Identification of differentially expressed genes in cucumber

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Mol Biol Rep-2012

Gene expression proﬁling of Sinapis alba leaves under drought stress and rewatering growth conditions with Illumina deep sequencingCai-Hua Dong •Chen Li •Xiao-Hong Yan •Shun-Mou Huang •Jin-Yong Huang •Li-Jun Wang •Rui-Xing Guo •Guang-Yuan Lu •Xue-Kun Zhang •Xiao-Ping Fang •Wen-Hui WeiReceived:20June 2011/Accepted:17December 2011ÓSpringer Science+Business Media B.V.2011Abstract Sinapis alba has many desirable agronomic traits including tolerance to drought.In this investigation,we performed the genome-wide transcriptional proﬁling of S.alba leaves under drought stress and rewatering growth conditions in an attempt to identify candidate genes involved in drought tolerance,using the Illumina deep sequencing technology.The comparative analysis revealed numerous changes in gene expression level attributable to the drought stress,which resulted in the down-regulation of 309genes and the up-regulation of 248genes.Gene ontology analysis revealed that the differentially expressedgenes were mainly involved in cell division and catalytic and metabolic processes.Our results provide useful infor-mation for further analyses of the drought stress tolerance in Sinapis ,and will facilitate molecular breeding for Brassica crop plants.Keywords Sinapis alba ÁDrought stress ÁIllumina sequencing ÁGene expression ÁDrought tolerance genesIntroductionDrought is a meteorological occurrence in practice which displays zero rainfall for a long time,it ﬁrstly causes the depletion of moisture in soil,and ﬁnally works the decrease of water potential of plant tissues for water deﬁcit [1].In the light of the agricultural point of view,its operating deﬁnition would be the insufﬁcient of water availability from the soil during the life cycle of crop plants,which restricts a full exertion of genetic potential of the plants.At present,it is one of the grand restrictive factors in agri-cultural production by inhibiting crop plants reaching the theoretical maximum yield genetically determined.Drought stress is one of the most common stress factors decreasing crop output.Plants changes adaptively in cell morphology,gene expression,physiological and bio-chemical metabolisms to mitigate the damage caused by drought stress,and form a variety of drought stress adap-tation in aspects of growth habit and physiological and biochemical habits during long-term interaction with the environment and during evolution [2].As plants experience drought,many drought stress response genes are induced and a large number of speciﬁc proteins are produced to regulate physiological and biochemical and metabolic changes of plants cooperatively.Cai-Hua Dong,Chen Li and Xiao-Hong Yan contributed equally to this work.Electronic supplementary material The online version of this article (doi:10.1007/s11033-011-1395-9)contains supplementary material,which is available to authorized users.C.-H.Dong ÁX.-H.Yan ÁS.-M.Huang ÁL.-J.Wang ÁR.-X.Guo ÁG.-Y.Lu ÁX.-K.Zhang (&)ÁX.-P.Fang (&)ÁW.-H.Wei (&)Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences,Key Laboratory of Oil Crop Biology and Genetic Breeding of the Ministry of Agriculture,Wuhan 430062,China e-mail:whwei@ X.-P.Fange-mail:xpfang@ X.-K.Zhange-mail:zhangxk@C.LiCollege of Food Science and Technology,Agricultural University of Hebei,Baoding 071001,China J.-Y.HuangDepartment of Bioengineering,Zhengzhou University,Zhengzhou 450001,ChinaMol Biol RepDOI 10.1007/s11033-011-1395-9The mechanisms of drought tolerance in plants have already been studied at the gene level.Gene expression proﬁles of plant tissuesﬂuctuated under drought stress,this stress response made plants obtain drought tolerance.The genes related to drought tolerance can be grouped into two categories,theﬁrst one encode the special functional pro-teins directly involving drought tolerance,including pro-tective factors,osmotic adjustment proteins,ion tunnel proteins,ion transport proteins,oxidation-resistant pro-teins,etc.The second one encode those regulatory proteins, such as transcription factors,protein kinases,protein phos-phatases,calmodulin-binding proteins,etc.The expression levels of these genes are up-regulated or down-regulated during drought stress,which regulates the intra-and intercellular environments,and plants exhibit the trait tolerant to drought[3].With Arabidopsis[4],rice[5,6]and other plant genome sequencing completed,the research of genes of plants have entered the era of functional genomics,which studies not only the structure and function of genes,but also the temporal and spatial expression of plant genes and the regulation network.For a comprehensive understanding of the genetic basis of drought tolerance,exploring drought tolerance genes,cultivating resistant and water saving species,it is signiﬁcant to discuss the sorts of genes induced by drought stress by methods of molecular biol-ogy,to construct the expression proﬁling of drought-related genes,to acknowledge the metabolism mechanism of plants under drought stress condition from the overall level.Arabidopsis thaliana is the model species used to study the mechanisms of drought tolerance and used to clone the genes that might code for the mechanisms leading to the tolerance to drought,several hundreds of drought-toler-ance-related genes have been identiﬁed from it[7–9]. There are four pathways for these genes to respond to drought stress,of which two pathways are dependent to abscisic acid(ABA),and two are not[8].Another good material for drought stress research is Thellungiella sal-suginea.Wong et al.[10]have studied gene expression of leaf tissue of T.salsuginea under drought stress using cDNA microarray,and revealed new abiotic stress response mechanisms in T.salsuginea.Sinapis alba(white mustard)is a crucifer classiﬁed into the genus Sinapis which includes about ten grass species.It is now widespread worldwide,although it probably originated in the Mediterranean region.It has many desirable agronomic traits including tolerance to drought[11,12].Until now,however,no research has been performed on the molecular mechanisms of drought tolerance in S.alba.It is necessary to analyze the gene expression proﬁle of white mustard under drought stress. In recent years,various techniques,such as cDNA microarray(or cDNA chip),SSH and RT-PCR,were thought to be a powerful tool in the study of gene expression proﬁles induced by abiotic stress in plants [7,8,13–15].However,these techniques present some defects.They are laborious,rely on a prior knowledge of the sequence,or suffer from high noise or cross-hybrid-ization problem.With the Illumina sequencing(formerly Solexa sequencing)technique developed recently,this situation has changed,and it can execute quantitative and qualitative analyses of gene expression at low cost,even if the genome of a species have not been noted,and the Illumina sequencing data are highly replicable,with rel-atively little technical variation,it may sufﬁce to sequence each mRNA sample only once[16,17].In this study,aﬁne comparison of mRNA expression levels of S.alba leaves under rewatering growth conditions (SaW-A)and drought stress conditions(DL-B)was per-formed based on Illumina sequencing for theﬁrst time. These results provide novel information for studying the molecular mechanisms of drought tolerance in S.alba, since a number of candidate genes for drought tolerance were identiﬁed.Materials and methodsPlant materials and stress treatmentsPlants growth and stress treatment were performed as described by Wong et al.[10].Plants of S.alba were grown in controlled environments with a day/night temperature regime of22°C/10°C.An irradiance of250l mol photons m-2s-1over a21-h daylength was provided.When all the plants were4weeks old,some plants,named sample Saw-A,were subjected to drought stress treatment until they wilted visibly(3–4days),and then rewatered and allowed to recover.At the moment when sample Saw-A plants were rewatered,other plants,named sample DL-B,were sub-jected to drought treatment until they wilted visibly.0.1g leaves ofﬁve individual plants for each of Saw-A and DL-B were synchronously harvested8h after the lights came on in the growth chamber.Then the equal leaf samples fromﬁve individual plants were mixed together for RNA extraction of Saw-A and DL-B,respectively. Processing samples for sequencingTotal RNA was extracted using the TRIzol reagent (Invitrogen).After precipitation,RNA was puriﬁed with Qiagen’s RNeasy kit with on-column DNase digestion according to the manufacturer’s instructions.Puriﬁed RNA samples were dissolved in diethylpyrocarbonate-treated H2O,and the concentration determined spectroscopically. The quality of the RNA was assessed on1.0%denaturingMol Biol Repagarose gels in combination with the Bioanalyzer2100 (Agilent).Illumina sequencingIllumina sequencing was completed at Beijing Genomics Institute,with the use of an Illumina genome analyzer(San Diego,CA).Initially,we used poly(T)oligo-attached magnetic beads to isolate poly(A)mRNA from total RNA sample.First-and second-strand cDNA synthesis were performed while the RNA was bound to the beads.While on the beads,samples were digested with NlaIII to retain a cDNA fragment from the most30CATG to the poly(A)-tail.Subsequently,the GEX adapter1was ligated to the free50end of the RNA,and a digestion with MmeI was performed,which cuts17bp downstream of the CATG site.At this point,the fragments detach from the beads. After dephosphorylation and phenol extraction,the GEX adapter2was ligated to the30end of the tag.Finally,the short cDNA fragments were prepared for Solexa sequenc-ing on an Illumina genome analyzer(San Diego,CA), using the manufacturer’s protocol and reagents of the genomic DNA sequencing sample prep kit.The Illumina/Solexa approach involved sequencing of cDNA fragments,followed by counting of the number of times a particular fragment was observed.The terminators were labeled withﬂuorescent compounds of four different colors to distinguish among the different bases at the given sequence position.The template sequence of each cluster was deduced by reading the color at each successive nucleotide addition step.Image analysis and base calling were performed using the Illumina Pipeline,where high-throughput short-read sequence tags were obtained after purityﬁltering.This was followed by sorting and counting the unique tags.Sequence annotation,comparison and functionalclassiﬁcationThe unigenes of Sinapis,Arabidopsis and Brassica were used as a reference sequence to align and identify the sequencing reads.To map the reads to the reference,the alignments and the candidate gene identiﬁcation procedure were conducted using the mapping and assembly with qualities software package MAQ[18].Differentially expressed genes between two samples were analysed according to the digital gene expression detection methods reported by Audic and Claverie[19].To categorize transcripts by putative function,we have utilized the gene ontology(GO)classiﬁcation scheme[20]. GO provides a dynamic controlled vocabulary and hierar-chy that uniﬁes descriptions of biological,cellular and molecular functions across genomes.ResultsIllumina sequencing and gene annotationWe obtained4,123,307tags in sample Saw-A(accession: SRR353366)and4,340,054tags in sample DL-B(acces-sion:SRR352383)through Illumina sequencing(Fig.1a), the original data have been placed in public databases(http:// /sra/?term=SRA047029).204,279dis-tinct tags were obtained in sample Saw-A after eliminating low quality tags and single copy tags,and217,599distinct tags were obtained in sample DL-B(Fig.1b).Though some tag copy number is far more than100,this is not what we are interested in,because two samples may both have high expression genes.In the present study,our research focuses on those tags that have obvious differences between the two samples.Though different copy number of distinct tags displayed very similar distribution patterns on the whole,speciﬁc distinct tags are quite different in the two samples(Fig.2). In Fig.2,the regions on the left of the peak zones denote the distinct tag copy number of Saw-A are aboveﬁvetimesMol Biol Repthe DL-B,these tags are down-regulation under drought stress.The regions on the right of the peak zones represent the distinct tag copy number of DL-B are above ﬁve times the Saw-A,these tags are up-regulation under drought stress.The peak zones differ in ﬁve times between two samples.Drought tolerance genes are probably found from these tags that have apparent change in expression.Through the comparison with the open reference sequences of Arabidopsis and Brassica ,all of the distinct tags were annotated.The expression levels of these annotated genes were quantitatively analyzed as their corresponding tag copy number,and they were classiﬁed into up-regulation,down-regulation and no signiﬁcance change genes.Although this was a preliminary analysis of white mustard short-read data,we have gained valuable infor-mation,which lead to the identiﬁcation of differentially expressed genes between Saw-A and DL-B samples.Figure 3shows the distribution of differentially expressed genes,the dexter and upper regions with dots reveal those genes with markedly expression difference,and the rest regions shows those genes with no obvious expression diversity.The upper region with dots displays the up-reg-ulated genes of sample DL-B after stress,248genes could be annotated.The dexter region with dots displays the down-regulated genes of sample SaW-A after stress,309genes could be annotated.More detailed information including data selection is provided in Supplementary Table S1.The unigenes of Arabidopsis and Brassica were used as a reference sequence,for Arabidopsis has good basis to study drought stress as model organism,and the application of Brassica will help to found drought-related genes for molecular breeding.GO classiﬁcation and annotation of differentially expressed genesThese differentially expressed genes may involve different functions,and their function annotation is very helpful for us to roundly analyze the changes of the gene expression proﬁles under drought stress.Then GO classiﬁcation of the differentially expressed genes was performed as their up-and down-regulation changes (Fig.4).A gene can be classiﬁed into different functional gene type,so the gene number shown in the chart is more than the total number of the differentially expressed gene.The GO classiﬁcation results showed that there were not down-regulated genes in the classiﬁcation involved in enzyme regulator and multi\-organism processes,and up-regulated genes were not found in the classiﬁcation with functions of envelope and auxiliary transport protein.These results may reﬂect that up-and down-regulated genes participate in different metabolic pathways and are involved in different regulation mechanisms.The differentially expressed genes were mainly involved in the cell division and catalytic and metabolic processes.According to the annotation results,parts of the up-and down-regulated genes are related to the cDNAs and unigenes expressed under both biotic and abiotic stress,which shows that there may be some the same response mechanism for diverse stress.At the same time,there are a lot of unknown tags,some important functional genes will be found in them,especially those tags that have obvious change in expression level under stresscondition.Fig.2Distribution of ratio of distinct tag copy number between twolibrariesFig.3Down-regulation and up-regulation of gene expression in S.alba under drought stress conditionMol Biol RepDiscussionThe tolerance to biotic and abiotic stresses like low or high temperature,drought,salt and disease factors in plants is a defense response involving multiple genes.Drought stress causes a great change of gene expression proﬁle,deep understanding of the cross-talk between the transcription factor of different pathways will help the improvement of the integrated characters of crops,it is important to study the changes of gene expression proﬁles from the overall level.The ﬁne comparative analysis of mRNA expression levels of S.alba leaves under drought stress and rewatering growth conditions was performed by Illumina deep sequencing method in the present study.When the plants wilted,not only the expressions of the genes related to drought stress were changed,but also the expressions of partial genes related to plant growth and development were changed.When the wilting plants were re-watered,prob-ably the expressions of the genes related to plant growth and development still maintained the changed levels at the early stage.In addition,RNA-changes are not strictly correlated to protein levels,osmotic relations or membrane characteristics [21].So we could rightly screen the genes related to drought stress when the re-watering plants and wilting plants were used as the tested materials.Illumina sequencing,different from Sanger sequence method,can provide giant sequencing data with saving time and lower cost.It is also helpful for the study of molecular breeding,evolution and development,and stress response to envi-ronment in crop plants.In the present study,557annotated genes and a large number of no matched tags were found to be involved in drought stress response,some genes encode signaling components,transcription regulators or other proteins,these proteins are necessary for cell growth and develop-ment under drought stress [22].These results indicate that it is effective to analyze the gene expression proﬁles under drought stress by high-throughput sequencing technologies and many novel tags have been found,however,more reliable results in the present study could be obtained with biological repetition experiments.Lee et al.[23]has ana-lyzed 24,000unigenes using a B.rapa oligo microarray and many unigenes were found to be involved in the abiotic stresses,however,this technology relied on a prior knowledge of the sequences.It is now hypothesized that halophytes use salt-tolerance effectors and regulatory pathways very similar to those in glycophytes and that subtle differences in their regulation can account for large variations in salt sensitivity [24–26],other researchers have begun to test this hypothesis [27].Plants have many common response mechanisms under abiotic stress such as salt stress and drought stress.Molecular regulation mechanisms of salt stress and drought stress can be found through comparative analysis and genetic function analysis between halophytes and glyco-phytes,and new functions will be found in the genes that have been identiﬁed in glycophytes.Arabidopsis ,a relative of white mustard,was annotated completely in genomics,its genome was used as a reference to ﬁnd some known and unknown functional genes related to drought stress in white mustard as possible as we can do.At the same time,transcriptome analysis using high-throughput short-read sequencing need not be restricted to the genome of model organisms [28,29].The gene expression proﬁles (Supplementary Table S1)showed that the annotated genes could be grouped into two categories,the ﬁrst one encode protective proteins,such as oxidoreductase,the second one encode regulatory proteins,such as transcription factors.In the up-regulated genes,theFig.4Percentagerepresentation of GO mappings for drought-tolerance correlated clustersMol Biol RepFATTY ACID REDUCTASE1gene(AT5G22500.1)has the fatty-acyl-CoA reductase activity involved in salt stress,it is grouped into the protective protein.Another gene AT4G20890.1has GTPase activity,it is grouped into the regulatory protein.A lot of the annotated genes have not been found to be involved in drought tolerance,their function need to be identiﬁed in the future research.Brassica plants are also the relatives of white mustard, 329of the557genes related to drought stress were anno-tated as the reference sequences of Brassica,these329 genes include plenty of genes with unknown function.With the deep research on gene function we will know more about these genes in the role of drought tolerance,and at last those drought tolerance genes can be applied to the genetic improvement of Brassica crop plants with mass transforming.Acknowledgments This work was supported by the National Nat-ural Science Foundation of China(30671312),the Natural Science Foundation of Hubei Province(2008CDA083and2009CDB191),the Natural Science Foundation of Henan Province(114100510013),the Chenguang Program of Wuhan City(201050231022),the Interna-tional Science and Technology Cooperation Item(S2012GR0080), and the Science and Technical Innovation Project of Hubei Province. 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differentially expressed genes

