sgRNA and target DNA also revealed how the REC lobe facilitates target binding.An arginine-rich bridge helix(BH)within the REC lobe is responsible for contacting the308–12nt of the RNA-DNA het-eroduplex(Nishimasu et al.,2014),which correspond with the seed sequence identified through guide sequence mutation ex-periments(Jinek et al.,2012;Cong et al.,2013;Fu et al.,2013; Hsu et al.,2013;Pattanayak et al.,2013;Mali et al.,2013b). The SpCas9structure also provides a useful scaffold for engi-neering or refactoring of Cas9and sgRNA.Because the REC2 domain of SpCas9is poorly conserved in shorter orthologs, domain recombination or truncation is a promising approach for minimizing Cas9size.SpCas9mutants lacking REC2retain roughly50%of wild-type cleavage activity,which could be partly attributed to their weaker expression levels(Nishimasu et al., 2014).Introducing combinations of orthologous domain re-combination,truncation,and peptide linkers could facilitate the generation of a suite of Cas9mutant variants optimized for different parameters such as DNA binding,DNA cleavage,or overall protein size.Metagenomic,Structural,and Functional Diversity of Cas9Cas9is exclusively associated with the type II CRISPR locus and serves as the signature type II gene.Based on the diversity of associated Cas genes,type II CRISPR loci are further subdivided into three subtypes(IIA–IIC)(Figure5B)(Makarova et al.,2011a; Chylinski et al.,2013).Type II CRISPR loci mostly consist of the cas9,cas1,and cas2genes,as well as a CRISPR array and tracrRNA.Type IIC CRISPR systems contain only this minimal set of cas genes,whereas types IIA and IIB have an additional signature csn2or cas4gene,respectively(Chylinski et al.,2013). Subtype classification of type II CRISPR loci is based on the architecture and organization of each CRISPR locus.For example,type IIA and IIB loci usually consist of four cas genes, whereas type IIC loci only contain three cas genes.However, this classification does not reflect the structural diversity of Cas9proteins,which exhibit sequence homology and length variability irrespective of the subtype classification of their parental CRISPR locus.Of>1,000Cas9nucleases identified from sequence databases(UniProt)based on homology,protein length is rather heterogeneous,roughly ranging from900to1600 amino acids(Figure5C).The length distribution of most Cas9 proteins can be divided into two populations centered around 1,100and1,350amino acids in length.It is worth noting that a third population of large Cas9proteins belonging to subtype IIA,formerly called Csx12,typically contain around1500amino acids.Despite the apparent diversity of protein length,all Cas9pro-teins share similar domain architecture(Makarova et al.,2011a;Cell157,June5,2014ª2014Elsevier Inc.1267。
专利名称:一种新的多肽——核糖体蛋白S10 14和编码这种多肽的多核苷酸
专利类型:发明专利
发明人:毛裕民,谢毅
申请号:CN99125390.6
申请日:19991227
公开号:CN1301725A
公开日:
20010704
专利内容由知识产权出版社提供