Inﬂuence of the PMT gain setting for the identiﬁcation ofdifferentially expressed genes in microarray experimentsS´e bastien D´e jean1,Abdel Belkorchia2,C´e cile Militon2,Muriel Bonnet2,Olivier Gonc¸alves2,andPierre Peyret21Laboratoire de Statistique et Probabilit´e s,UMR CNRS-UPS-INSA5583Universit´e Paul Sabatier,Toulouse3,France2Laboratoire de Biologie des Protistes,UMR CNRS-UBP6023Universit´e Blaise Pascal,Clermont2,FranceMain Thematics:Transcriptomics,expression.Technical Fields:RNA,classiﬁcation,regulation,statistics.Keywords:microarray,PMT gain,clustering,factorial methods.High throughput techniques like DNA microarray are formidable tools to understand original molec-ular mechanisms through gene expression proﬁling.The study of two different mRNA populations typically consists in labelling the transcripts with differentﬂuorochromes and challenging them in competitive hybridisation with a single slide presenting thousands of speciﬁc probes.Remainingﬂu-orescence signal is then measured by confoncal laser scanner for both markers and difference in gene expression calculated from measured signals.However,this technique suffers from too much vari-ability due to its multiple processing steps precluding then straightforward interpretable results.Therefore numerous strategies have been developed in order to correct experimental biases,opti-mising for instance probes selection and within slide localization[7],planning experimental replica-tions with or without inversion of dyes or targets[4,2],or using variousﬂuorescence signal normal-ization algorithms[8].Recent work has emphasized the importance of properly tuning the power of the scanner photomultiplicator(PMT).Indeed it has been demonstrated that PMT settings can inﬂu-ence ratio experimental estimation,dynamical range extension[1]or saturation of highly expressed genes[5,3].For those reasons,it is often recommended to scan microarray slides atﬁxed gain settings under which the linearity between concentration and intensity is optimised.Instead of working withﬁxed PMT settings,we propose to take advantage of using multiple gain settings to improve detection of differentially expressed genes.Indeed,low-intensity spots cannot be analysed under the same PMT settings than the majority of the other individuals.Our approach consists in gradually increasing the PMT gain in order to improve the signal-to-noise ratio of homo-geneous labelled targets groups.The strategy is applied on an original dataset where two experimental conditions are studied: healthy vs infested human cells.Data expression are measured for about3500genes at7different PMT gains between5and60dB.An exploratory analysis,combining hierarchical clustering and factorial methods,highlights the gain inﬂuence.Then,identiﬁcation of differentially expressed genes is performed for each gain value;resulting clusters are compared.Results are completed considering gene expression as a function of gain as this can be done for time-course or dose-response experiments [6].References[1]H.Bengtsson,G.Jonsson,J.Vallon-Christersson,Calibration and assessment of channel-speciﬁc biasesin microarray data with extended dynamical range.BMC Bioinformatics.5:177,2004.[2]K.Dobbin,J.H.Shih,R.Simon,Statistical design of reverse dye microarrays.Bioinformatics,19:803-810,2003.[3]L.E.Dodd,E.L.Korn,L.M.McShane,G.V.Chandramouli,E.Y.Chuang,Correcting log ratios for signalsaturation in cDNA microarrays.Bioinformatics,20:2685-93,2004.[4]M.K.Kerr,G.A.Churchill,Experimental design for gene expression microarrays.Biostatistics,2:183-201,2001.[5]H.Lyng,A.Badiee,D.H.Svendsrud,E.Hovig,O.Myklebost,T.Stokke,Profound inﬂuence of mi-croarray scanner characteristics on gene expression ratios:analysis and procedure for correction.BMC Genomics,5:10,2004.[6]S.D.Peddada,E.K.Lobenhofer,L.Li,C.A.Afshari,C.R.Weinberg,D.M.Umbach,Gene selection andclustering for time-course and dose-response microarray experiments using order-restricted inference.Bioinformatics,19:834-841,2003.[7]S.Rimour,D.Hill,iton,P.Peyret,GoArrays:highly dynamic and efﬁcient microarray probe de-sign.Bioinformatics,21:1094-103,2005.[8]Y.H.Yang,S.Dudoit,P.Luu,D.M.Lin,V.Peng,J.Ngai,T.P.Speed,Normalization for cDNA microarraydata:a robust composite method addressing single and multiple slide systematic variation.Nucleic Acids Res.,30:e15,2002.。

Cahn-Hilliard_方程的一个超紧致有限差分格式

第38卷第1期2024年1月山东理工大学学报(自然科学版)Journal of Shandong University of Technology(Natural Science Edition)Vol.38No.1Jan.2024收稿日期:20221209基金项目:陕西省自然科学基金项目(2018JQ1043)第一作者:栗雪娟,女,lxj_zk@;通信作者:王丹,女,1611182118@文章编号:1672-6197(2024)01-0073-06Cahn-Hilliard 方程的一个超紧致有限差分格式栗雪娟,王丹(西安建筑科技大学理学院,陕西西安710055)摘要:研究四阶Cahn-Hilliard 方程的数值求解方法㊂给出组合型超紧致差分格式,将其用于四阶Cahn-Hilliard 方程的空间导数离散,采用四阶Runge-Kutta 格式离散时间导数,将二者结合得到四阶Cahn-Hilliard 方程的离散格式,并给出了该格式的误差估计㊂通过编程计算得到其数值解,并与精确解进行对比,结果表明本文的数值方法误差小,验证了所提方法的有效性和可行性㊂关键词:四阶Cahn-Hilliard 方程;组合型超紧致差分方法;四阶Runge-Kutta 方法;误差估计中图分类号:TB532.1;TB553文献标志码:AA supercompact finite difference scheme for Cahn-Hilliard equationsLI Xuejuan,WANG Dan(School of Science,Xiᶄan University of Architecture and Technology,Xiᶄan 710055,China)Abstract :A numerical method for solving the fourth order Cahn-Hilliard equation is studied.The combi-national ultra-compact difference scheme is given and applied to the spatial derivative discretization of the fourth order Cahn-Hilliard equation.The fourth-order Runge-Kutta scheme is used to discrete time deriv-atives.The discrete scheme of the fourth order Cahn-Hilliard equation is obtained by combining the two methods,and the error estimate of the scheme is given.Finally,the numerical solution is obtained by programming and compared with the exact solution.The results show that the numerical method in this paper has a small error,verifying the effectiveness and feasibility of the proposed method.Keywords :fourth order Cahn-Hilliard equation;combinational supercompact difference scheme;fourthorder Runge-Kutta;error estimation㊀㊀本文考虑的四阶Cahn-Hilliard 方程为u t -f u ()xx +ku xxxx =0,x ɪ0,2π[],t >0,u x ,0()=u 0x (),x ɪ0,2π[],u 0,t ()=0,u 2π,t ()=0,t >0,ìîíïïïï(1)式中:求解区域为0,2π[],且kn ȡ0;f u ()为光滑函数;u 0x ()表示t =0时刻的初值;u t 表示u 关于时间t 求偏导数,u t =∂u∂t;f u ()xx表示f u ()关于x求二阶偏导数,f u ()xx=∂2f u ()∂x 2;u xxxx 表示u 关于x 求四阶偏导数,u xxxx=∂4u∂x4;u 是混合物中某种物质的浓度,被称为相变量㊂1958年,Cahn 和Hilliard 提出Cahn-Hilliard 方程,该方程最早被用来描述在温度降低时两种均匀的混合物所发生的相分离现象㊂随着学者对该方程的研究越来越深入,该方程的应用也越来越广泛,特别是在材料科学和物理学等领域中有广泛的应用[1-3]㊂㊀Cahn-Hilliard 方程的数值解法目前已有很多研究,文献[4]使用了全离散有限元方法,文献[5]使用了一类二阶稳定的Crank-Nicolson /Adams-Bashforth 离散化的一致性有限元逼近方法,文献[6-7]使用了有限元方法,文献[8]使用了不连续伽辽金有限元方法,文献[9]使用了Cahn-Hilliard 方程的完全离散谱格式,文献[10]使用了高阶超紧致有限差分方法,文献[11]使用了高阶优化组合型紧致有限差分方法㊂综上所述,本文拟对Cahn-Hilliard 方程构造一种新的超紧致差分格式,将空间组合型超紧致差分方法和修正的时间四阶Runge-Kutta 方法相结合,求解Cahn-Hilliard 方程的数值解,得到相对于现有广义格式精度更高的数值求解格式,并对组合型超紧致差分格式进行误差估计,最后通过数值算例验证该方法的可行性㊂1㊀高阶精度数值求解方法1.1㊀空间组合型超紧致差分格式早期的紧致差分格式是在Hermite 多项式的基础上构造而来的,Hermite 多项式中连续三个节点的一阶导数㊁二阶导数和函数值的数值关系可以表示为ð1k =-1a k f i +k +b k fᶄi +k +c k fᵡi +k ()=0㊂(2)1998年,Krishnan 提出如下紧致差分格式:a 1fᶄi -1+a 0fᶄi +a 2fᶄi +1+hb 1fᵡi -1+b 0fᵡi +b 2fᵡi +1()=1h c 1f i -2+c 2f i -1+c 0f i +c 3f i +1+c 4f i +2(),(3)式中:h 为空间网格间距;a 1,a 0,a 2,b 1,b 0,b 2,c 1,c 2,c 0,c 3,c 4均表示差分格式系数;f i 表示i 节点的函数值;fᶄi 和fᵡi 分别表示i 节点的一阶导数值和二阶导数值;f i -1,f i -2,f i +1,f i +2分别表示i 节点依次向前两个节点和依次向后两个节点的函数值;fᶄi -1,fᶄi +1分别表示i 节点依次向前一个节点和依次向后一个节点的一阶导数值;fᵡi -1,fᵡi +1分别表示i 节点依次向前一个节点和依次向后一个节点的二阶导数值㊂式(2)对应f (x )展开以x i 为邻域的泰勒级数为f x ()=f x i ()+hfᶄx i ()+h 2fᵡx i ()2!+㊀㊀㊀㊀㊀h3f‴x i ()3!+h 4f 4()x i ()4!+h 5f 5()x i ()5!+h 6f 6()x i ()6!+h 7f 7()x i ()7!㊂㊀㊀(4)㊀㊀差分格式的各项系数由式(3)决定,可得到如下的三点六阶超紧致差分格式:716fᶄi +1+fᶄi -1()+fᶄi -h 16fᵡi +1-fᵡi -1()=㊀㊀1516h f i +1-f i -1(),98h fᶄi +1-fᶄi -1()+fᵡi -18fᵡi +1+fᵡi -1()=㊀㊀3h 2f i +1-2f i +f i -1()ìîíïïïïïïïïïï(5)为优化三点六阶紧致差分格式,并保持较好的数值频散,将迎风机制[12]引入式(5),构造出如下三点五阶迎风型超紧致差分格式:78fᶄi -1+fᶄi +h 19fᵡi -1-718fᵡi -172fᵡi +1()=㊀㊀1h -10148f i -1+73f i -1148f i +1(),25fᵡi -1+fᵡi +1h 1910fᶄi -1+165fᶄi +910fᶄi +1()=㊀㊀1h 2-135f i -1-45f i +175f i +1()㊂ìîíïïïïïïïïïï(6)左右边界可达到三阶精度紧致格式:fᶄ1-132fᶄ2+fᶄ3()+3h4fᵡ2+fᵡ3()=㊀㊀-12h f 3-f 2(),fᵡ1+3728h fᶄ3-fᶄ2()+3914h fᶄ1-3356fᵡ3-fᵡ2()=㊀㊀f 3-2f 1+f 2(),ìîíïïïïïïïï(7)fᶄN -132fᶄN -2+fᶄN -1()-3h 4fᵡN -2+fᵡN -1()=㊀㊀12h f N -2-f N -1(),fᵡN -3728h (fᶄN -2-fᶄN -1)-3914h fᶄN -3356(fᵡN -2-㊀㊀fᵡN -1)=1314h 2f N -2-2f N +f N -1()㊂ìîíïïïïïïïïïï(8)上述组合型超紧致差分格式只需要相邻的三个节点便可以同时求得一阶导数和二阶导数的五阶精度近似值,比普通差分格式的节点更少,降低了计算量㊂为便于编程计算,将上述构造的组合型超紧致差分格式重写为矩阵表达形式㊂假设U 为位移矩阵,其大小为m ˑn ,则求一阶导数和二阶导数的离47山东理工大学学报(自然科学版)2024年㊀散过程可以用矩阵运算表示为AF=BU,(9)结合内点的三点五阶迎风型超紧致差分格式和边界点的三点三阶差分格式,组成式(9)中等式左边的矩阵A和等式右边的矩阵B,大小分别为2mˑ2n 和2mˑn;F为奇数行为空间一阶导数和偶数行为空间二阶导数组成的矩阵,大小为2mˑn㊂以上矩阵分别为:A=10-13/23h/4-13/23h/439/14h1-37/28h33/5637/28h-33/567/8h/91-7h/180-h/7219/10h2/516/5h19/1007/8h/91-7h/180-h/7219/10h2/516/5h19/100⋱⋱⋱⋱⋱⋱7/8h/91-7h/180-h/7219/10h2/516/5h19/100-13/2-3h/4-13/2-3h/410-37/28h-33/5637/28h33/56-39/14h1éëêêêêêêêêêêêêêêêêùûúúúúúúúúúúúúúúúú,(10)F=∂u∂x()1,1∂u∂x()1,2∂u∂x()1,n-1∂u∂x()1,n∂2u∂x2()1,1∂2u∂x2()1,2 ∂2u∂x2()1,n-1∂2u∂x2()1,n︙︙︙︙∂u∂x()m,1∂u∂x()m,2∂u∂x()m,n-1∂u∂x()m,n∂2u∂x2()m,1∂2u∂x2()m,2 ∂2u∂x2()m,n-1∂2u∂x2()m,néëêêêêêêêêêêêêêùûúúúúúúúúúúúúú,(11) B=012/h-12/h-13/7h213/14h213/14h2-101/48h7/3h-11/48h-13/5h2-4/5h217/5h2-101/48h27/3h-11/48h-13/5h2-4/5h217/5h2⋱⋱⋱-101/48h7/3h-11/48h-13/5h2-4/5h217/5h2012/h-12/h-13/7h213/14h213/14h2éëêêêêêêêêêêêêêêêêùûúúúúúúúúúúúúúúúú,(12)U=u1,1u1,2 u1,n-1u1,nu2,1u2,2 u2,n-1u2,n︙︙︙︙u m-1,1u m-1,2 u m-1,n-1u m-1,nu m,1u m,2 u m,n-1u m,néëêêêêêêêùûúúúúúúú㊂(13)㊀㊀由式(9)可得F=A-1BU㊂(14)㊀㊀解线性代数方程组(9)可得Cahn-Hilliard方程的空间一阶导数和二阶导数㊂对于四阶导数,可将已求得的二阶导数替代式(14)中的U,再次使用式(14)进行求取㊂57第1期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀栗雪娟,等:Cahn-Hilliard方程的一个超紧致有限差分格式1.2㊀时间离散格式在对很多偏微分方程的数值求解中不仅需要高精度的空间离散格式,同时还需要高精度的时间离散格式㊂普通的一阶精度时间离散格式显然满足不了高精度计算要求,因此本文选用时间四阶Runge-Kutta 格式进行时间离散㊂Runge-Kutta 方法是基于欧拉方法改进后的求解偏微分方程的常用方法,这种方法不仅计算效率高,而且稳定性好㊂格式的推算过程如下:假设求解方程为∂u∂t+F u ()=0,(15)式中F 是对空间变量的微分算子,则修正的四阶Runge-Kutta 格式为u 0i =u n i ,u 1i =u n i-Δt 4F u ()()0i,u 2i =u ni -Δt 3F u ()()1i,u 3i =u n i-Δt 2F u ()()2i,u n +1i =u n i -Δt F u ()()3i ㊂ìîíïïïïïïïïïïïï(16)1.3㊀误差估计以五阶精度将fᶄi -1,fᶄi +1,fᵡi -1,fᵡi +1泰勒级数展开:fᶄi -1=fᶄi -hfᵡi +h 22!f (3)i -h 33!f (4)i +㊀㊀h 44!f (5)i -h 55!f (6)i ,fᶄi +1=fᶄi +hfᵡi +h 22!f (3)i +h 33!f (4)i+㊀㊀h 44!f (5)i +h 55!f (6)i ,fᵡi -1=fᵡi -hf (3)i +h 22!f (4)i -h 33!f (5)i+㊀㊀h 44!f (6)i -h 55!f (7)i ,fᵡi +1=fᵡi +hf (3)i +h 22!f (4)i +h 33!f (5)i +㊀㊀h 44!f (6)i +h 55!f (7)i ㊂ìîíïïïïïïïïïïïïïïïïïïïïïïïï(17)将式(17)代入式(6),所求得组合型超紧致差分格式的一阶导数及二阶导数对应的截断误差为:78fᶄi -1+fᶄi +h19fᵡi -1-718fᵡi -172fᵡi +1()=㊀1h -10148f i -1+73f i -1148f i +1()+78640f 6()ih 5,25fᵡi -1+fᵡi +1h 1910fᶄi -1+165fᶄi +910fᶄi +1()=㊀-135f i -1-45f i +175f i +1()-5125200f 7()i h 5,ìîíïïïïïïïïïï(18)78640f 6()i h 5ʈ8.101ˑ10-4f 6()i h 5,5125200f 7()ih 5ʈ2.023ˑ10-3f 7()i h 5㊂ìîíïïïï(19)㊀㊀使用组合型超紧致差分格式的好处是在每一个网格点上存在一个一阶和二阶连续导数的多项式㊂本文比较了组合型超紧致差分格式和现有广义格式的一阶导数和二阶导数的截断误差:fᶄi +αfᶄi +1+fᶄi -1()+βfᶄi +2+fᶄi -2()=㊀㊀a f i +1-f i -12h +b f i +2-f i -24h +c f i +3-f i -36h ,fᵡi +αfᵡi +1+fᵡi -1()+βfᵡi +2+fᵡi -2()=㊀㊀a f i +1-2f i +f i -1h 2+b f i +2-2f i +f i -24h2+㊀㊀c f i +3-2f i +f i -39h 2,ìîíïïïïïïïïïïï(20)式中参数α,β,a ,b ,c 在各种格式中取不同的值(表1,表2)㊂本文发现在各种方案中,组合型超紧致差分格式的截断误差最小㊂表1㊀不同格式一阶导数的截断误差格式αβa b c 截断误差二阶中心010013!f 3()ih 2标准Padeᶄ格式1/403/20-15f 5()ih 4六阶中心03/2-3/51/1036ˑ17!f 7()ih 6五阶迎风143ˑ16!f 6()ih 5表2㊀不同格式二阶导数的截断误差格式αβa b c 截断误差二阶中心01002ˑ14!f 4()ih 2标准Padeᶄ格式1/1006/50185ˑ16!f 6()ih 4六阶中心03/2-3/51/1072ˑ18!f 8()ih 6五阶迎风165ˑ17!f 7()ih 567山东理工大学学报(自然科学版)2024年㊀2㊀数值算例误差范数L 1和L 2的定义为:L 1=1N ðNi =1u -U ,L 2=1N ðNi =1u -U ()2㊂对四阶Cahn-Hilliard 取f u ()=u 2,k =2,在边界条件u 0,t ()=u 2π,t ()=0下的计算区域为0,2π[],方程的精确解为u x ,t ()=e -tsin x2,数值解为U ㊂对给出的数值算例,计算误差范数L 1和L 2,并采用四种方法进行数值模拟,对其数值结果进行误差分析和对比,结果见表3,本文所使用方法效果最佳,由此证明所提方法的有效性和可行性㊂表3㊀0.5s 时刻精确度测试结果(N =10)方法L 1误差L 2误差间断有限元格式1.56235ˑ10-21.37823ˑ10-2普通中心差分格式1.66667ˑ10-18.33333ˑ10-2紧致差分格式7.14286ˑ10-31.78571ˑ10-3组合型超紧致差分格式6.48148ˑ10-36.34921ˑ10-4㊀㊀用本文提出的式(6) 式(8)和式(16)计算算例,图1 图3给出了不同时刻数值解与精确解的(a)精确解(b)数值解图1㊀0.1s 的精确解与数值解(a)精确解(b)数值解图2㊀0.5s 的精确解与数值解(a)精确解(b)数值解图3㊀1s 的精确解与数值解77第1期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀栗雪娟,等:Cahn-Hilliard 方程的一个超紧致有限差分格式对比图,可以看出,数值解与精确解吻合很好,表明本文给出的数值格式是可行的,并且精度较高㊂3　结论本文研究了组合型超紧致差分方法和四阶Runge-Kutta方法,并将其运用于四阶Cahn-Hilliard 方程的数值求解,通过研究与分析,得到如下结论: 1)使用泰勒级数展开锁定差分格式系数,得到本文的组合型超紧致差分格式精度更高,误差更小㊂2)在边界点处有效地达到了降阶,并提高了精度㊂3)通过数值算例验证了数值格式的有效性㊂4)预估该方法可应用于高阶偏微分方程的数值求解㊂参考文献:[1]HUANG Q M,YANG J X.Linear and energy-stable method with en-hanced consistency for the incompressible Cahn-Hilliard-Navier-Stokes two-phase flow model[J].Mathematics,2022,10 (24):4711.[2]AKRIVIS G,LI B Y,LI D F.Energy-decaying extrapolated RK-SAV methods for the allen-Cahn and Cahn-Hilliard equations[J].SIAM Journal on Scientific Computing,2019,41(6):3703-3727. 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Computers and Mathematics with Applications,2022,126:172 -181.[7]MESFORUSH A,LARSSON S.A posteriori error analysis for the Cahn-Hilliard equation[J].Journal of Mathematical Modeling, 2022,10(4):437-452.[8]XIA Y,XU Y,SHU C W.Local discontinuous Galerkin methods for the Cahn-Hilliard type equation[J].Journal of Computational Phys-ics,2007,227(1):472-491.[9]CHEN L,LüS J.A fully discrete spectral scheme for time fractional Cahn-Hilliard equation with initial singularity[J].Computers and Mathematics with Applications,2022,127:213-224. [10]周诚尧,汪勇,桂志先,等.二维黏弹介质五点八阶超紧致有限差分声波方程数值模拟[J].科学技术与工程,2020,20(1):54 -63.[11]汪勇,徐佑德,高刚,等.二维黏滞声波方程的优化组合型紧致有限差分数值模拟[J].石油地球物理勘探,2018,53(6):1152 -1164,1110.[12]程晓晗,封建湖,郑素佩.求解对流扩散方程的低耗散中心迎风格式[J].应用数学,2017,30(2):344-349.(编辑:杜清玲)87山东理工大学学报(自然科学版)2024年㊀。

Differentially expressed genes in Populus simonii×Populus nigra in response

Plant Science 180(2011)796–801Contents lists available at ScienceDirectPlantSciencej o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /p l a n t s ciShort communicationDifferentially expressed genes in Populus simonii ×Populus nigra in response to NaCl stress using cDNA-AFLPLei Wang a ,c ,Boru Zhou a ,Lili Wu a ,Baozhu Guo b ,Tingbo Jiang a ,∗aKey Laboratory of Forest Tree Genetic Improvement and Biotechnology of Ministry of Education,Northeast Forestry University,Harbin 150040,China bUSDA-Agricultural Research Service,Crop Protection and Management Research Unit,University of Georgia,Tifton,GA 31793,USA cHigh-Tech Institute,Heilongjiang Academy of Sciences,Harbin 150088,Chinaa r t i c l e i n f o Article history:Received 7June 2010Received in revised form 31January 2011Accepted 2February 2011Available online 16February 2011Keywords:cDNA-AFLPDifferential gene expression Salinity stress Real-time PCRa b s t r a c tSalinity is an important environmental factor limiting growth and productivity of plants,and affects almost every aspect of the plant physiology and biochemistry.The objective of this study was to apply cDNA-AFLP and to identify differentially expressed genes in response to NaCl stress vs.no-stress in Populus simonii ×Populus nigra in order to develop genetic resources for genetic improvement.Selective ampli-ﬁcation with 64primer combinations allowed the visualization of 4407transcript-derived fragments (TDFs),and 2027were differentially expressed.Overall,107TDFs were re-sequenced successfully,and 86unique sequences were identiﬁed in 10functional categories based on their putative functions.A sub-set of these genes was selected for real-time PCR validation,which conﬁrmed the differential expression patterns in the leaf tissues under NaCl stress vs.no stress.Differential expressed genes will be studied further for association with salt or drought-tolerance in P.simonii ×P.nigra .This study suggests that cDNA-AFLP is a useful tool to serve as an initial step for characterizing transcriptional changes induced by NaCl salinity stress in P.simonii ×P.nigra and provides resources for further study and application in genetic improvement and breeding.All unique sequences have been deposited in the Genbank as accession numbers GW672587–GW672672for public use.©2011Elsevier Ireland Ltd.All rights reserved.1.IntroductionSalinity is a major environmental factor limiting plant growth and productivity.Salinity leads to osmotic stress,reactive oxygen damage,and ion toxicity resulting in irreversible cellular damage and photo-inhibition [1,2].During exposure to salt stress condi-tions,almost every aspect of a plant’s important life processes are affected [3].Salt stress not only causes physiological changes in plants (phenotypic variation),but also affects plant gene expression levels (genotypic variation).Populus simonii ×Populus nigra ,which is the hybrid of P.simonii and P.nigra ,widely distributes in the northern region of the Yellow River Basin in China.The early studies of P.simonii ×P.nigra were focused on germplasm introduction and cultivation [4,5].In recent years,the research has been focusing on using transgenic technol-ogy to enhance disease resistance [6],insect resistance [7]and salt tolerance [8,9].However,there still lacks genomic information in P.simonii ×P.nigra for molecular characterization of stress tolerance and breeding.∗Corresponding author.E-mail address:tbjiang@ (T.Jiang).The advent of next-generation sequencing has made sequence based gene expression analysis an increasingly common.Gene expression proﬁling is the measurement of the activity and the expression of thousands of genes at the same time.DNA microarray technology measures the relative activity of previously identiﬁed target genes.Sequence based techniques,like serial analysis of gene expression (SAGE,SuperSAGE)are also used for gene expression proﬁling.However,the cost and complexity of these experiments are also concerns to many research laboratories.We decided to apply a simple and quick RNA ﬁngerprinting method described by Bachem et al.[10]in P.simonii ×P.nigra gene expression analysis in responding to salt stress.RNA ﬁngerprinting method,based on AFLP (ampliﬁed fragment length polymorphism)or called cDNA-AFLP,does not require prior sequence information and allows the detailed characterization of gene expression in a wide range of biological processes [10].Comprehensive and systematic analysis can be carried out on the organism transcriptome by cDNA-AFLP,which can then be applied successfully to study gene expression characteristics [11,12],genetic marker analysis [13]and separation of differentially expressed genes [14].The objective of this study was to apply cDNA-AFLP and to identify differentially expressed genes in response to NaCl stress vs.no-stress in P.simonii ×P.nigra in order to develop genetic resources for genetic improve-ment,even though there are other genomic resources available0168-9452/$–see front matter ©2011Elsevier Ireland Ltd.All rights reserved.doi:10.1016/j.plantsci.2011.02.001L.Wang et al./Plant Science180(2011)796–801797Table1Numbers of transcript-derived fragments(TDFs)and different primer combination.Primer name T-AG T-CA T-CT T-AC T-TC T-TG T-GA T-GT TotalM-AC4a/3b(16c)38/20(42)30/9(23)3/10(29)25/7(41)16/21(48)11/40(43)23/1(4)150/111(246)M-AG34/5(65)21/37(43)18/5(32)32/14(48)7/6(5)14/20(41)30/9(65)18/24(49)174/120(348)M-CA5/13(16)29/8(38)1/12(28)10/19(40)5/1(19)38/6(46)22/11(77)11/34(59)121/104(323)M-CT37/19(77)13/10(51)26/10(27)7/16(62)7/4(16)13/24(53)17/27(49)13/16(35)133/126(370)M-TC0/80(20)28/18(30)5/5(23)14/12(43)2/13(14)33/9(45)22/14(46)17/3(24)121/154(245)M-TG0/44(42)36/4(42)11/4(52)(49)(11)0/7(18)(62)7/27(39)101/146(315)M-GT25/29(19)14/26(44)4/20(28)3/8(5)11/8(19)15/13(42)28/17(43)8/8(7)108/129(207)M-GA4/25(57)20/12(54)(43)28/15(63)7/5(18)22/2(11)22/25(70)8/12(10)123/106(326)Total109/218(312)199/135(344)107/75(256)108/131(339)79/47(143)151/102(304)173/163(455)105/125(227)1031/996(2380)a Down-regulated gene.b Up-regulated gene.c Constitutive expressed gene.such as /poplar.We carried out cDNA-AFLP analysis in leaf tissues under salt stress vs.no stress in order to identify differentially expressed genes,which were validated by real-time PCR analysis.The differentially expressed genes could be used in further study in characterization and breeding for salinity tolerance and understanding the response of P.simonii×P.nigra to NaCl stress.2.Materials and methods2.1.Plant materialsThe branches of P.simonii×P.nigra from the same clone were grown under hydroponic conditions in a phytotron at26◦C/22◦C (day/night)with75%relative humidity,16h photoperiod and 175␮mol/(m2s)light intensity.New leaves and roots were grown out after40days.The branches with new leaves and roots were divided into two groups.One group was grown under normal con-dition as control and the other was stressed with200mM NaCl. After two days,the leaf tissues of these two groups were harvested and frozen immediately in liquid nitrogen.Tissues were then stored at−80◦C until use.2.2.cDNA-AFLP analysis and TDFs isolationTotal RNA was extracted from frozen leaf tissues using Trizol reagent(Invitrogen)according to the manufacture’s instructions. Two micrograms of total RNA was used initially for theﬁrst-strand cDNA synthesis,followed by the second-strand cDNA sythesis using a M-MLV RTase cDNA Synthesis Kit(Takara)according to the man-ufacture’s instructions.cDNA-AFLP analysis was carried out using AFLP Expression Analysis Kit(LI-COR).One hundred nanograms of double-stranded cDNA was digested with Taq I and Mse I,and the fragments were ligated to adapter for ampliﬁcation(Taq I-F: 5 -CTCGTAGACTGCGTAC-3 ;Taq I-R:5 -CGGTACGCAGTCT-3 ;Mse I-F:5 -GACGATGAGTCCTGAG-3 ;Mse I-R:5 -TACTCAGGACTCAT-3 ). Pre-ampliﬁcation was performed with a Taq I primer(TPPC:5 -GTAGACTGCGTACCGA-3 ),combined with a Mse I primer(MPPC: 5 -GATGAGTCCTGAGTAA-3 ).Pre-ampliﬁcation PCR conditions were as follows:denaturation at94◦C for30s,annealing at56◦C for60s,extension at72◦C for60s,total20cycles.After preampli-ﬁcation,the selective ampliﬁcation with64primer Taq I/Mse I(+2, +2)combination(Table1)was carried out using a touchdown pro-gram.The PCR conditions were as follows:denaturation at94◦C for30s,annealing at65◦C for30s,extension at72◦C for60s(12 cycles,scaledown of0.7◦C per cycle);denaturation at94◦C for30s, annealing at56◦C for60s,extension at72◦C for60s(23cycles). The Taq I primers were labeled with IRD700ﬂuorescent dye(LI-COR,Lincoln,Nebraska).The selective ampliﬁcation products were separated on a6%polyacrylamide gel with a LI-COR4300DNA analyzer under1500V and40W condition.The transcript-derived fragments(TDFs)were isolated using a LI-COR Odyssey®Infrared Imaging System.The bands of interest were cut from the gel with a surgical blade and eluted in60␮l sterile distilled water.Two micro-liters of eluted DNA was used as template for re-ampliﬁcation using selective ampliﬁed primers.PCR products were puriﬁed with a PCR puriﬁcation kit(Takara,Dalian),and cloned into pUC119vector (Takara,Dalian)and sequenced.2.3.Sequence analysisSequencing results were analyzed using BLASTX searches against the GenBank non-redundant public sequence database.The TDFs sequences were manually assigned to functional categories based on the analysis of scientiﬁc literature and also with the aid of the information reported for each sequence by Gene Ontology consortium.2.4.Real-time PCR and data analysisLeaf tissues in the stressed group were sampled at2days after the treatment with200mM NaCl,as well as the control group.All samples were examined in three independent biological replica-tions.To decrease replicated experimental variation at each sample, the three puriﬁed RNA from each biological replicate were pooled equally for qRT-PCR.Three experimental technical replications were performed for each pooled sample to assess the reproducibil-ity,and the mean of the three replications was used to calculate relative expression quantitation.First strand cDNA was synthesized from1␮g DNase-treated total RNA using Reverse Transcriptase M-MLV(Takara).The reverse transcription reaction was diluted to a ﬁnal volume of100␮l,and2␮l was used as template for PCR using SYBR Premix ExTaq TM.Threshold values(C T)generated from DNA Engine Opticon TM2(MJ Research)were employed to quantify rel-ative gene expression using the comparative2− C T method[15]. Cycling parameters were set up according to the recommenda-tion of QuantiTect SYBR green RT-PCR kit.Melting curves were run immediately after the last cycle to examine if the measurements were inﬂuenced by primer–dimer pairs.The ampliﬁcation curve was generated after analyzing the raw data,and the cycle threshold(C T)value was calculated based on theﬂuorescence threshold as0.01.Populus actin (EF418792)gene expression was used as an internal control to normalize all data.The expression of Populus actin was constant using real-time PCR.The“delta–delta C T”(2− C T)mathemat-ical model was used for description and comparison of the relative quantiﬁcation of gene expressions between samples. Therefore,the amount of target gene in test sample was given by R=2− C T,where C T= C Ttest sample− C Tcontrol sample, C Tsample=C T test gene−C Treference gene.Theﬁnal value of relative798L.Wang et al./Plant Science180(2011)796–801Fig. 1.Expression of Populus simonii×P.nigra transcripts under NaCl stress displayed by cDNA-AFLP.An example showing selective amplication with dif-ferent primer combinations;a=water control leaves;b=NaCl treated leaves; 1–22=different primer combination:T-TG/M-AC,T-TG/M-AG,T-TG/M-CA,T-GA/M-AC,T-TG/M-TC,T-TG/M-TG,T-TG/M-GT,T-TG/M-GA,T-TG/M-CT,T-GA/M-AG, T-GA/M-CA,T-GA/M-CT,T-GA/M-TC,T-GA/M-TG,T-GA/M-GT,T-GA/M-GA,T-GT/M-AC,T-GT/M-AG,T-TG/M-CA,T-TG/M-CT,T-GT/M-TC,and T-GT/M-TG.This study had three technical replicates of equally pooled samples of three biological replicates.quantitation was described as fold change of gene expression in the tested sample compared with the control sample.3.Results and discussionTo isolate differentially expressed transcripts,we carried out cDNA-AFLP analysis on total RNA samples from leaves under nor-mal growth and salt stress.Selective ampliﬁcation with64primer combinations allowed the visualization of4407TDFs,2027of which were differentially expressed,corresponding to about46%of all visualized transcripts.Of the2027TDFs,996were up-regulated and 1031down-regulated(Fig.1).A total of161differentially expressed TDFs were recovered from gels and121were re-ampliﬁed,cloned and sequenced.The differentially expressed TDF band was excised from the gel,eluted,re-ampliﬁed and puriﬁed for direct sequencing,which yielded107cDNA fragments that gave rise to useable sequence data.Among these sequences,86were unique sequences and searched for homologous to known databases,and70sequences were annotated with database matches and16sequences had no database matches.There were some unique sequences homologous to various Populus sequence databases,either as tentative consen-sus sequences or expressed sequence tags(EST)without known functional annotations.Seventy were homologous to known func-tion genes and listed in Table2,while majority were homologous to Arabidopsis sequences(Table2)which have annotated functions. These TDFs might be homologous to Populus sequences but these sequences were not annotated yet and,therefore,these TDFs were annotated to the species with known annotations(Table2).All86TDFs isolated from NaCl stressed P.simonii×P.nigra were deposited in the Genbank under accession numbers from GW672587–GW672672,while a selection of the TDFs with known functions is shown in Table2.Each transcript was functionally annotated through careful analysis of the scientiﬁc literature and the Gene Ontology Database.Fig.2shows the percentages of P.simonii×P.nigra genes assigned to different functional cat-egories.Approximately17.4%of the annotated sequences have primary metabolic roles,11.6%are involved in signal transduc-tion,and a further12.79%in transcription regulation.There are about18.6%with unknown proteins.Interestingly,there are about 5.8%have roles in response to stresses.Other relevant groups of differentially expressed TDFs include cellular biosynthesis (10.5%),transport(4.7%),cellular catabolism(4.7%),photosynthe-sis and redox(7%),and development process(6.98%).Most of the differentially-expressed P.simonii×P.nigra transcripts were down regulated in response to salt stress.There were two exception cate-gories,response to stresses and transcription regulation where60% and64%of the differentially expressed genes were up-regulated (Table2).To verify cDNA-AFLP identiﬁed genes by real-time PCR,10genes with induced or repressed patterns in cDNA-AFLP study were selected for speciﬁc primer design for qRT-PCR.Relative quan-titative method delta–delta C T(2− C T)was used to describe expression patterns of selected genes by comparing the gene expression levels at2days after NaCl treatment with control.The relative quantitation comparisons based on C T values from the treated samples and the control samples were calculated as the algorithm R=2− C T.Generally,R value>2.00was described as induced,R value<0.50as repressed,and2.00≥R value≥0.50as no-change.The results indicated that the expression levels measured by qRT-PCR reproduced the cDNA-AFLP study very well(Table3). One exception was TDF C-2,repressed in cDNA-AFLP but classiﬁed as no-change in qPCR.Therefore,the results showed that the cDNA-AFLP technique was effective in identifying differentially expressed genes in P.simonii×P.nigra.Although DNA microarrays are currently the standard tool for genome-wide expression analysis,their application also is limited to organisms for which the complete genome sequence or large col-lections of known transcript sequences are available[16,17].Other differential cDNA screening methods,such as the suppression subtractive hybridization technique may allow such previously un-identiﬁed genes to be isolated.Here,we applied our LI-COR system and tested AFLP-based transcript proﬁling method,cDNA-AFLP, that allows genome-wide expression analysis without the need for prior sequence knowledge.This method has utility in tree study like P.simonii×P.nigra for gene discovery on the basis of fragment detection and for temporal quantitative gene expression analysis.Brinker et al.