摘要:本发明公开了一种新的多肽——核糖体蛋白S1014,编码此多肽的多核苷酸和经DNA重组技术产生这种多肽的方法。
本发明还公开了此多肽用于治疗多种疾病的方法,如恶性肿瘤,血液病,HIV感染和免疫性疾病和各类炎症等。
本发明还公开了抗此多肽的拮抗剂及其治疗作用。
本发明还公开了编码这种新的核糖体蛋白S1014的多核苷酸的用途。
申请人:上海博德基因开发有限公司
地址:200092 上海市中山北二路1111号3号楼12层
国籍:CN
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中国病原生物学杂志2020年12月第15卷第12期•1370•Journal of Pathogen Biology Dec.2020,Vol.15.No.121)01:10.13350/j.cjpb.201202•论著•一株野鸟源H16N3亚型禽流感病毒的遗传进化分析与感染能力评估*孙雷云李元果张醒海….赵梦琳;.胡鑫宇;.王铁成‘,孙伟洋',冯娜:赵永坤杨松涛夏成柱「,孟德荣‘.高玉伟心…(1•占林农业大学动物科学技术学院,吉林长春130118;2.军事医学研究院军事兽医研究所;3.吉林大学;4.沧州师范学院〉目的了解H16N3亚型禽流感病毒的遗传进化特征及其化物学特性,为野鸟源禽流感病毒预警提供科学依据。
方法采集途径我国中东部地区重要候鸟栖息地的野鸟粪便样品.经处理后接种SPF鸡胚.获得具有血液凝集特性病原体.经全底因测序确定病毒亚型。
选取H16亚型流感病毒构建系统发育树并进行分子特性分析。
检测病毒受体结合特性.并进行小鼠和家禽感染试验.评价该病毒对哺乳动物和家禽的致病性。
结果分离到1株病原体(CZ-638),经全基因组测疗;及电镜观察.确定为H16N3W:型禽流感病毒。
在系统发育树种,该带株位于欧亚谱系分支。
氨基酸位点分析显示.HA蛋白裂解位点为INERl GI.F.符合低致病性禽流感病毒分子特征.受体结合域的228位点由G (It氨酸)突变为S"纟訊酸)。
该病毒株能够凝集绵羊红细胞、正常鸡红细胞及仅有SA«2,6受体的鸡红细胞.表明该毒株具有双受体结合能力。
动物感染试验显示.该毒株对小鼠、1周龄雏鸡、亚成体家鸭均不具有感染力。
结论分离的H16N3毒株为欧亚谱系.对小鼠和家禽无致病性。
该毒株存在结合人1:呼吸道流感病毒受体的能力.但尚未获得感染家禽和哺乳动物的能力•应持续监测.追踪病毒进化待征°关键词】禽流感病馭H16N3;遗传进化;致病性;受体结合待性;感染能力中图分类号】S852.65【文献标识码】【文章编号】1673-5234(2020)12-1370-07[Journal of Pathogen Biology.2020Dec;15(12):1370—1376.]A genetic evolutionary analysis and an evaluation of the infectivity of avian influenza H16N3isolated fromwild birds in ChinaSUN Lei-yun1'・LI Yuan-guo,ZHANG Xing-hai2',ZHAO Meng-lin・HU Xin—yu,WANG Tie-cheng J,SUN Wei-yang・FENG Na2・ZHAO Yong-kun~・YANG Song-tao2•XIA Xian-zhu2,MENG De~R o n g1,GAO Y u-wei~(1.College of Alli mal Science and Technology・Jilin Agricultural University»Changchun9China130118; 2.Institute of Military Veterinary Medicine.Academy of Military Medical Science; 3.J ilin University; 4.Can^zhou Normal University)Objectives To ascertain the genetic evolutionary characteristics and biological characteristics of the H16N3 subtype of the avian influenza virus in order to provide a scientific basis for early warning of avian influenza virus from wild birds.Methods Fecal samples from wild birds in major migratory bird habitats in central and eastern China were collected and inoculated into SPF chicken embryos after treatment to obtain pathogens with blood agglutination characteristics.and the virus subtypes were determined using whole gene sequencing.A phylogenetic tree was constructed for the H16subtype of the influenza virus.and the subtypes were characterized molecularly.The receptor binding characteristics of the virus were determined and infection tests were conducted in mice and poultry to evaluate the pathogenicity of the virus to mammals and poultry.Results One strain of pathogen(CZ-638)was isolated and identified as avian influenza virus subtype H16N3according to whole genome sequencing and electron microscopy.In the phylogenetic tree・the strain was located in the Eurasian lineage branch.An analysis of amino acid sites indicated that an HA protein cleavage site was INER J GLF.which was in line with the molecular characteristics of the low pathogenic avian influenza virus.Amino acid 228of the receptor binding domain mutated from G(glycine)to S(serine).This strain agglutinated sheep red blood cells・normal chicken red blood cells,and chicken red blood cells with the SAa2,6receptor alone・indicating that this vi-【基金项目】【通讯作者】【作者简介】国家科技重大专项(No.2O2OZX1OOO1-O16-OO3);国家自然科学基金项目(No.31970502)。