[16]carried out transcriptome study to investi-gate early salt-responsive genes in early salt treatment after24h in a salt-tolerant poplar species Populus euphratica using microarray containing ESTs representing about6340genes from P.euphratica. They revealed that the leaves suffered initially from dehydration, which resulted in changes in transcript levels of mitochondrial and photosynthetic genes.Initially,decreases in stresses in stress-related genes were found,whereas increases occurred only when leaves had restored the osmotic balance by salt accumulation.In our study,after2days salt treatment,we also found that in the photosynthesis group,majority(4out of6)genes were repressed (Table2),indicating adjustment of energy metabolism.Ding et al.[17]studied salt-induced expression of genes related to Na/K and ROS homeostasis in leaves of salt-resistant and salt-sensitive Populus species using the Affymetrix poplar genome array after24h short-term exposure to150mM NaCl and28days long-term exposure to200mM NaCl.We studied salt-induced expression of genes in response to200mM NaCl after2days expo-sure and successfully identiﬁed86unique genes which will be used in further study,such as the highly expressed genes TDF D-10(putative Cupin family proteins)and TDF88-1(putative Zinc ﬁnger protein)and the repressed gene TDF109-2(WRKY tran-scription factor).Cupin was germin-like and plant storage proteins, which regulated seed germination and early seedling development [18].The expression level of the cupin gene(GW672616)was very high under salt stress than under control conditions using qPCR (Table3),which will be further studied.A C3HC4-type RINGﬁnger protein was involved in protein–protein interaction and ubiqui-tination[19].Most ringﬁnger proteins were E3ubiquitin ligases that mediate the transfer of the ubiquitin to target proteins and play important roles in diverse aspects of celluar regulations inL.Wang et al./Plant Science180(2011)796–801799Table2Function classiﬁcation of NaCl salt stress related transcript-derived fragment(TDF)in P.simonii×P.nigra.TDF Primercombination GenbankaccessionLength(bp)I/R Annotation(species)Blast score(Blastx/Blastn a)Regulation of transcription109-2T-GA/M-GT GW672671311−WRKY transcription factor[Populus tremula×Populus alba] 4.00E−31 N-3T-AG/M-AG GW672667437+TCP family transcription factor[Arabidopsis tha liana]7.00E−42 N-11T-AG/M-AG GW672664108+Bel1homeotic proteine[Ricinus communis] 2.00E−06 M-21T-CA/M-TG GW672654221−Zinc knuckle(CCHC-type)family protein[Arabidopsis thaliana]0.58E-5T-CA/M-AG GW672623288−ARR12(Arabidopsis response regulator12;transcriptionfactor)[Arabidopsis thaliana]6.00E−0482-2T-TG/M-AC GW672595350+Mitochondrial transcription termination factor[Arabidopsisthaliana]0.74F-3T-CA/M-CT GW672629379+AP2/ERF domain-containing transcription factor[Populustrichocarpa]5.00E−36G-19T-GA/M-CA GW672636143−ATP binding/DNA binding/DNA-dependent ATPase[Arabidopsisthaliana]0.41a20-2T-AG/M-AG GW672590334+RDR6(RNA-directed RNA polymerase6)[Arabidopsis thaliana]0.087aN-8T-AC/M-CT GW672671391+DEAH box helicase[Arabidopsis thaliana] 1.00E−19 N-6T-AC/M-GT GW672670117+ATP binding/DNA binding/helicase[Arabidopsis thaliana]9.00E−15Response to stressM-4T-TG/M-TC GW672662100−CPHSC70-1(chloroplast heat shock protein70-1)[Arabidopsisthaliana]2.00E−07 H-2T-AC/M-CT GW672647166+Osmotin precursor[Ricinus communis]3.00E−21 B-4T-CT/M-CA GW672608240+Disease resistance protein(CC-NBS-LRR class)[Arabidopsisthaliana]9.00E−21 N-5T-AC/M-AC GW672669295+Peroxidase12(PER12)[Arabidopsis thaliana] 4.00E−07 A-5T-GT/M-CA GW672602108−ADH1(Alcohol dehydrogenase1)[Arabidosis thaliana] 2.00E−04TransportE-1T-AG/M-AG GW672620310−ADNT1(adenine nucleotide transporter1)[Arabidopsisthaliana]6.00E−48 G-23T-AC/M-CT GW672637107−ATPase,coupled to transmembrane movement ofsubstances[Arabidopsis thaliana]1.00E−11H-12T-AC/M-CT GW672644154+Xenobiotic-transporting ATPase[Arabidopsis thaliana] 1.00E−19 G-1T-GA/M-CA GW672631151−ATARLA1C(ADP-ribosylation factor-like A1C)[Arabidopsisthaliana]3.00E−22Photosynthesis and redoxA-4T-CA/M-GC GW67260171−Photosystem II protein D1[Arabidopsis thaliana] 1.00E−07 G-6T-AC/M-GA GW672639125−LHCB4.2(light harvesting complex PSII)[Arabidopsis thaliana] 2.00E−16 C-1T-AG/M-AG GW672611218−LHCB3(light-harvesting chlorophyll binding protein3)[Arabidopsis thaliana]6.00E−36 D-6T-CG/M-CA GW672619147+NADH dehydrogenase subunit K[Populu trichocarpa]8.00E−11 H-22T-CA/M-AG GW672648160+Cytochrome P450[Populus trichocarpa] 1.00E−20 C-2T-CA/M-CA GW672612266−Malate dehydrogenase[Clusia uvitana] 3.00E−29Development process30-2T-AG/M-GT GW672591413+Senescence-associated protein[Arabidopsis thaliana] 1.00E−31 M-34T-TG/M-TC GW672660340−TPR1(topless-related1)[Arabidopsis thaliana] 1.00E−56 D-10T-CT/M-CA GW672616238+Cupin family protein[Arabidopsis thaliana]9.00E−18 10-1T-AC/M-AC GW672588546+Cysteine proteinase[Arabidopsis thaliana] 2.00E−04 F-7T-TC/M-CT GW672630359−Cytokinin oxidase[Populus trichocarpa] 5.00E−56 43-3T-CA/M-TG GW672593455−Cinnamyl alcohol dehydrogenase-like protein[Populustrichocarpa]5.00E−68Cellular catabolismE-9T-CT/M-AC GW672627130−UBP5(Ubiquitin-speciﬁc protease5)[Arabidopsis thaliana] 2.00E−18 F-1T-CT/M-GA GW672628167+Chitinase[Ricinus communis]8.00E−11 M-26T-CT/M-TG GW672657299−Ubiquitin-conjugation enzyme[Glycine max]8.00E−40 88-1T-TG/M-CT GW672596326+Zincﬁnger(C3HC4-type RINGﬁnger)familyprotein[Arabidopsis thaliana]5.00E−37Cellular biosynthesisE-12T-CA/M-AC GW672621324−Serine palmitoyl transferase subunit[Nicotiana benthamiana] 5.00E−54 E-8T-GA/M-CA GW672626129−EIF4A1(eukaryotic translation initiation factor4A-1)[Arabidopsis thaliana]3.00E−1989-2T-TG/M-TC GW672597388−S-adenosylmethionine decarboxylase1[Populusmaximowiczii×Populus nigra]6.00E−44 C-4T-GA/M-GT GW672614180−Ribosomal protein S3[Flacourtia jangomas] 3.00E−26 G-15T-GA/M-CT GW672635140−GAUT3(Galacturonosyl transferase3)[Arabidopsis thaliana] 2.00E−09 M-28T-AG/M-AG GW672658269−Ferrochelatase II[Arabidopsis thaliana] 1.00E−23 N-16T-AC/M-AC GW672666282+CARB(Carbamoyl phosphate synthetase B)[Arabidopsisthaliana]8.00E−08G-7T-GA/M-CT GW672640146−Trehalose-6-phosphate synthase[Ricinus communis]8.00E−18 D-2T-CT/M-GT GW672617205+2-isopropylmalate synthase[Arabidopsis thaliana] 1.00E−28MetabolismE-6T-AG/M-GA GW672624237−Radical sam protein[Ricinus communis] 3.00E−11 E-7T-CA/M-AG GW672625270−Adenosine kinase[Ricinus communis] 2.00E−43 43-1T-CA/M-TG GW672592526−Lactoylglutathione lyase[Arabidopsis thaliana] 4.00E−60 C-3T-AG/M-AG GW672613342−4-coumarate–CoA ligase family protein[Arabidopsis thaliana]0.75M-6T-TG/M-TC GW672663158−Serine carboxypeptidase[Ricinus communis] 4.00E−18 H-10T-AC/M-TC GW672642351+Lactoylglutathione lyase family protein/glyoxalase I familyprotein[ArabidopsisThaliana]2.00E−23800L.Wang et al./Plant Science180(2011)796–801 Table2(Continued)TDF Primercombination GenbankaccessionLength(bp)I/R Annotation(species)Blast score(Blastx/Blastn a)47-1T-CA/M-GA GW672594350−Glycine decarboxylase P-protein1[Arabidopsis thaliana]9.00E−60 N-4T-AG/M-AC GW672668360+Acetate-CoA ligase[Arabidopsis thalian] 1.00E−52 N-10T-AG/M-AG GW672587437+Shock protein binding protein[Ricinus communis]7.00E−30 D-5T-CT/M-CA GW672618216+Carbonate dehydratase[Arabidopsis thaliana] 2.00E−19 H-15T-TG/M-CT GW672645101+FKBP-type peptidyl-prolyl cis–trans isomerase familyprotein[Arabidopsis thaliana]4.00E−07H-24T-AG/M-CT GW672649180+PDC3(Pyruvate decarboxylase-3)[Arabidopsis thaliana] 1.00E−28 H-11T-AC/M-CT GW672643169+NAD+ADP-ribosyltransferase[Arabidopsis thaliana]0.0003a M-31T-CT/M-GT GW672659228−Nicotinamide phosphoribosyl transferase[Aeromonas phage44RR2.8t]9.00E−19 M-35T-AC/M-TC GW67266186−Trehalose/maltose hydrolase or phosphorylase[Capnocytophaga ochracea]5.00E−06Signal transductionA-7T-TG/M-CT GW672603108−G-H2AX/GAMMA-H2AX/H2AXB/HTA3;DNAbinding[Arabidopsis thaliana]9.10E−02 G-12T-AC/M-CT GW67263390−FTSZ2-2structural molecule[Arabidopsis thaliana] 3.00E−09 H-25T-TG/M-CT GW672650187+Calmodulin[Arabidopsis thaliana]0.088M-13T-AG/M-AC GW672652179−Leucine-rich repeat transmembrane protein kinase[Arabidopsisthaliana]0.15aB-1T-CT/M-CT GW672605123+Kinase family protein[Arabidopsis thaliana]0.097aG-11T-CT/M-AC GW672632158−Cpk-related protein kinase3[Populus trichocarpa] 4.00E−21 M-14T-CT/M-TG GW672653305−F-box family protein[Arabidopsis thaliana]0.57H-4T-AG/M-GA GW672651163+SIT4phosphatase-associated family protein[Arabidopsisthaliana]2.00E−17M-23T-AG/M-TC GW672655226−Phosphate-responsive protein[Arabidopsis thaliana]8.00E−24 C-9T-AG/M-GA GW672615328−Serine–threonine protein kinase,plant-type[Ricinuscommunis]1.00E−49I/R:induced or repressed in cDNA-AFLP studies.a Blast scores with asterisk were from Blastn,otherwise from Blastx.plants[20,21].The expression level of the C3HC4-type RINGﬁn-ger gene TDF88-1was higher under salt stress than under control conditions.WRKY proteins are newly identiﬁed transcription fac-tors involved in many plant processes including plant responses to biotic and abiotic stresses.To regulate gene expression,the WRKY domain binds to the W box in the promoter of the target gene to modulate transcription[22,23].In plants,many WRKY proteins are involved in the defense against attacks from pathogens[24,25], and abiotic stresses of wounding,the combination of drought and heat stress,and cold stress[26].The expression of putative WRKY TDF109-2was repressed in this study,which we will study this gene further in broad germplasm to characterize the expression in response to salt/drought stress.In summary,we present a method that could be used for synthe-sizing cDNA from salt stressed P.simonii×P.nigra vs.control,which gives broad genome coverage;this study also provides genomic information on the differentially expressed TDFs by cDNA-AFLP in P. simonii×P.nigra under NaCl salt stress.Adaptation of plants totheirFig.2.Functional classiﬁcation of expressed genes or TDFs(transcript-derived fragments)in P.simonii×P.nigra under NaCl stress displayed by cDNA-AFLP.The percentage of up-regulated(in grey)and down-regulated(in white)transcripts within each functional category,which was primarily based on the data displayed in Table2.L.Wang et al./Plant Science180(2011)796–801801Table3Validation of expression patterns of selected genes from cDNA-AFLP using real-time qRT-PCR.TDF ID a Expression pattern in cDNA-AFLP b qRT-PCR c(mean±SE)C-1−0.32±0.22C-2−0.61±0.3110-1+ 4.57±1.5330-1+ 2.65±0.9047-1−0.25±0.0488-1+10.55±6.06D-2+ 2.34±0.51D-5+7.21±4.53D-6+ 2.98±1.48D-10+113.3±59.5a ID:TDF identiﬁcation number in Table2.b cDNA-AFLP,results of the expression patterns of selected genes at2days after NaCl treatment compared with no stress control;+/−used to show gene expression trends in cDNA-AFLP,+,induced,−,repressed.c Real time qRT-PCR,results of relative quantitative qRT-PCR(R=2− C(T))of selected genes at2days after NaCl treatment compared to no stress control. R value>2.00as induced,R value<0.50as repressed, 2.00≥R value≥0.50as unchanged.Three experimental technical replications were performed for each equally pooled sample from three biological samples to assess the reproducibil-ity,and the mean of the three replications was used to calculate relative expression quantitation.environment can be highly efﬁcient,involving many metabolic and physiological changes.This study shows that it is possible to repro-duce the proﬁles of gene expression in a salt stressed P.simonii×P. nigra and to isolate differentially regulated sequences using a modi-ﬁcation of the cDNA-AFLP protocol of Bachem et al.[10].Therefore, these data suggest that cDNA-AFLP is a useful tool to serve as an initial step for characterizing transcriptional changes induced by NaCl salinity stress in P.simonii×P.nigra and provides resources for further study and will contribute to the genetic improvement of P. simonii×P.nigra.This is because prior sequence data is not required for the visual identiﬁcation of differentially expressed transcripts, in contrast to other approaches.AcknowledgmentsThis work has been supported in part by the Fundamental Research Funds for the Central Universities and the Key Research Project of Heilongjiang Province(GA09B201-4).References[1]R.Munns,Comparative physiology of salt and water stress,Plant Cell Environ.25(2002)239–250.[2]Widodo,J.H.Patterson,E.Newbigin,M.Tester,A.Bacic,U.Roessner,Metabolicresponses to salt stress of barley(Hordeum vulgare L.)cultivars,Sahara and Clipper,which differ in salinity tolerance,J.Exp.Bot.60(2009)4089–4103. 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差别差异英文作文