化工进展Chemical Industry and Engineering Progress2023 年第 42 卷第 8 期微纳米气泡在厌氧消化中的应用研究进展奚永兰1,2,3,4,王成成1,2,叶小梅2,3,刘洋1,2,贾昭炎1,2,曹春晖1,2,韩挺2,3,4,张应鹏2,3,4,田雨5(1 江苏大学农业工程学院,江苏 镇江 212013;2 江苏省农业科学院,江苏 南京 210014;3 农业农村部种养结合重点实验室,江苏 南京 210014;4 农业农村部农村可再生能源华东科学观测实验站,江苏 南京 210014;5南京卫岗乳业有限公司,江苏 南京 210014)摘要:微纳米气泡(micro/nano bubbles ,MNBs )因其广泛的应用领域及良好的应用前景而备受关注。
在厌氧消化(AD )领域,MNBs 具备的高气体转移效率、产生ROS 、高zeta 电位、高表面电荷以及固有的微曝气能力(空气或O 2-MNBs )等特性,可以提高AD 过程的性能和效率,改善限速步骤(水解和产甲烷),这为AD 工艺的改进提供了新的方向。
近年来,越来越多的研究将不同MNBs 以不同方式用于AD 中,这些研究主要集中于利用富有纳米气泡的纳米气泡水来提高AD 反应器性能,而有关微气泡在AD 中的应用研究相对较少。
考虑到纳米气泡和微气泡都拥有改善AD 的潜能,本文从MNBs 的特性、制备方法和设备、其在AD 中的研究现状和可能的作用机理方面开展综述,并探讨了未来可能的应用方向,旨在为进一步利用MNBs 增强AD 的研究提供参考。
关键词:微纳米气泡;厌氧消化;有机废弃物;微曝气;甲烷中图分类号:X70;TQ02 文献标志码:A 文章编号:1000-6613(2023)08-4414-10Research progress on the application of micro/nano bubblesin anaerobic digestionXI Yonglan 1,2,3,4,WANG Chengcheng 1,2,YE Xiaomei 2,3,LIU Yang 1,2,JIA Zhaoyan 1,2,CAO Chunhui 1,2,HAN Ting 2,3,4,ZHANG Yingpeng 2,3,4,TIAN Yu 5(1 School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China; 2 Jiangsu Academy ofAgriculture Sciences, Nanjing 210014, Jiangsu, China; 3 Key Laboratory of Crop and Livestock Integration, Ministry of Agriculture and Rural Affairs, Nanjing 210014, Jiangsu, China; 4 East China Scientific Observing and Experimental Station of Development and Utilization of Rural Renewable Energy, Ministry of Agriculture and Rural Affairs, Nanjing 210014,Jiangsu, China; 5 Nanjing Weigang Dairy Co., Ltd., Nanjing 210014, Jiangsu, China)Abstract: Research on micro/nano bubbles (MNBs) have attracted much attention due to their wideapplication fields and good application prospects. In the field of anaerobic digestion (AD), the high gas transfer efficiency, ROS generation, high zeta potential, high surface charge, and inherent micro-aeration capability (air or O 2-MNBs) of MNBs can improve the performance of AD processes, improving the rate-limiting steps (hydrolysis and methanogenesis), which provide new directions for the improvement of ADprocesses. In recent years, more and more studies have used different MNBs in AD in different ways.综述与专论DOI :10.16085/j.issn.1000-6613.2022-1762收稿日期:2022-09-21;修改稿日期:2022-11-09。
2023《NatureGenetics》棉属植物演化过程中的基因组创新和调控重整导读不同棉种植株表型差异明显,包括多年生高大树状、小灌木或匍匐生长;大部分纤维粗短,紧紧附着在种子上,只有 A 基因组二倍体和AD基因组类型的四倍体被驯化为一年生栽培种,其余棉种皆为野生种。
由于长期的驯化和育种选择,栽培种棉花的遗传多态性逐渐降低,亟需从野生棉中鉴定未被利用的优异基因,拓宽育种可用的基因资源。
厘清棉属基因组结构演化与纤维性状形成的关系,对于另辟蹊径提升棉花纤维品质至关重要。
结果1. 组装注释 7 个 2 倍体棉花基因组这些组装的基因组拥有40,321-41,949个蛋白编码基因和5,992-16,606个非编码RNAs。
基因组中的重复序列比例从69.5%到86.6%不等。