差别差异英文作文Title: Exploring Distinctions: Understanding Differences。

In our vast and diverse world, differences abound, shaping the tapestry of cultures, beliefs, and perspectives. Understanding these disparities is paramount to fostering empathy, promoting inclusivity, and building bridges across divides. In this essay, we delve into the realm of distinctions, exploring the myriad ways in whichdifferences manifest and influence our lives.At the heart of distinctions lie contrasts, disparities that highlight the uniqueness of individuals, groups, or concepts. These disparities can manifest in various forms, be it cultural, social, economic, or ideological. Understanding these differences requires a nuanced approach, one that transcends superficial observations and delvesinto the underlying factors that contribute to disparities.Cultural distinctions, for instance, are perhaps among the most palpable. Cultures shape our beliefs, traditions, and behaviors, imbuing each society with its distinct identity. From the vibrant festivities of one culture to the solemn rituals of another, cultural disparities enrich the human experience, offering glimpses into the kaleidoscope of human existence.Moreover, social disparities underscore the unequal distribution of resources, opportunities, and privileges within society. These differences can stem from various factors, including socioeconomic status, race, gender, or geographic location. Recognizing and addressing these disparities is essential for promoting social justice and equality, ensuring that every individual has the chance to thrive irrespective of their background.Economic distinctions further accentuate thedisparities that exist within our globalized world. The gap between the affluent and the impoverished continues to widen, perpetuating cycles of inequality and marginalization. Bridging this gap requires concertedefforts to address systemic barriers and create pathwaysfor economic empowerment and upward mobility.Ideological distinctions, on the other hand, reflect the diversity of thought and belief systems that shape our worldviews. From political ideologies to religious doctrines, these differences often fuel spirited debates and contentious discussions. However, embracing ideological diversity fosters intellectual curiosity and promotes critical thinking, paving the way for constructive dialogue and mutual understanding.In addition to these overarching categories, distinctions can also emerge in more subtle ways, influencing our perceptions and interactions on a daily basis. These micro-level differences, whether in communication styles, interpersonal dynamics, or personal preferences, underscore the complexity of human relationships and interactions.Yet, amidst these disparities, lies the potential for growth, learning, and mutual enrichment. By embracingdiversity in all its forms, we cultivate a richer tapestry of experiences, perspectives, and insights. Rather than viewing differences as barriers, we should celebrate them as opportunities for connection, collaboration, and collective progress.In conclusion, distinctions are an inherent aspect of the human experience, shaping our identities, interactions, and worldview. Embracing these differences with empathy, respect, and openness is essential for building inclusive societies and fostering a more harmonious world. As we navigate the complexities of our diverse world, let us strive to transcend divisions and embrace the beauty of our shared humanity.。