使用着丝粒相关LTR序列和基于CENH3染色质免疫沉淀法预测每个基因组中的着丝粒区域,然后进行(ChIP-seq)测序。
所用棉花材料包括7个代表性二倍体野生棉种和3个A基因组类型的棉花。
2. Gossypium(棉属)谱系的演化和历史种群规模a,二倍体棉种的系统发育地位。
使用 Gossypioides kirkii 作为外群进行每个棉属基因组的祖先染色体重建推断。
根据棉属谱系之间的Ks峰进行分歧时间推断,同义替换率(r)的扩张度为3.48×10−9、每个物种中总基因家族的扩张和收缩分别以蓝色和红色标示。
如图所示,比例尺表示的是1cm或5mm尺度。
b,不同棉属植物的地理分布。
蓝色箭头表示多个棉属物种之间的基因流,箭头大小表示D值大小。
根据历史记录,红色箭头表示从A1物种到A2基因组物种的两次人类介导的传播。
该地图经R包ggplot2“maps”和公共领域的自然地球数据集(修改。
c,来自25个物种70个棉种的主成分分析。
虚线框表示四个组:A基因组物种(A,第I组);B,F基因组物种(BF,第II组);C、K、G-基因组物种(CKG,第III 组);D基因组物种(D、E第IV组)。
现代农业科技2023年第7期农村经济学棉田信息采集云平台建设研究易孟华刘锋邢小丹(塔里木大学信息工程学院,新疆阿拉尔843300)摘要我国是棉花生产和消费大国,年生产总量和销量一直是世界首位,但棉花种植环节信息孤岛现象严重,没有实现棉花种植过程中信息的采集和融合。
针对这些问题,本文开发了一种棉田信息采集云平台,可实现棉花种植生产过程信息的收集、统计和共享。
病虫害综合防治是一个复杂的决策问题,采用以隐马尔可夫模型为基础的层次分析法将这一问题分为若干层次逐步进行分析,并建立棉田病虫害综合防治的层次结构模型。
关键词棉田;信息采集;云平台;病虫害防治中图分类号TP311.52;S562文献标识码A文章编号1007-5739(2023)07-0218-03DOI:10.3969/j.issn.1007-5739.2023.07.059开放科学(资源服务)标识码(OSID):Research on Construction of Cotton Field Information Collection Cloud PlatformYI Menghua LIU Feng XING Xiaodan(College of Information Engineering,Tarim University,Alar Xinjiang843300) Abstract China is a large country in cotton production and consumption,and its annual total production and sales volume have always been the first in the world,but the phenomenon of information island in cotton planting is serious, and the collection and integration of information in the cotton planting process has not been realized.Aiming at these problems,a cotton field information collection cloud platform was developed to realize the collection,statistics and sharing of cotton planting and production process information.Pest control was a complex decision-making problem.The analytic hierarchy process based on Hidden Markov Model was used to divide the problem into several levels and analyze it step by step,and the hierarchical structure model of integrated pest control in cotton fields was established.Keywords cotton field;information collection;cloud platform;pest control棉花是重要的经济作物,是关系国计民生的重要战略物资。
《锌指核酸酶介导的小鼠MSTN基因敲除的研究》篇一一、引言在基因编辑领域,锌指核酸酶(ZFNs)以其高度的序列特异性被广泛运用于各类基因编辑操作中。
本文着重介绍了一种以锌指核酸酶为工具的小鼠MSTN(肌萎缩硬化症基因)基因敲除研究。
该研究对于疾病模型建立、基因治疗及医学研究具有重要意义。
二、锌指核酸酶(ZFNs)简介锌指核酸酶(ZFNs)是一种人工合成的蛋白质,由锌指结构域和核酸酶结构域组成。
其最大的特点在于对靶序列的特异性识别和切割能力,能够在基因组中实现精确的编辑。
ZFNs在基因敲除、插入和突变等操作中发挥着重要作用。
三、MSTN基因简介MSTN(肌萎缩硬化症基因)是一种重要的生长调控基因,在哺乳动物中广泛存在。
该基因的突变或表达异常会导致多种疾病,如肌肉萎缩、骨质疏松等。
因此,研究MSTN基因的功能及表达调控机制,对于揭示相关疾病的发病机制及治疗具有重要意义。
四、实验方法本研究采用锌指核酸酶介导的基因敲除技术,针对小鼠MSTN基因进行敲除。
具体步骤如下:1. 设计并合成针对MSTN基因的锌指核酸酶;2. 将锌指核酸酶与DNA修复模板共转染至小鼠胚胎干细胞中;3. 通过同源重组技术,实现MSTN基因的敲除;4. 将敲除后的胚胎干细胞注射至小鼠囊胚中,构建基因敲除小鼠模型。
五、实验结果1. 成功设计并合成了针对MSTN基因的锌指核酸酶;2. 锌指核酸酶与DNA修复模板共转染后,成功实现了MSTN基因的敲除;3. 构建的基因敲除小鼠模型表现出明显的表型变化,如肌肉发育异常等;4. 通过PCR、Western Blot等分子生物学手段,验证了MSTN基因的敲除效果。
六、讨论本研究利用锌指核酸酶介导的基因编辑技术成功实现了小鼠MSTN基因的敲除,为研究MSTN基因的功能及表达调控机制提供了重要的工具。
同时,构建的基因敲除小鼠模型为相关疾病的研究和治疗提供了新的思路和方法。
然而,本研究仍存在一些局限性,如锌指核酸酶的设计和合成需要较高的技术要求,且基因编辑过程中可能存在脱靶效应等问题。