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Network impacts of a road capacity reduction:Empirical analysisand model predictionsDavid Watling a ,⇑,David Milne a ,Stephen Clark baInstitute for Transport Studies,University of Leeds,Woodhouse Lane,Leeds LS29JT,UK b Leeds City Council,Leonardo Building,2Rossington Street,Leeds LS28HD,UKa r t i c l e i n f o Article history:Received 24May 2010Received in revised form 15July 2011Accepted 7September 2011Keywords:Trafﬁc assignment Network models Equilibrium Route choice Day-to-day variabilitya b s t r a c tIn spite of their widespread use in policy design and evaluation,relatively little evidencehas been reported on how well trafﬁc equilibrium models predict real network impacts.Here we present what we believe to be the ﬁrst paper that together analyses the explicitimpacts on observed route choice of an actual network intervention and compares thiswith the before-and-after predictions of a network equilibrium model.The analysis isbased on the ﬁndings of an empirical study of the travel time and route choice impactsof a road capacity reduction.Time-stamped,partial licence plates were recorded across aseries of locations,over a period of days both with and without the capacity reduction,and the data were ‘matched’between locations using special-purpose statistical methods.Hypothesis tests were used to identify statistically signiﬁcant changes in travel times androute choice,between the periods of days with and without the capacity reduction.A trafﬁcnetwork equilibrium model was then independently applied to the same scenarios,and itspredictions compared with the empirical ﬁndings.From a comparison of route choice pat-terns,a particularly inﬂuential spatial effect was revealed of the parameter specifying therelative values of distance and travel time assumed in the generalised cost equations.When this parameter was ‘ﬁtted’to the data without the capacity reduction,the networkmodel broadly predicted the route choice impacts of the capacity reduction,but with othervalues it was seen to perform poorly.The paper concludes by discussing the wider practicaland research implications of the study’s ﬁndings.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionIt is well known that altering the localised characteristics of a road network,such as a planned change in road capacity,will tend to have both direct and indirect effects.The direct effects are imparted on the road itself,in terms of how it can deal with a given demand ﬂow entering the link,with an impact on travel times to traverse the link at a given demand ﬂow level.The indirect effects arise due to drivers changing their travel decisions,such as choice of route,in response to the altered travel times.There are many practical circumstances in which it is desirable to forecast these direct and indirect impacts in the context of a systematic change in road capacity.For example,in the case of proposed road widening or junction improvements,there is typically a need to justify econom-ically the required investment in terms of the beneﬁts that will likely accrue.There are also several examples in which it is relevant to examine the impacts of road capacity reduction .For example,if one proposes to reallocate road space between alternative modes,such as increased bus and cycle lane provision or a pedestrianisation scheme,then typically a range of alternative designs exist which may differ in their ability to accommodate efﬁciently the new trafﬁc and routing patterns.0965-8564/$-see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.tra.2011.09.010⇑Corresponding author.Tel.:+441133436612;fax:+441133435334.E-mail address:d.p.watling@ (D.Watling).168 D.Watling et al./Transportation Research Part A46(2012)167–189Through mathematical modelling,the alternative designs may be tested in a simulated environment and the most efﬁcient selected for implementation.Even after a particular design is selected,mathematical models may be used to adjust signal timings to optimise the use of the transport system.Road capacity may also be affected periodically by maintenance to essential services(e.g.water,electricity)or to the road itself,and often this can lead to restricted access over a period of days and weeks.In such cases,planning authorities may use modelling to devise suitable diversionary advice for drivers,and to plan any temporary changes to trafﬁc signals or priorities.Berdica(2002)and Taylor et al.(2006)suggest more of a pro-ac-tive approach,proposing that models should be used to test networks for potential vulnerability,before any reduction mate-rialises,identifying links which if reduced in capacity over an extended period1would have a substantial impact on system performance.There are therefore practical requirements for a suitable network model of travel time and route choice impacts of capac-ity changes.The dominant method that has emerged for this purpose over the last decades is clearly the network equilibrium approach,as proposed by Beckmann et al.(1956)and developed in several directions since.The basis of using this approach is the proposition of what are believed to be‘rational’models of behaviour and other system components(e.g.link perfor-mance functions),with site-speciﬁc data used to tailor such models to particular case studies.Cross-sectional forecasts of network performance at speciﬁc road capacity states may then be made,such that at the time of any‘snapshot’forecast, drivers’route choices are in some kind of individually-optimum state.In this state,drivers cannot improve their route selec-tion by a unilateral change of route,at the snapshot travel time levels.The accepted practice is to‘validate’such models on a case-by-case basis,by ensuring that the model—when supplied with a particular set of parameters,input network data and input origin–destination demand data—reproduces current mea-sured mean link trafﬁcﬂows and mean journey times,on a sample of links,to some degree of accuracy(see for example,the practical guidelines in TMIP(1997)and Highways Agency(2002)).This kind of aggregate level,cross-sectional validation to existing conditions persists across a range of network modelling paradigms,ranging from static and dynamic equilibrium (Florian and Nguyen,1976;Leonard and Tough,1979;Stephenson and Teply,1984;Matzoros et al.,1987;Janson et al., 1986;Janson,1991)to micro-simulation approaches(Laird et al.,1999;Ben-Akiva et al.,2000;Keenan,2005).While such an approach is plausible,it leaves many questions unanswered,and we would particularly highlight two: 1.The process of calibration and validation of a network equilibrium model may typically occur in a cycle.That is to say,having initially calibrated a model using the base data sources,if the subsequent validation reveals substantial discrep-ancies in some part of the network,it is then natural to adjust the model parameters(including perhaps even the OD matrix elements)until the model outputs better reﬂect the validation data.2In this process,then,we allow the adjustment of potentially a large number of network parameters and input data in order to replicate the validation data,yet these data themselves are highly aggregate,existing only at the link level.To be clear here,we are talking about a level of coarseness even greater than that in aggregate choice models,since we cannot even infer from link-level data the aggregate shares on alternative routes or OD movements.The question that arises is then:how many different combinations of parameters and input data values might lead to a similar link-level validation,and even if we knew the answer to this question,how might we choose between these alternative combinations?In practice,this issue is typically neglected,meaning that the‘valida-tion’is a rather weak test of the model.2.Since the data are cross-sectional in time(i.e.the aim is to reproduce current base conditions in equilibrium),then in spiteof the large efforts required in data collection,no empirical evidence is routinely collected regarding the model’s main purpose,namely its ability to predict changes in behaviour and network performance under changes to the network/ demand.This issue is exacerbated by the aggregation concerns in point1:the‘ambiguity’in choosing appropriate param-eter values to satisfy the aggregate,link-level,base validation strengthens the need to independently verify that,with the selected parameter values,the model responds reliably to changes.Although such problems–ofﬁtting equilibrium models to cross-sectional data–have long been recognised by practitioners and academics(see,e.g.,Goodwin,1998), the approach described above remains the state-of-practice.Having identiﬁed these two problems,how might we go about addressing them?One approach to theﬁrst problem would be to return to the underlying formulation of the network model,and instead require a model deﬁnition that permits analysis by statistical inference techniques(see for example,Nakayama et al.,2009).In this way,we may potentially exploit more information in the variability of the link-level data,with well-deﬁned notions(such as maximum likelihood)allowing a systematic basis for selection between alternative parameter value combinations.However,this approach is still using rather limited data and it is natural not just to question the model but also the data that we use to calibrate and validate it.Yet this is not altogether straightforward to resolve.As Mahmassani and Jou(2000) remarked:‘A major difﬁculty...is obtaining observations of actual trip-maker behaviour,at the desired level of richness, simultaneously with measurements of prevailing conditions’.For this reason,several authors have turned to simulated gaming environments and/or stated preference techniques to elicit information on drivers’route choice behaviour(e.g. 1Clearly,more sporadic and less predictable reductions in capacity may also occur,such as in the case of breakdowns and accidents,and environmental factors such as severe weather,ﬂoods or landslides(see for example,Iida,1999),but the responses to such cases are outside the scope of the present paper. 2Some authors have suggested more systematic,bi-level type optimization processes for thisﬁtting process(e.g.Xu et al.,2004),but this has no material effect on the essential points above.D.Watling et al./Transportation Research Part A46(2012)167–189169 Mahmassani and Herman,1990;Iida et al.,1992;Khattak et al.,1993;Vaughn et al.,1995;Wardman et al.,1997;Jou,2001; Chen et al.,2001).This provides potentially rich information for calibrating complex behavioural models,but has the obvious limitation that it is based on imagined rather than real route choice situations.Aside from its common focus on hypothetical decision situations,this latter body of work also signiﬁes a subtle change of emphasis in the treatment of the overall network calibration problem.Rather than viewing the network equilibrium calibra-tion process as a whole,the focus is on particular components of the model;in the cases above,the focus is on that compo-nent concerned with how drivers make route decisions.If we are prepared to make such a component-wise analysis,then certainly there exists abundant empirical evidence in the literature,with a history across a number of decades of research into issues such as the factors affecting drivers’route choice(e.g.Wachs,1967;Huchingson et al.,1977;Abu-Eisheh and Mannering,1987;Duffell and Kalombaris,1988;Antonisse et al.,1989;Bekhor et al.,2002;Liu et al.,2004),the nature of travel time variability(e.g.Smeed and Jeffcoate,1971;Montgomery and May,1987;May et al.,1989;McLeod et al., 1993),and the factors affecting trafﬁcﬂow variability(Bonsall et al.,1984;Huff and Hanson,1986;Ribeiro,1994;Rakha and Van Aerde,1995;Fox et al.,1998).While these works provide useful evidence for the network equilibrium calibration problem,they do not provide a frame-work in which we can judge the overall‘ﬁt’of a particular network model in the light of uncertainty,ambient variation and systematic changes in network attributes,be they related to the OD demand,the route choice process,travel times or the network data.Moreover,such data does nothing to address the second point made above,namely the question of how to validate the model forecasts under systematic changes to its inputs.The studies of Mannering et al.(1994)and Emmerink et al.(1996)are distinctive in this context in that they address some of the empirical concerns expressed in the context of travel information impacts,but their work stops at the stage of the empirical analysis,without a link being made to net-work prediction models.The focus of the present paper therefore is both to present theﬁndings of an empirical study and to link this empirical evidence to network forecasting models.More recently,Zhu et al.(2010)analysed several sources of data for evidence of the trafﬁc and behavioural impacts of the I-35W bridge collapse in Minneapolis.Most pertinent to the present paper is their location-speciﬁc analysis of linkﬂows at 24locations;by computing the root mean square difference inﬂows between successive weeks,and comparing the trend for 2006with that for2007(the latter with the bridge collapse),they observed an apparent transient impact of the bridge col-lapse.They also showed there was no statistically-signiﬁcant evidence of a difference in the pattern ofﬂows in the period September–November2007(a period starting6weeks after the bridge collapse),when compared with the corresponding period in2006.They suggested that this was indicative of the length of a‘re-equilibration process’in a conceptual sense, though did not explicitly compare their empiricalﬁndings with those of a network equilibrium model.The structure of the remainder of the paper is as follows.In Section2we describe the process of selecting the real-life problem to analyse,together with the details and rationale behind the survey design.Following this,Section3describes the statistical techniques used to extract information on travel times and routing patterns from the survey data.Statistical inference is then considered in Section4,with the aim of detecting statistically signiﬁcant explanatory factors.In Section5 comparisons are made between the observed network data and those predicted by a network equilibrium model.Finally,in Section6the conclusions of the study are highlighted,and recommendations made for both practice and future research.2.Experimental designThe ultimate objective of the study was to compare actual data with the output of a trafﬁc network equilibrium model, speciﬁcally in terms of how well the equilibrium model was able to correctly forecast the impact of a systematic change ap-plied to the network.While a wealth of surveillance data on linkﬂows and travel times is routinely collected by many local and national agencies,we did not believe that such data would be sufﬁciently informative for our purposes.The reason is that while such data can often be disaggregated down to small time step resolutions,the data remains aggregate in terms of what it informs about driver response,since it does not provide the opportunity to explicitly trace vehicles(even in aggre-gate form)across more than one location.This has the effect that observed differences in linkﬂows might be attributed to many potential causes:it is especially difﬁcult to separate out,say,ambient daily variation in the trip demand matrix from systematic changes in route choice,since both may give rise to similar impacts on observed linkﬂow patterns across re-corded sites.While methods do exist for reconstructing OD and network route patterns from observed link data(e.g.Yang et al.,1994),these are typically based on the premise of a valid network equilibrium model:in this case then,the data would not be able to give independent information on the validity of the network equilibrium approach.For these reasons it was decided to design and implement a purpose-built survey.However,it would not be efﬁcient to extensively monitor a network in order to wait for something to happen,and therefore we required advance notiﬁcation of some planned intervention.For this reason we chose to study the impact of urban maintenance work affecting the roads,which UK local government authorities organise on an annual basis as part of their‘Local Transport Plan’.The city council of York,a historic city in the north of England,agreed to inform us of their plans and to assist in the subsequent data collection exercise.Based on the interventions planned by York CC,the list of candidate studies was narrowed by considering factors such as its propensity to induce signiﬁcant re-routing and its impact on the peak periods.Effectively the motivation here was to identify interventions that were likely to have a large impact on delays,since route choice impacts would then likely be more signiﬁcant and more easily distinguished from ambient variability.This was notably at odds with the objectives of York CC,170 D.Watling et al./Transportation Research Part A46(2012)167–189in that they wished to minimise disruption,and so where possible York CC planned interventions to take place at times of day and of the year where impacts were minimised;therefore our own requirement greatly reduced the candidate set of studies to monitor.A further consideration in study selection was its timing in the year for scheduling before/after surveys so to avoid confounding effects of known signiﬁcant‘seasonal’demand changes,e.g.the impact of the change between school semesters and holidays.A further consideration was York’s role as a major tourist attraction,which is also known to have a seasonal trend.However,the impact on car trafﬁc is relatively small due to the strong promotion of public trans-port and restrictions on car travel and parking in the historic centre.We felt that we further mitigated such impacts by sub-sequently choosing to survey in the morning peak,at a time before most tourist attractions are open.Aside from the question of which intervention to survey was the issue of what data to collect.Within the resources of the project,we considered several options.We rejected stated preference survey methods as,although they provide a link to personal/socio-economic drivers,we wanted to compare actual behaviour with a network model;if the stated preference data conﬂicted with the network model,it would not be clear which we should question most.For revealed preference data, options considered included(i)self-completion diaries(Mahmassani and Jou,2000),(ii)automatic tracking through GPS(Jan et al.,2000;Quiroga et al.,2000;Taylor et al.,2000),and(iii)licence plate surveys(Schaefer,1988).Regarding self-comple-tion surveys,from our own interview experiments with self-completion questionnaires it was evident that travellersﬁnd it relatively difﬁcult to recall and describe complex choice options such as a route through an urban network,giving the po-tential for signiﬁcant errors to be introduced.The automatic tracking option was believed to be the most attractive in this respect,in its potential to accurately map a given individual’s journey,but the negative side would be the potential sample size,as we would need to purchase/hire and distribute the devices;even with a large budget,it is not straightforward to identify in advance the target users,nor to guarantee their cooperation.Licence plate surveys,it was believed,offered the potential for compromise between sample size and data resolution: while we could not track routes to the same resolution as GPS,by judicious location of surveyors we had the opportunity to track vehicles across more than one location,thus providing route-like information.With time-stamped licence plates, the matched data would also provide journey time information.The negative side of this approach is the well-known poten-tial for signiﬁcant recording errors if large sample rates are required.Our aim was to avoid this by recording only partial licence plates,and employing statistical methods to remove the impact of‘spurious matches’,i.e.where two different vehi-cles with the same partial licence plate occur at different locations.Moreover,extensive simulation experiments(Watling,1994)had previously shown that these latter statistical methods were effective in recovering the underlying movements and travel times,even if only a relatively small part of the licence plate were recorded,in spite of giving a large potential for spurious matching.We believed that such an approach reduced the opportunity for recorder error to such a level to suggest that a100%sample rate of vehicles passing may be feasible.This was tested in a pilot study conducted by the project team,with dictaphones used to record a100%sample of time-stamped, partial licence plates.Independent,duplicate observers were employed at the same location to compare error rates;the same study was also conducted with full licence plates.The study indicated that100%surveys with dictaphones would be feasible in moderate trafﬁcﬂow,but only if partial licence plate data were used in order to control observation errors; for higherﬂow rates or to obtain full number plate data,video surveys should be considered.Other important practical les-sons learned from the pilot included the need for clarity in terms of vehicle types to survey(e.g.whether to include motor-cycles and taxis),and of the phonetic alphabet used by surveyors to avoid transcription ambiguities.Based on the twin considerations above of planned interventions and survey approach,several candidate studies were identiﬁed.For a candidate study,detailed design issues involved identifying:likely affected movements and alternative routes(using local knowledge of York CC,together with an existing network model of the city),in order to determine the number and location of survey sites;feasible viewpoints,based on site visits;the timing of surveys,e.g.visibility issues in the dark,winter evening peak period;the peak duration from automatic trafﬁcﬂow data;and speciﬁc survey days,in view of public/school holidays.Our budget led us to survey the majority of licence plate sites manually(partial plates by audio-tape or,in lowﬂows,pen and paper),with video surveys limited to a small number of high-ﬂow sites.From this combination of techniques,100%sampling rate was feasible at each site.Surveys took place in the morning peak due both to visibility considerations and to minimise conﬂicts with tourist/special event trafﬁc.From automatic trafﬁc count data it was decided to survey the period7:45–9:15as the main morning peak period.This design process led to the identiﬁcation of two studies:2.1.Lendal Bridge study(Fig.1)Lendal Bridge,a critical part of York’s inner ring road,was scheduled to be closed for maintenance from September2000 for a duration of several weeks.To avoid school holidays,the‘before’surveys were scheduled for June and early September.It was decided to focus on investigating a signiﬁcant southwest-to-northeast movement of trafﬁc,the river providing a natural barrier which suggested surveying the six river crossing points(C,J,H,K,L,M in Fig.1).In total,13locations were identiﬁed for survey,in an attempt to capture trafﬁc on both sides of the river as well as a crossing.2.2.Fishergate study(Fig.2)The partial closure(capacity reduction)of the street known as Fishergate,again part of York’s inner ring road,was scheduled for July2001to allow repairs to a collapsed sewer.Survey locations were chosen in order to intercept clockwiseFig.1.Intervention and survey locations for Lendal Bridge study.around the inner ring road,this being the direction of the partial closure.A particular aim wasFulford Road(site E in Fig.2),the main radial affected,with F and K monitoring local diversion I,J to capture wider-area diversion.studies,the plan was to survey the selected locations in the morning peak over a period of approximately covering the three periods before,during and after the intervention,with the days selected so holidays or special events.Fig.2.Intervention and survey locations for Fishergate study.In the Lendal Bridge study,while the‘before’surveys proceeded as planned,the bridge’s actualﬁrst day of closure on Sep-tember11th2000also marked the beginning of the UK fuel protests(BBC,2000a;Lyons and Chaterjee,2002).Trafﬁcﬂows were considerably affected by the scarcity of fuel,with congestion extremely low in theﬁrst week of closure,to the extent that any changes could not be attributed to the bridge closure;neither had our design anticipated how to survey the impacts of the fuel shortages.We thus re-arranged our surveys to monitor more closely the planned re-opening of the bridge.Unfor-tunately these surveys were hampered by a second unanticipated event,namely the wettest autumn in the UK for270years and the highest level ofﬂooding in York since records began(BBC,2000b).Theﬂooding closed much of the centre of York to road trafﬁc,including our study area,as the roads were impassable,and therefore we abandoned the planned‘after’surveys. As a result of these events,the useable data we had(not affected by the fuel protests orﬂooding)consisted ofﬁve‘before’days and one‘during’day.In the Fishergate study,fortunately no extreme events occurred,allowing six‘before’and seven‘during’days to be sur-veyed,together with one additional day in the‘during’period when the works were temporarily removed.However,the works over-ran into the long summer school holidays,when it is well-known that there is a substantial seasonal effect of much lowerﬂows and congestion levels.We did not believe it possible to meaningfully isolate the impact of the link fully re-opening while controlling for such an effect,and so our plans for‘after re-opening’surveys were abandoned.3.Estimation of vehicle movements and travel timesThe data resulting from the surveys described in Section2is in the form of(for each day and each study)a set of time-stamped,partial licence plates,observed at a number of locations across the network.Since the data include only partial plates,they cannot simply be matched across observation points to yield reliable estimates of vehicle movements,since there is ambiguity in whether the same partial plate observed at different locations was truly caused by the same vehicle. Indeed,since the observed system is‘open’—in the sense that not all points of entry,exit,generation and attraction are mon-itored—the question is not just which of several potential matches to accept,but also whether there is any match at all.That is to say,an apparent match between data at two observation points could be caused by two separate vehicles that passed no other observation point.Theﬁrst stage of analysis therefore applied a series of specially-designed statistical techniques to reconstruct the vehicle movements and point-to-point travel time distributions from the observed data,allowing for all such ambiguities in the data.Although the detailed derivations of each method are not given here,since they may be found in the references provided,it is necessary to understand some of the characteristics of each method in order to interpret the results subsequently provided.Furthermore,since some of the basic techniques required modiﬁcation relative to the published descriptions,then in order to explain these adaptations it is necessary to understand some of the theoretical basis.3.1.Graphical method for estimating point-to-point travel time distributionsThe preliminary technique applied to each data set was the graphical method described in Watling and Maher(1988).This method is derived for analysing partial registration plate data for unidirectional movement between a pair of observation stations(referred to as an‘origin’and a‘destination’).Thus in the data study here,it must be independently applied to given pairs of observation stations,without regard for the interdependencies between observation station pairs.On the other hand, it makes no assumption that the system is‘closed’;there may be vehicles that pass the origin that do not pass the destina-tion,and vice versa.While limited in considering only two-point surveys,the attraction of the graphical technique is that it is a non-parametric method,with no assumptions made about the arrival time distributions at the observation points(they may be non-uniform in particular),and no assumptions made about the journey time probability density.It is therefore very suitable as aﬁrst means of investigative analysis for such data.The method begins by forming all pairs of possible matches in the data,of which some will be genuine matches(the pair of observations were due to a single vehicle)and the remainder spurious matches.Thus, for example,if there are three origin observations and two destination observations of a particular partial registration num-ber,then six possible matches may be formed,of which clearly no more than two can be genuine(and possibly only one or zero are genuine).A scatter plot may then be drawn for each possible match of the observation time at the origin versus that at the destination.The characteristic pattern of such a plot is as that shown in Fig.4a,with a dense‘line’of points(which will primarily be the genuine matches)superimposed upon a scatter of points over the whole region(which will primarily be the spurious matches).If we were to assume uniform arrival rates at the observation stations,then the spurious matches would be uniformly distributed over this plot;however,we shall avoid making such a restrictive assumption.The method begins by making a coarse estimate of the total number of genuine matches across the whole of this plot.As part of this analysis we then assume knowledge of,for any randomly selected vehicle,the probabilities:h k¼Prðvehicle is of the k th type of partial registration plateÞðk¼1;2;...;mÞwhereX m k¼1h k¼1172 D.Watling et al./Transportation Research Part A46(2012)167–189。

2024年江苏新高考一卷英语试题.doc

2024年江苏新高考一卷英语试题2024年江苏新高考一卷英语试题及答案例：How much is the shirt?A.E19.15.B.E9.18.C.E9.15.答案是C.1.What is Kate doing?A.Boarding a flight.B.Arranging a tripC.Seeing a friend off.2.What are the speakers talking about?A.pop star.B.An old songC.A radio program3.What will the speakers do today?A.Goto an art show.B.Meet the mans aunt.C.Eat out with Mark4.What does the man want to do?A.Cancel an order.B.Ask for a receipt.C.Reschedule a delivery5.When will the next train to Bedford leave?A.At 9:45.B.At 10:15C.At 11:00.第二节 (共15小题;每小题1.5分，满分22.5分)听下面5段对话或独白。

每段对话或独白后有几个小题，从题中所给的 A 、B 、C 三个选项中选出最佳选项。

听每段对话或独白前，你将有时间阅读各个小题，每小题5秒钟;听完后，各小题将给出5秒钟的作答时间。

每段对话或独白读两遍。

听第6段材料，回答第6、7题。

6.What will the weather be like today?A.StormyB.SunnyC.Foggy7.What is the man going to do?A.Plant a tree.B.Move his carC.Check the map听第7段材料，回答第8至10题。