屋尘螨cDNA表达文库的构建及初步鉴定作者:刘良,彭江龙,周鹰,崔玉宝【摘要】目的:构建屋尘螨cDNA表达文库. 方法:用RNAiso Reagent试剂盒提取屋尘螨Total RNA,用Poly AT tract mRNA分离试剂盒提取mRNA,用Clontech公司SMARTTM PCR cDNA library kit 反转录合成第1链cDNA,用LD PCR合成第2链cDNA并扩增,PCR产物与MaxPlax TM试剂盒体外连接包装,建成未扩增的cDNA 文库,计算其滴度和重组效率后进行文库扩增并测定扩增文库的滴度. 结果:cDNA文库未扩增时滴度为9.148×106,重组效率达93.88%以上,文库扩增后滴度为7.628×109,插入片段平均1.63 kb. 结论:成功地构建了高质量的屋尘螨cDNA表达文库.【关键词】欧洲屋尘螨,基因文库0引言随着现代分子生物学技术的发展,应用基因工程变应原诊断和治疗变态反应性疾病已成为当代变态反应学研究的主流. 研究表明,基因工程技术通过减少重组变应原IgE结合的抗原表位,能有效地降低IgE介导的过敏反应,同时通过保留变应原T细胞识别所必须的结构域,因而具有较好的免疫原性,减少免疫治疗的危险性,可以提高脱敏治疗的效果[1-2].制备基因工程变应原的前提是获取目的基因. 从理论上讲,研究者可从一个合格的基因文库中调取任何目的基因,是发现并获取特异性基因克隆应用于重组变应原研究的有效方法之一. 本研究采用Clontech公司Smart方法构建屋尘螨cDNA表达文库,为系统地研究屋尘螨的基因结构和功能、进一步制备尘螨基因工程变应原奠定基础.1材料和方法1.1材料SMARTTM PCR cDNA library kit为美国Clontech 公司产品,RNAiso Reagent,Transcript RNA Clean Up Kit均购自日本TaKaRa公司,poly AT tract mRNA分离试剂盒由美国Promega公司生产. Oligo dT纤维素、MMLV反转录酶和MaxPlaxTMLambda Packaging Extract购自德国Eicentre technologies公司,其余试剂为国产分析纯. TP3000 PCR仪(Takara),Mupid电泳仪(Advance bio Co., Ltd),ImageMaster VDS电泳成像装置(Pharmacia Biotech),ABI PrismTM 377XL DNA Sequencer DNA测序仪(Perkin Elmer).1.2方法1.2.1cDNA文库的构建解剖镜下挑取屋尘螨约200只,匀浆后用TaKaRa RNAiso Reagent试剂盒提取Total RNA,用Poly AT tract mRNA 分离试剂盒提取mRNA,操作按说明书进行. 参照试剂盒说明书,cDNA文库的构建以mRNA为模板,Smart ⅢOligonucleotid (dT)为引物,在MMLV反转录酶作用下合成cDNA第1链;加入dNTPs,5 PCR Primer,3PCR Primer和Advantage Ⅱ聚合酶,置基因扩增仪上用长距离PCR(LD PCR)合成得双链DNA,取PCR产物5 μL用琼脂糖凝胶电泳检测,以鉴定双链DNA分布范围. 经蛋白酶K灭活、SfiⅠ酶切、过柱(Chro2Maspin 400) 分级分离后,连续收集12个单滴组分,每组分取2 μL进行琼脂糖凝胶电泳,收集前4个含有cDNA 的组分,用乙醇沉淀回收. 按照cDNA∶Vector = 1∶2/1∶1/2∶1 三种方式进行连接,用MaxPlaxTM试剂盒对连接产物进行体外包装,至此建屋尘螨cDNA未扩增文库.1.2.2未扩增文库滴度测定取3种连接方式稀释度为1∶5,1∶10和1∶20的包装产物各1 μL,分别与过夜液体培养物200 μL混合,并加入融化的LB/MgSO4 Top Agar 3 mL,快速倒在37℃预热的90 mm LB/MgSO4平板,快速旋转平板,使Top Agar水平分布,室温冷却10 min后置37℃倒置培养6~18 h. 统计噬菌斑,计算滴度pfu/mL=噬菌斑数×稀释因子×103/铺板体积(μL). 利用IPTG和X Gal诱导上述未扩增文库计数蓝、白斑,重组效率=白斑数/(白斑数+蓝斑数).1.2.3扩增文库滴度计算在10 mL试管中加入过夜培养的XL1 Blue菌液500 μL和足够的噬菌体液(未扩增文库),在37℃水浴15 min 后,每管加入融化的LB/MgSO4 Top Agar 9 mL,快速混匀铺板,室温冷却后置37℃倒置培养至噬菌斑长满,每块平板加入1×λ稀释液12 mL,4℃过夜,以得到已扩增文库的裂解液;将平板在脱色摇台上室温50 r/min 1 h,用灭菌烧杯收集平板中的噬菌体裂解液,充分混匀后分装于50 mL灭菌离心管,每管加入氯仿10 mL,盖紧,振荡混匀2 min,7000 r/min离心10 min,收集上清液,4℃保存. 扩增文库滴度计算方法同上,所选用的噬菌体裂解液稀释度为10-4,铺板体积为5 μL/12 mm.1.2.4文库的PCR鉴定随机挑取12个单克隆噬菌斑分别加入到装有100 μL 1×λ稀释缓冲液的管中,并加入XL1Blue菌的过夜培养物200 μL,制备DNA模板. 根据克隆位点两端的序列设计并委托上海生物工程公司合成引物P1(5′CTCCGAGATCTGGACGAGC 3′)和引物P2 (5′TAATACGACTCACTATAGGG3′). PCR 反应体积为50 μL,具体如下:DNA模板6 μL,引物P1和P2各1 μL (10 μmol/L),10×Advantage 2 PCR Buffer 5 μL,10 mmol/L dNTP Mix 2μL,50×Advantage 2 Polymerase Mix及ddH2O 34.6 μL. 反应条件:95℃预变性1 min,95℃50 s,56℃50 s,72℃1 min,共35个循环,最后72℃维持10 min. PCR产物用琼脂糖凝胶电泳鉴定.取灭菌的1.5 mL Eppendorf管,每管加文库裂解液1 mL及DMSO 70 μL,混匀,并做好标记、封口,-80℃保存文库.2结果2.1cDNA文库的构建按Takara公司RNAiso Reagent 说明书操作获得屋尘螨总RNA 30 μL,紫外分光光度计测定核酸含量0.62 g/L,A260/A280=1.87,12 g/L琼脂糖凝胶电泳结果显示18 s和28 s两条带,未见降解(图1),提示获得的总RNA纯度高. 用Poly AT tract mRNA 分离试剂盒提取mRNA后,以mRNA为模板,Smart ⅢOligonucleotid (dT)为引物,在MMLV反转录酶作用下合成cDNA第1链,电泳显示第1链cDNA大部分集中在0.5~2 kb之间,符合cDNA合成的分布规律(图2). LD PCR合成得双链DNA,取产物5 μL用琼脂糖凝胶电泳检测,合成的双链cDNA稍大于第1链,大部分集中在0.5 kb以上(图3).1~2: 屋尘螨总RNA.