自动化专业英语教程第2版王宏文主编翻译PART2

P2U1A The World of Control
第二部分第一单元课文A 控制的世界
控制系统的分类和术语控制系统可根据系统本 R(s) C(s) 身或其参量进行分类：控制对象控制元件开环和闭环系统（如图 a) 2-1A-1）：开环控制系统是 R(s) C(s) 控制行为与输出无关的系统。控制对象控制元件 + 而闭环系统，其被控对象的输入在某种程度上依赖于实际的输出。因为输出以由反反馈元件馈元件决定的一种函数形式 b) 反馈回来，然后被输入减去。闭环系统通常是指负反馈系图2-1A-1 开环控制系统和闭环控制系统统或简称为反馈系统。
P2U1A The World of Control
第二部分第一单元课文A 控制的世界
5. 参考译文
A 控制的世界简介控制一词的含义一般是调节、指导或者命令。控制系统大量存在于我们周围。在最抽象的意义上说，每个物理对象都是一个控制系统。控制系统被人们用来扩展自己的能力，补偿生理上的限制，或把自己从常规、单调的工作中解脱出来，或者用来节省开支。例如在现代航空器中，功率助推装置可以把飞行员的力量放大，从而克服巨大的空气阻力推动飞行控制翼面。飞行员的反应速度太慢，如果不附加阻尼偏航系统，飞行员就无法通过轻微阻尼的侧倾转向方式来驾驶飞机。自动飞行控制系统把飞行员从保持正确航向、高度和姿态的连续操作任务中解脱出来。没有了这些常规操作，飞行员可以执行其他的任务，如领航或通讯，这样就减少了所需的机组人员，降低了飞行费用。在很多情况下，控制系统的设计是基于某种理论，而不是靠直觉或试凑法。控制系统能够用来处理系统对命令、调节或扰动的动态响应。控制理论的应用基本上有两个方面：动态
July 28, 2007

【转】Isoform

【转】Isoform expre...Exon-centric DEDSGseq summary:This programs uses gapped alignments to the genome to generate differential splicing for groups of technical and biological replicates in two treatments. You can't compare just two samples, two samples per group is the minimum.It generates a ranking of differentially spliced genes using their negative binomial statistic which focuses of difference in expression. The NB statistic is provided per gene and per exon. A threshold used in the paper is NB > 5. The program doesn't support reconstruction of isoforms or quantification of specific isoforms, which apparently is computationally harder.I found it easy to get it to run using the example data provided and the instructions. You need to run a preparation step on the gene annotation. Starting from BAM files, you also need to run two preparation steps on each library, first to convert it to BED, and then to get the counts.While the paper clearly says that transcript annotation information is not necessary for the algorithm, you do need to provide a gene annotation file in refFlat format, which the output is based on.The developers are unresponsive so no help is at hand if you get stuck.DEXseq summaryThis is similar to DSGseq and Diffsplice insofar as the isoform reconstruction and quantification are skipped and differential exon expression is carried out. Whereas the other two tools say that they don't need an annotation for their statistics, this program is based on only annotated exons, and uses the supplied transcript annotation in the form of a GFF file.It also needs at least two replicates per group.I found the usage of this program extremely tedious (as a matlab person). To install it you need to also install numpy and HTSeq. For preparing the data (similarly to DSGseq) you need to do a preparation step on the annotations, and another preparation step for every sample separately which collects the counts (both using python scripts). Then you switch to R, where you need to prepare something called an ExonCountSet object. To do this you need to first make a data.frame R object with the files that come out of the counting step. Yo also need to define a bunch of parameters in the R console. Then you can finally run the analysis. Despite the long instructional PDF, all this is not especially clear, and it's a rather tedious process compared to the others I've tried so far. In the end, I ran makeCompleteDEUAnalysis, and printed out a table of the results. I tried to plot some graphics too, but couldn't because "semi-transparency is not supported on this device". However, there's an extremely useful function that creates a browsable HTML directory with the graphics for all the significant genes. If anyone wants a copy of the workflow I used, send me a message, trying to figure it out might take weeks, but after you get the hang of it, this program is really useful.DiffSplice summaryThis is a similar approach for exon-centric differential expression to DEXseq and DSGseq (no attempt to reconstruct or quantify specific isoforms). Also supports groups of treatments, minimum 2 samples per group. The SAM inputs and various rather detailed parameters are supplied in two config files. I found this very convenient. In the data config file you can specify treatment group ID, individual IDs, and sample IDs, which determine how the shuffling in their permuation test is done. It was unclear to me what the sample IDs are (as opposed to the individual ID).DiffSplice prefers alignments that come from TopHat or MapSplice because it looks for the XS (strand) tag which BWA doesn't create. There's no need to do a separate preparation step on the alignments. However, if you want you can separate the three steps of the analysis using parameters for selective re-running. This program is user friendly and the doc page makes sense.On the downside, when the program has bad inputs or stops in the middle there's no errors or warnings - it just completes in an unreasonably short time and you get no results.Diffsplice appears to be sensitive to rare deviations from the SAM spec, because while I'm able to successfully run it on mini datasets, the whole datasets are crashing it. I ran Picard's FixMateInformation and ValidateSamFile tools to see if they will make my data acceptable (mates are fine, and sam files are valid! woot), but no dice. It definitely isn't due to the presence of unaligned reads.SplicingCompass summary:SplicingCompass would be included together with DEXseq, DiffSplice, and DSGseq, insofar as it's an exon-centric differential expression tool. However, unlike DEXseq and DSGseq, it provides novel junctions as well. Unlike DiffSplice, it does use an annotation. The annotation + novel detection feature of this program is pretty attractive.This is an R package, though as far as i can tell, it's not included in bioconductor. Personally I find it tedious to type lines upon lines of commands into R, and would much prefer to supply a configuration file and run one or a few things on the command line. Alas. Here, at least the instructions are complete, step by step, and on a "for dummies" level. Great.This tool is based on genome alignments. You basically have to run Tophat, because the inputs are both bam files and junction.bed files which Tophat provides. A downside is that you basically have to use the GTF annotation that they provide which is based on UCSC ccds genes. If you want to use ensembl or something else, you meed to email the developer for an untested procedure that might get you a useable annotation at the end (directly using an ensembl GTF doesn't work).Another problem is that I got no significant exons at the end of the analysis:>sc=initSigGenesFromResults(sc,adjusted=TRUE,threshold=0.1)Error in order(df[, pValName]) : argument 1 is not a vectorI'm still unsure as to whether this is due to some mistake or because this tool is extremely conservative.Transcriptome based reconstruction and quantificationeXpress summary:This program can take a BAM file in a stream, or a complete SAM or BAM file.It produces a set of isoforms and a quantification of said isoforms. There is no built in differential expression function (yet) so they recommend inputting the rounded effective counts that eXpress produces into EdgeR or DEGSeq. No novel junctions or isoforms are assembled.I used bowtie2 for the alignments to the transcriptome. Once you have those, using eXpress is extremely simple and fun. There's also a cloud version available on Galaxy, though running from the command line is so simple in this case I don't see any advantage to that. Definite favorite!SailFish summary:This program is unique insofar as it isn't based on read alignment to the genome or the transcriptome. It is based on k-mer alignment, which is based on a k-merized reference transcriptome. It is extremely fast. The first, indexing step took about 20 minutes. This step only needs to be run once per reference transcriptome for a certain k-mer size. The second, quant step took from 15 minutes to 1.5 hours depending on the library. The input for the quant step is fastq's as opposed to bam files. No novel junctions or isoforms are assembled.Like eXpress, there is no built in differential expression function. I used the counts from the non-bias-corrected (quant.sf) output file as inputs for DESeq and got reasonable results.The method is published on arXiv, and has been discussed in Lior Pachter's blog. According to the website the manuscript has been submitted for publication. The program is quite user friendly.RSEM +EBSeq summary:This also generates isoforms and quantifies them. It also needs to be followed by an external cont-based DE tool - they recommend EBSeq, which is actually included in the latest RSEM release, and can be run from the command line easily.RSEM can't tolerate any gaps in your transcriptome alignment, including the indels bowtie2 supports. Hence, you either need to align ahead of time with bowtie and input a SAM/BAM, or use the bowtie that's built into the RSEM call and input a fsta/fastq. For me this was unfortunate because we don't keep fastq files on hand (only illumina qseq files) which bowtie doesn't take as inputs. However, it does work! I successfully followed the instructions to execute EBSeq, which is conveniently included as an RSEM function, and gives intelligible results. Together, this workflow is complete.An advantage of RSEM is that it supplies expression relative to the whole transcriptome (RPKM, TPM) and, if supplied with a transcript-to-gene mapping, it also supplies relative expression of transcripts within genes (PSI). ie. transcript A comprises 70% of the expression of gene X, transcript B comprises 20 %, etc. MISO is the only other transcript-based program, as far as I know, that provides this useful information.BitSeq summary:This, like DEXSeq, is an R bioconductor package. I found the manual a lot easier to understand than DEXSeq.They recalculate the probability of each alignment, come up with a set of isoforms, quantify them, and also provide a DE function. In this way, it is the most complete tool I've tried so far, since all the other tools have assumed, skipped, or left out at least one of these stages. Also, BitSeq automatically generates results files, which is useful for people that don't know R. One annoying thing is that (as far as I know) you have to use sam files.For running BitSeq I used the same bowtie2 alignments to the transcriptome as for eXpress. You need to run the function getExpression on each sample separately. Then you make a list of the result objects in each treatment group and run the function getDE on those.Genome based reconstruction and quantificationiReckon summary:iReckon generates isoforms and quantifies them. However, this is based on gapped alignment to the genome (unlike eXpress, RSEM and BitSeq which are based on alignments to the transcriptome). It doesn't have a built in DE function, so each sample is run separately.This tool is a little curious because it requires both a gapped alignment to the genome, and the unaligned reads in fastq or fasta format with a reference genome. Since it requires a BWA executable, it's doing some re-alignment. iReckon claims to generate novel isoforms with low false positives by taking into consideration a whole slew of biological and technical biases.One irritating thing in getting the program running is that you need to re-format your refgene annotation file using an esoteric indexing tool from the Savant genome browser package. If you happen to use IGV, this is a bit tedious. Apparently, this will change in the next version. Also, iReckon takes up an enormous amount of memory and scratch space. For a library with 350 million reads, you would need about 800 G scratch space. Apparently everything (run time, RAM, and space) is linear to the number of reads, so this program would be a alright for a subset of the library or for lower coverage libraries.Cufflinks + cuffdiff2 summary:This pipeline, like iReckon, is based on gapped alignment to the genome. It requires the XS tag, so if you're not using tophat to align your RNA, you need to add that tag. I also found out that our gapped aligner introduces some pesky 0M and 0N's in the cigars, since cufflinks doesn't tolerate these. But with these matters sorted out, it's pretty easy to use.I like the versatility. You can run cufflinks for transcriptome reconstruction and isoform quantification in a variety of different modes. For example, with annotations and novel transcript discovery, with annotations and no novel discovery, with no annotations, and with annotations to be ignored in the output. For differential expression, cuffdiff 2 can be run with the results of the transcript quantification from cufflinks to include novel transcripts, or, it can be run directly from the alignment bam files with an annotation. Unlike the exon-based approaches, you don't need to have more than one library in each treatment group, (ie. you can do pairwise comparisons) though if you do it's better to keep them separate than to merge them. The problem here is that the results of cuffdiff are so numerous that it's not easy to figure out what you need in the end. Also, not all the files include the gene/transcript names so you need to do a fair bit of command line munging. There's also cummeRbund, which is a visualization package in R that so far seems to work ok.。

权威的证据英语作文

权威的证据英语作文Title: The Significance of Authoritative Evidence。

In the realm of academia, professional discourse, and policymaking, the reliance on authoritative evidence stands as a cornerstone for informed decision-making and the advancement of knowledge. Authoritative evidence, characterized by its credibility, reliability, and validity, holds immense importance in various domains, including scientific research, legal proceedings, and public policy formulation. This essay explores the significance of authoritative evidence and its pivotal role in shaping our understanding of the world.First and foremost, authoritative evidence serves asthe bedrock of scientific inquiry. In the scientific community, empirical research grounded in robust methodologies and supported by authoritative evidence forms the basis for theory development and empirical validation. Scientific progress hinges upon the meticulous collection,analysis, and interpretation of data derived from reliable sources. Peer-reviewed journals, reputable research institutions, and established scientific organizations are instrumental in vetting and disseminating authoritative evidence to the broader scientific community, thereby fostering a culture of transparency and accountability.Moreover, authoritative evidence plays a critical rolein ensuring the fairness and integrity of legal proceedings. In the realm of law, the admissibility of evidence is contingent upon its relevance, reliability, and authenticity. Judges and jurors rely on authoritative evidence presented by expert witnesses, forensic specialists, and credible testimonies to adjudicate legal disputes and dispense justice fairly. The exclusion of hearsay and the adherence to evidentiary standardssafeguard the rights of litigants and uphold the principles of due process within the judicial system.Furthermore, authoritative evidence serves as alinchpin for evidence-based policymaking and governance. Policymakers, lawmakers, and public officials rely onempirical research, statistical analyses, and policy evaluations to craft effective policies, allocate resources efficiently, and address societal challenges. Evidence-based interventions informed by authoritative evidence have been instrumental in improving public health outcomes, mitigating socioeconomic disparities, and promoting environmental sustainability. By grounding policy decisions in empirical evidence rather than ideological beliefs or political expediency, policymakers can foster public trust and enhance the efficacy of governance mechanisms.Additionally, authoritative evidence plays a pivotal role in fostering public trust and combating misinformation in an era characterized by information overload and digital disinformation. With the proliferation of social media platforms and online forums, misinformation and fake news have become pervasive, undermining public discourse and eroding trust in institutions. Fact-checking organizations, independent journalists, and academic researchers play a crucial role in debunking falsehoods and verifying the veracity of claims through rigorous fact-checking and evidence-based analysis. By promoting media literacy andcritical thinking skills, individuals can discern credible sources from dubious ones and make informed decisions based on authoritative evidence.In conclusion, authoritative evidence serves as a cornerstone for informed decision-making, empirical inquiry, and societal progress across various domains. Whether inthe realms of science, law, policymaking, or public discourse, the reliance on credible, reliable, and valid evidence is indispensable for advancing knowledge,upholding justice, and fostering public trust. As we navigate an increasingly complex and interconnected world, the pursuit and dissemination of authoritative evidence remain paramount in shaping our understanding of realityand informing our collective actions for the betterment of society.。