图1屋尘螨总RNA电泳图1: 屋尘螨cDNA第1链; M: DNA Marker DL2000.图2屋尘螨mRNA反转录合成的cDNA第1链2.2未扩增文库滴度计算噬菌斑数结果表明以第2种连接方式所产生的噬菌斑数最多,第3种连接方式次之,第1种连接方式最少,说明第2种连接方式效果最好(表1). 取3种连接的最小值计算每种连接的滴度,pfu/L=(2.21+16.875+8.36)×0.5×109/(0.5+0.5+0.5) =9.148×109 pfu/L. 重组效率的计算表明,3种连接方式的重组效率(94.77%, 97.66%, 93.88%)均高于80%,满足构建质量文库的需要. M: DNA Marker DL2000;1: 屋尘螨cDNA第2链.图3屋尘螨mRNA反转录合成的cDNA第2链表13种连接方式在3种稀释度时的噬菌斑数2.3扩增文库的滴度稀释度为10-5的3个平板内噬菌板最为清晰可数,以此计算扩增文库滴度,pfu/L= [(462/5+637/10+1455/20)/3]×1085×103=7.628×1012(表2). 表2稀释度为10-5的3个平板计算结果2.4文库的PCR鉴定根据载体克隆位点两端的序列设计引物进行PCR鉴定,结果显示,所选的12个噬菌体均含有重组cDNA,长度在400 bp以上的有2个,500 bp左右的有2个,750 bp以上的有2个,1000 bp以上的有2个,2000 bp及其以上的有4个,平均1.63 kb(图4).3讨论尘螨隶属于节肢动物门(Arthropoda)、蛛形纲(Arachnida)、蜱螨亚纲(Acari),广泛存在于人类生活和工作环境中,其排泄物、代谢物及螨体均具较强的M: DNA Marker DL2000;1~12: 含重组cDNA 的噬菌体PCR产物.图4屋尘螨cDNA文库的PCR鉴定变应原性,可引起螨性哮喘、异位性皮炎、过敏性鼻炎等Ⅰ型变态反应性疾病. 法国N. Roche(2000)估计约60%~100%的哮喘患者对尘螨过敏[3]. 国内学者用粉尘螨浸液对哮喘患者进行皮肤挑刺试验,47%~92.11%的成人患者呈阳性反应,51.64%~78.85%患儿对尘螨过敏[4]. 《哮喘全球防治策略》指出,哮喘每年造成的直接或间接损失达100亿美元[5]. 随着工业化的发展,人民生活水平的提高,此类疾病的发生不但不下降反而呈上升趋势[6].特异性免疫治疗被认为是目前唯一Ⅰ型变态反应性疾病病因治疗方法,即通过逐渐增加、反复皮下注射特异性变应原,提高患者对特异性变应原的免疫耐受力,调节患者细胞免疫功能,增强Th1反应,抑制Th2反应,并产生高水平的IgG抗体阻断变应原结合到IgE,以提高患者对特异性变应原的免疫耐受力,达到再次暴露于特异性变应原后不发病或虽发病、但症状明显减轻的目的[1-2,7-9].尘螨变应原成份复杂,约有30余种,现已提纯出16类变应原. 目前,临床主要采用尘螨变应原粗提浸液免疫治疗哮喘患者,由于变应原浸液包含成分较复杂,如存在变应原、非过敏性或毒性蛋白及其他成分,所以很难进行变应原标准化,且在治疗中长期使用易导致严重IgE介导的过敏反应,如可发生红晕、肿胀、硬结、坏死等局部反应和休克、喉头水肿、支气管痉挛、荨麻疹、血管性水肿、全身性红斑等全身反应[1-2,7-9],因此,提高变应原纯度是减少免疫治疗副反应发生的有效途径. 1998年,WHO发布的关于免疫治疗的指导文件中强调,变应原的质量对变态反应疾病的诊断和治疗至关重要,用于免疫治疗的变应原应该是纯品而不宜为粗制浸液[7]. 但是,尘螨变应原主要存在于螨的排泄物和皮壳中,采用生物化学方法提纯尘螨变应原,耗时长,过程繁琐,成本较高,且不能从根本上提高变应原的纯度、避免免疫治疗中副反应的发生.本研究提取屋尘螨总RNA,反转录合成cDNA第1链并用置换法合成双链cDNA,用Smart方法构建全长cDNA表达文库,为进一步研究尘螨变应原基因信息、通过基因工程技术制备重组变应原疫苗用于尘螨变态反应性疾病的诊断和治疗奠定基础.容量和重组噬菌体中插入的cDNA片段长度是评价基因文库的重要指标. 当一个cDNA文库的容量为4.6×104~4.6×105时,得到所需克隆的概率为99%,就可以筛选出低丰度的cDNA. 本研究所构建的cDNA文库未扩增时滴度为9.148×106,文库扩增后滴度为7.628×109,完全满足要求. 为了鉴定重组体中插入的cDNA片段的长度,本研究从文库中随机挑选12个噬菌斑进行PCR鉴定,电泳结果显示此11个噬菌斑中均含有重组的cDNA,片段长度均在400 bp以上,提示我们所构建的cDNA文库质量较好.【参考文献】[1]Norman PS. Immunotherapy:19992004 [J]. J Allergy Clin Immunol, 2004,113(6):1013-1023.[2]Greenberger PA. Immunotherpy update: mechanisms of action [J]. Allergy Asthma Proc, 2002,23(6):373-376.[3]Roche N, Chinet TC,Belouchi NE, et al. Dermatophagoides pteronyssinus and bioelectric properties of airway epithelium: role of cysteine proteases [J]. Eur Respir J, 2000,16(2):309-315.[4]崔玉宝,何珍,李朝品. 居室环境中螨类的孳生与疾病[J]. 环境与健康杂志,2005,22(6):500-502.[5]NHLBI workshop report. Global strategy for asthma management and prevention [M]. New York: NIH Publication, 2002: 95-3659.[6]Isolauri E, Huurre A, Salminen S, et al. The allergy epidemic extends beyond the past few decades [J]. Clin Exp Allergy, 2004,34(7):1007-1010.[7]Bousquet J, Lockey RF, Malling HJ. Allergen immunotherapy: therapeutic vaccines for allergic diseases [J]. Allergy, 1998,53(Suppl):1-42.[8]Ramirez NC, Ledford DK. Immunotherapy for allergic asthma [J]. Med Clin North Am, 2002,86(5):1091-1112.[9]Dinakar C, Portnoy JM. Allergen immunotherapy in the prevention asthma [J]. Curr Opin Allergy Clin Immunol, 2004,4(2):131-136.。
基于GEO数据库奶牛乳房炎致病菌差异表达基因的生物信息学分析张雅昆;王亭亭;谭晶;杨敏;曾艳荣;谭承建【期刊名称】《中国奶牛》【年(卷),期】2024()5【摘要】试验旨在筛选与奶牛乳房炎致病菌相关的差异表达基因(DEGs),分析其作用机理。
在基因表达数据库(GEO)中筛选得到与奶牛乳房炎致病菌密切相关的基因芯片数据集GSE25413进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)分析。
联合String数据库在Cytoscape软件中构建出蛋白质互作网络(PPI)并筛选出主要的关键基因。
结果显示,在GSE25413数据集中共筛选得到127个差异表达基因,其中上调表达基因49个,下调表达基因78个。
GO分析共得到450条功能注释,主要涉及炎症反应、缺氧反应等;KEGG通路富集共得到446条信号通路,主要涉及癌症途径、肿瘤坏死因子等信号通路。
PPI互作网络共筛选得到IL-6、IL-1B、VEGFA、CXCL8等10个关键基因。
经筛选获得的关键基因可作为潜在的分子标志物用于奶牛乳房炎的早期诊断和临床治疗靶点的选择,为奶牛乳房炎的防治提供参考。
【总页数】5页(P21-25)【作者】张雅昆;王亭亭;谭晶;杨敏;曾艳荣;谭承建【作者单位】贵州民族大学民族医药学院;贵州民族大学化学工程学院【正文语种】中文【中图分类】S823.3【相关文献】1.基于GEO数据库的2型糖尿病骨骼肌差异表达基因生物信息学分析2.基于GEO 数据库的膀胱癌差异表达基因的生物信息学分析3.基于GEO数据库的狼疮肾炎生物信息学分析及差异表达基因筛选4.基于GEO和TCGA数据库对肺腺癌差异表达基因的生物信息学分析5.基于GEO数据库的硒酵母影响肉鸡肝脏差异表达基因的生物信息学分析因版权原因,仅展示原文概要,查看原文内容请购买。
350Genome Informatics14:350–351(2003) Multiscale Bootstrap Analysis of Gene Networks Based on Bayesian Networks and Nonparametric Regression Takeshi Kamimura1Hidetoshi Shimodaira1Seiya Imoto2kamimur1@is.titech.ac.jp shimo@is.titech.ac.jp imoto@ims.u-tokyo.ac.jp SunYong Kim2Kousuke Tashiro3Satoru Kuhara3 sunk@ims.u-tokyo.ac.jp ktashiro@grt.kyushu-u.ac.jp kuhara@grt.kyushu-u.ac.jpSatoru Miyano2miyano@ims.u-tokyo.ac.jp1Department of Mathematical and Computing Sciences,Tokyo Institute of Technology, Ookayama,Meguro,Tokyo152-8552,Japan2Human Genome Center,Institute of Medical Science,University of Tokyo,4-6-1Shi-rokanedai,Minato-ku,Tokyo108-8639,Japan3Guraduate School of Genetic Resources Technology,Kyushu University,6-10-1 Hakozaki,Higashi-ku,Fukuoka812-8581,JapanKeywords:multiscale bootstrap,Bayesian network,nonparametric regression,microarray1IntroductionThe Bayesian network[2,3,4]is a very powerful tool for estimating the gene network from microarray expression profiles.The estimated network is often susceptible to statistical sampling error,and thus Imoto et al.[3,4]evaluated the reliability of estimation by calculating the bootstrap probabilities for the edges connecting genes.The bootstrap method,however,underestimates the probability values, and it sometimes leads to false“discovery”.For improving the accuracy of the bootstrap probability, we propose the application of the newly developed multiscale bootstrap[5,6]to the gene network estimation.2Method2.1Nonlinear Bayesian Network ModelIn the estimation of a gene network,Imoto et al.[3,4]proposed the nonlinear Bayesian network model for caputuring even nonlinear relationship among genes by using the nonparametric regression model.The criterion,BNRC,was newly introduced for evaluating the estimated gene network from Bayes approach.The details of the nonlinear Bayesian network model are described in[4].2.2Bootstrap and Multiscale Bootstrap Edge IntensityWe measure the intensity of the edge by the bootstrap and multiscale bootstrap method.In the multi-scale bootstrap method,we generate replicates X∗n =(x∗1,...,x∗n )for several n values from the orig-inal gene expression data X n=(x1,...,x n).In other words,we alter the number of arrays from n to n in the bootstrap replication.We will take n values with n /n=0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3,1.4, in the example shown later.We callτ=Multiscale Bootstrap Analysis of Gene Networks351 In the normal boostrap method,we take n =n,and the bootstrap edge intensity can be written as BP ij(1).In the multiscale bootstrap method,we calculate BP ij(τ)with severalτvalues by altering n /n.Then we calculate the multiscale bootstrap edge intensity between gene i and gene j from BP ij(τ) values.According to the statistical geometric theory of Efron et al.[1]and Shimodaira[5],the very accurate probability value is expressed as MS ij=1−Φ(d ij−c ij)using geometric quantities d ij and c ij,whereΦdenote the ditribution function of standard normal distribution.We estimate d ij and c ij byfitting the theorerical curve BP ij(τ)=1−Φ(d ij/τ+c ijτ)to the observed BP ij(τ)values calculated by the multiscale bootstrap method.3Result Table1:Gene pairs with high multiscalebootstrap intensities.We applied the proposed method to the S.cerevisiae geneexpression data.We focused on9genes,which are involvedor putatively involved in the heat shock response.We tookB=10000.Figure1is the resulting network.The mul-tiscale bootstrap edge intensity MS ij is shown by the linewidth,and the number next to the line is the degree of con-fidence of the edge direction.The observed BP ij(τ)valuesand thefitted curves are shown for some of the edges in Fig-ure2.Table1shows the gene pairs with high multiscalebootstrap intensities.Figure1:The resulting network.-3-2-112bp=0.047ms=0.075bp=0.284ms=0.426bp=0.596ms=0.592bp=0.972ms=0.998MCM1YRO2HIG1YRO2SSA1HSP104SSA1MCM1Figure2:The curvefitting to the observedBP ij(τ)values.References[1]Efron,B.,Halloran,E.,and Holmes,S.,Bootstrap confidence levels for phylogenetic trees,Proc.A,93:13429–13434,1996.[2]Friedman,N.,Linial,M.,Machman,I.,and Pe’er,D.,Using Bayesian networks to analyze ex-pression data,p.Biol.,7:601–620,2000.[3]Imoto,S.,Goto,T.,and Miyano,S.,Estimation of genetic networks and functional structuresbetween genes by using Bayesian networks and nonparametric regression,Pac.Symp.Biocomput., World Scientific,7:175–186,2002.[4]Imoto,S.,Kim,S.,Goto,T.,Aburatani,S.,Tashiro,K.,Kuhara,S.,and Miyano,S.,Bayesiannetwork and nonparametric heteroscedastic regression for nonlinear modeling of genetic network, J.Bioinformatics and Computational Biology,1(2):231–252,2003.[5]Shimodaira,H.,An approximately unbiased test of phylogenetic tree selection,Systematic Biol-ogy,51:492–508,2002.[6]Shimodaira,H.and Hasegawa,M.,CONSEL:for assessing the confidence of phylogenetic treeselection,Bioinformatics,17:1246–1247,2001.。