Identification of differentially-expressed genes potentially implicated in

Identi ﬁcation of differentially-expressed genes potentially implicated in drought response in pitaya (Hylocereus undatus )by suppression subtractive hybridization and cDNA microarray analysisQing-Jie Fan 1,Feng-Xia Yan 1,Guang Qiao,Bing-Xue Zhang,Xiao-Peng Wen ⁎Guizhou Key Laboratory of Agricultural Bioengineering,Guizhou University,Guiyang 550025,Guizhou Province,PR Chinaa b s t r a c ta r t i c l e i n f o Article history:Accepted 29August 2013Available online 27September 2013Keywords:Pitaya (Hylocereus undatus )Drought stressDifferentially-expressed genes cDNA microarraySuppression subtractive hybridizationDrought is one of the most severe threats to the growth,development and yield of plant.In order to unravel the molecular basis underlying the high tolerance of pitaya (Hylocereus undatus )to drought stress,suppression subtractive hybridization (SSH)and cDNA microarray approaches were ﬁrstly combined to identify the potential important or novel genes involved in the plant responses to drought stress.The forward (drought over drought-free)and reverse (drought-free over drought)suppression subtractive cDNA libraries were constructed using in vitro shoots of cultivar ‘Zihonglong ’exposed to drought stress and drought-free (control).A total of 2112clones,among which half were from either forward or reverse SSH library,were randomly picked up to construct a pitaya cDNA microarray.Microarray analysis was carried out to verify the expression ﬂuctua-tions of this set of clones upon drought treatment compared with the controls.A total of 309expressed se-quence tags (ESTs),153from forward library and 156from reverse library,were obtained,and 138unique ESTs were identi ﬁed after sequencing by clustering and blast analyses,which included genes that had been previously reported as responsive to water stress as well as some functionally unknown genes.Thirty six genes were mapped to 47KEGG pathways,including carbohydrate metabolism,lipid metabolism,energy me-tabolism,nucleotide metabolism,and amino acid metabolism of pitaya.Expression analysis of the selected ESTs by reverse transcriptase polymerase chain reaction (RT-PCR)corroborated the results of differential screening.Moreover,time-course expression patterns of these selected ESTs further con ﬁrmed that they were closely responsive to drought treatment.Among the differentially expressed genes (DEGs),many are re-lated to stress tolerances including drought tolerance.Thereby,the mechanism of drought tolerance of this pitaya genotype is a very complex physiological and biochemical process,in which multiple metabolism pathways and many genes were implicated.The data gained herein provide an insight into the mechanism underlying the drought stress tolerance of pitaya,as well as may facilitate the screening of candidate genes for drought tolerance.©2013Elsevier B.V.All rights reserved.1.IntroductionAbiotic stress is a chief cause of crop loss worldwide,reducing aver-age yields for most major crops by more than 50%(Bray et al.,2000).Particularly,water de ﬁcit is becoming an increasing problem with global climate change and growing water scarcity.Drought stress is un-doubtedly the most severe abiotic stress factor limiting the crop produc-tivity around the world (Bohnert et al.,1995;Mafakheri et al.,2010).Today,intensive industrial farming in China is thought to lead to a steeply increasing vulnerability to climate change,and around half of the total area is faced with the challenge of water scarcity.Thus,an understanding of drought stress in relation to crop growth and develop-ment is of high importance for sustainable agriculture.To cope with environmental stress,plants have evolved speci ﬁc acclimation and adaptations including a number of physiological andGene 533(2014)322–331Abbreviations:AAP8,gene encoding amino acid permease 8;ABA,abscisic acid;ABAR PYL8,gene encoding abscisic acid receptor PYL8;ACTB ,gene encoding β-actin;CAM,crassulacean acid metabolism;CAT ,catalase;DEGs,differentially expressed genes;ESTs,expressed sequence tags;FDR,false discovery rate;FWC,ﬁeld water capacity;GGAT ,gene encoding glutamate –glyoxylate aminotransferase;GGT ,gene encoding gamma-glutamyltranspeptidase;GO,gene ontology;GSH,glutathione;GSR ,gene encoding glutathione reductase;ISSR,inter-simple sequence repeat;OSB –CoA ligase ,gene encoding O-succinylbenzoate –CoA ligase;PEG,polyethylene glycol;PEPC ,gene encoding phosphoenolpyruvate carboxylase;PLA 2,gene encoding phospholipase A 2;PLD α,Phospholipase D α;qRT-PCR,quantitative real-time PCR;RT-PCR,reverse transcrip-tase polymerase chain reaction;SHMT ,gene encoding glycine hydroxymethyltransferase;SSH,suppression subtractive hybridization;TAT ,gene encoding tyrosine aminotransferase;TPS ,gene encoding trehalose-6-phosphate synthase;γ-TMT ,gene encoding gamma-tocopherol methyltransferase.⁎Corresponding author.Tel.:+868518298514;fax:+868513865027.E-mail address:xpwensc@ (X.-P.Wen).1These authors contributed equally to thiswork.0378-1119/$–see front matter ©2013Elsevier B.V.All rights reserved./10.1016/j.gene.2013.08.098Contents lists available at ScienceDirectGenej o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /g e nemetabolic responses,which allow them to survive the adverse condi-tions(Bohnert et al.,1995;Govind et al.,2009).In response to drought stress,plants can exhibit either drought escape or drought resistance mechanisms,with resistance further classiﬁed into drought avoidance and drought tolerance(Harb et al.,2010;Levitt,1980;Price et al., 2002).Drought escape is described as the ability of plants to complete the life cycle before severe stress starts.Drought avoidance is by main-tenance of high tissue water potential despite a soil water deﬁcit. Drought tolerance is the ability to withstand water deﬁcit with low tissue water potential(Ingram and Bartels,1996).In recent years,much molecular information has been generated on the response of plants to environmental stresses.Plant drought toler-ance is a quantitative trait controlled by multiple genes,and the com-plex biological processes need to be analyzed at a systematic level. Molecularly,plants may respond to drought stress by altering the expression of genes,related to different pathways associated with stress perception,signal transduction,regulators and synthesis of a number of compounds(Harb et al.,2010;Ramanjulu and Bartels,2002;Shinozaki and Yamaguchi-Shinozaki,2007;Sreenivasulu et al.,2007).Knowledge of drought responses at the molecular level is available in many plants (Nakashima et al.,2009;Qiao et al.,2012;Shinozaki et al.,2003; Umezawa et al.,2004).Further,some mutants,even in some drought-sensitive species can withstand longer or shorter periods of water deﬁcit.Illumination of molecular mechanism underlying the higher tolerance to drought stress will contribute to our knowledge of toler-ance to stress,as well as to the genetic improvement for stress tolerance (Krishnan and Pereira,2008).Pitaya(Hylocereus undatus),a member of the genus Hylocereus (Cactaceae),is perennial climbing cactus plant native to tropical areas of northern,central,and southern America.This species characterizes in bearing attractive,highly nutritious fruit(Barbeu,1990;Cai et al., 2008;Zhuang et al.,2012).Moreover,it can withstand prolonged drought,thus it is regarded as a high potential for horticultural develop-ment,especially in areas where were seriously striken by drought stress (Mohamed-Yasseen,2002;Viñas et al.,2012;Fan et al.,2013).Pitaya in-dustry has expanded greatly in the past two decades,notably in arid or semiarid regions(Mizrahi and Nerd,1999;Mohamed-Yasseen,2002). Also,it has been a thriving fruit crop in South China,especially in the karst regions,which are frequently exposed to severe drought stress (Wang and Zheng,2004).An elite pitaya genotype,cultivar‘Zihonglong’has been obtained from the previous work,and it demonstrates both good fruit quality and superior tolerance to drought stress in spite of no obvious difference in the morphological and anatomical structure of succulent stem with other genotypes(unpublished data),which makes it an ideal material for illumination of the molecular mechanism on high tolerance to drought and for identiﬁcation of the drought-related genes in this crop.To the best of our knowledge,no information concerning the mo-lecular mechanism underlying the higher tolerance to drought stress and identiﬁcation of drought-related genes has yet been reported in pitaya so far.Therefore,elucidation of the molecular response of this cultivar to drought stress at transcriptomic level would provide us with a better understanding of the acclimation process.Suppression subtractive hybridization(SSH)had been proven to be a potential technology for exploring differentially expressed genes(DEGs) implicated in abiotic stress including drought stress(Peng et al.,2012; Qiao et al.,2012).To decipher the complex molecular mechanism concerning the high tolerance to drought stress of pitaya,currently,a strategy of combining SSH with cDNA microarray was used to analyze the response of cultivar‘Zihonglong’to in vitro drought stress at transcriptomic level.Characterization of their stress responsive nature indicated that many of the isolated genes involved in adverse stress response during the water deﬁcit progress.The results generated from this work might be helpful for better understanding the molecular basis of plant tolerance to water stress,which has also beenﬁrstly documented in pitaya.2.Materials and methods2.1.Plant materials and stress treatmentsIn vitro plantlets,which were proven to be genetically homogeneous as detected by inter-simple sequence repeat(ISSR)markers,of red-purple pitaya(H.undatus‘Zihonglong’)were used in the present exper-iments.The rooted plantlets were transplanted to culture apparatus, and drought stress was induced by adding10%polyethylene glycol (PEG,MW6000)to the culture solution,by which the water potentials of stems were similar to that caused by30%–40%ﬁeld water capacity (FWC)in pitaya orchard.The succulent stems were collected separately after12h,24h,3d,5d,7d,10d,15d and20d for a total of eight sampling points after the start of stress treatment,immediately frozen in liquid nitrogen and stored at−80°C.Plantlets without PEG in the hydroponic solution were harvested as controls.2.2.RNA isolation and subtractive cDNA library constructionTotal RNA was extracted separately from succulent stems for each sampling point using RNAprep pure Plant Kit(Tiangen,China) according to the manufacturer's instructions.Both forward(PEG treat-ment as tester and control as driver)and reverse(control as tester and PEG treatment as driver)SSH cDNA libraries were constructed. For SSH,equal amounts of total RNA for from each stress sample or control were mixed and the cDNA was reversely transcribed from the mixed total RNA using the Super SMART™PCR cDNA Synthesis Kit (Clontech,United States).The tester and driver cDNA populations were digested with the restriction enzyme RsaI and then ligated to dif-ferent adaptors.PCR-selected cDNA subtraction was performed using PCR-Select™cDNA Subtraction Kit(Clontech,United States)according to the manufacturer's protocol,and the subtracted PCR products gener-ated by SSH were puriﬁed by QIAquick PCR Puriﬁcation Kit(Qiagen, Germany).The cDNA fragments were cloned into the T/A cloning vector pMD18-T(Takara,Japan)and transformed into Escherichia coli DH-5αcompetent cells(Takara,Japan).Ampicillin-resistant colonies were ran-domly picked up and grown in liquid Luria–Bertani(LB)medium with ampicillin.2.3.cDNA microarray preparation and hybridizationPlasmid DNA used as template for PCR reactions was isolated from transformed bacterial cell lysates.The cDNA inserts were ampliﬁed by PCR using the primer pair corresponding toﬂanking the cloning site of pMD18-T(primer F:5′-CGCCAGGGTTTTCCCAGTCACGAC-3′;primer R: 5′-GAGCGGATAACAATTTCACACAGG-3′;Sangong,China).PCR product from50μl of the PCR ampliﬁcation reaction mixtures wasprecipitated Fig.1.PCR dectection of randomly selected clones from SSH library.M:D2000marker(Tiangen,China);Lanes1–24:PCR products from different clones.323Q.-J.Fan et al./Gene533(2014)322–331324Q.-J.Fan et al./Gene533(2014)322–331Table1Expression proﬁles of ESTs regulated by drought stress in pitaya.Clone ID Annotations Length E-value Expression proﬁles12h24h3d5d7d10d15d20dP007G05Abscisic acid receptor PYL8160 5.13E−12 1.00 1.88 2.98 2.80 5.28 1.5411.98 2.85 P006F72Acylamino-acid-releasing enzyme397 1.41E−420.55 1.21 1.030.630.440.390.32 1.24 P015O10Alanine-2-oxoglutarate aminotransferase24338.78E−840.420.360.350.64 1.030.460.220.55 P006F62Albumin-2231 1.59E−120.56 1.08 2.39 2.25 3.820.320.970.23 P010J43Alpha-1,4-glucan phosphorylase H isozyme457 2.05E−76 1.43 2.46 1.42 1.55 1.39 1.38 3.00 2.19 P010J65Amino acid permease353 4.94E−09 1.25 2.27 3.23 3.44 6.17 1.54 6.34 3.07 P011K70AMP dependent CoA ligase639 1.04E−82 1.61 1.12 1.78 2.43 1.67 2.40 1.18 2.19 P001A11Ankyrin repeat-containing protein404 3.05E−150.360.160.200.180.350.580.300.28 P013M89Anthocyanidin-3-glucoside rhamnosyltransferase247 1.16E−15 1.150.650.670.75 1.200.250.640.29 CONTIG-17Auxin-binding protein ABP19a779 1.21E−43 2.07 3.17 1.120.990.44 2.320.86 1.59 CONTIG-03Beta-galactosidase828 2.23E−99 1.67 2.09 4.54 1.93 2.12 1.40 2.46 1.95 P018R41BTB/POZ and TAZ domain-containing protein4478 3.45E−65 1.560.77 1.50 2.16 4.47 2.52 1.13 1.01 P009I77Catalase522 6.07E−570.740.420.470.670.880.800.570.73 CONTIG-27Catalase698 5.38E−1240.730.350.400.660.53 1.070.48 1.04 CONTIG-10Cell wall-associated hydrolase1088 2.71E−12 2.43 2.190.980.590.26 1.550.80 2.50 P012L05Chlorophyll a/b binding protein362 3.75E−320.450.75 1.000.540.410.450.620.60 CONTIG-24Chlorophyllase-1726 6.52E−15 1.06 1.53 2.73 1.60 1.56 2.50 3.50 2.57 P008H53Chloroplast chlorophyll a/b binding protein cab-BO3223 2.02E−280.790.680.700.620.670.390.490.53 P019S60Cinnamyl alcohol dehydrogenase145 4.98E−10 2.760.190.77 2.49 2.82 1.890.780.78 P011K92Clathrin heavy chain1376 3.92E−16 1.12 1.35 1.32 1.71 1.51 2.38 2.31 2.07 P008H13Conserved hypothetical protein548 2.60E−05 2.190.95 4.170.87 1.38 5.31 2.01 2.88 P013M50CTP synthase401 5.48E−20 2.69 1.17 1.01 1.57 2.42 1.09 1.99 1.02 P018R40Phospholipase A23738.46E−37 3.880.88 1.83 3.8111.32 1.42 1.54 1.37 P016P05Cysteine-rich repeat secretory protein55337 1.18E−30 2.150.360.640.450.110.620.13 P008H42Cytochrome b6/f complex subunit V389 1.09E−14 1.320.490.40 1.050.57 1.820.59 1.47 P007G64Dehydration responsive domain partial810 4.30E−44 1.37 1.120.670.530.92 2.630.94 2.53 P012L25Dihydroxy-acid dehydratase572 6.60E−85 1.090.86 1.040.540.860.390.460.49 P009I03DNA J protein homolog2266 1.77E−13 2.12 2.38 1.12 1.000.42 1.570.76 1.27 CONTIG-26Endo-beta-1,3-glucanase4268.97E−20 2.860.230.58 2.070.67 1.78 1.43 1.02 P002B64FOUR LIPS transcription factor R2R3-MYB604 1.03E−370.660.810.490.590.390.570.83 1.10 P010J32Gamma-tocopherol methyltransferase482 3.36E−35 1.66 1.928.63 2.57 2.89 1.31 1.92 1.71 CONTIG-11Gamma-tocopherol methyltransferase649 2.12E−14 1.57 2.5414.98 3.83 5.42 2.877.98 3.18 P008H43Gamma-tocopherol methyltransferase6018.72E−72 2.20 2.888.75 4.05 6.85 2.29 6.75 3.65 CONTIG-14Germacrene D synthase1212 3.85E−39 1.14 1.12 2.08 3.5010.76 1.3546.47 1.64 P009I71Glucan endo-1,3-beta-D-glucosidase444 1.32E−610.960.92 1.060.880.400.390.480.40 P018R65Glucose-6-phosphate1-epimerase488 1.43E−730.73 1.000.940.640.740.410.470.59 P007G87Glutathione reductase550 1.05E−103 1.540.97 1.31 1.60 2.030.320.800.37 P009I44-2Long chain acyl-synthetase2230 3.11E−280.56 1.04 1.20 2.32 2.36 1.10 1.37 1.57 CONTIG-04Mitochondrial protein943 1.31E−93 1.69 1.980.920.640.42 1.340.94 1.72 P007G56–No hits–2690.550.50 1.180.680.690.350.450.21 P002B26–No hits–3500.90 2.42 2.18 2.24 3.07 1.34 3.74 1.18 CONTIG-15–No hits–5540.83 1.53 2.79 3.217.47 1.4912.57 2.76 CONTIG-12–No hits–5780.90 1.98 2.86 2.93 5.34 1.6811.44 2.83 CONTIG-07–No hits–5400.84 1.86 3.18 3.27 5.90 1.6714.07 2.96 P022V71Nitrate transporter579 3.14E−85 1.770.710.340.700.830.320.630.33 P019S51Nucleic acid binding589 1.16E−460.750.89 1.08 2.17 3.510.66 3.070.70 P015O61Pentatricopeptide repeat-containing protein659 4.62E−510.310.270.600.760.250.620.71 1.10 P009I44-1Phosphoenolpyruvate carboxylase302 2.17E−420.56 1.04 1.20 2.32 2.36 1.10 1.37 1.57 P001A84Phosphoenolpyruvate carboxylase266 1.08E−400.44 1.22 1.12 2.90 2.95 1.01 1.69 2.39 P019S16Phosphoenolpyruvate carboxylase500 1.29E−880.69 1.34 1.11 2.84 2.30 1.34 1.61 2.43 P011K34Phosphoenolpyruvate carboxylase5217.09E−500.43 1.27 1.44 3.20 2.33 1.70 2.56 3.33 P013M56Phosphoethanolamine N-methyltransferase4837.24E−860.380.390.990.580.400.270.290.12 P020T95Polygalacturonase-inhibiting protein5187.09E−660.370.220.210.450.260.480.230.23 P016P55Probable protein phosphatase2c10481 3.89E−77 1.96 1.23 1.79 1.72 1.43 1.35 2.16 1.08 P014N27-1Probable receptor protein kinase At1g49730190 1.68E−310.640.640.580.680.700.120.160.24 CONTIG-21Protease inhibitor II317 6.20E−19 1.72 1.41 2.67 1.250.52 1.080.380.88 P016P24Protein ECERIFERUM1500 1.16E−86 1.52 2.31 1.79 3.69 4.94 1.75 1.37 1.00 P010J14Protein h1ﬂk553 1.17E−630.610.390.780.460.490.640.340.58 P010J55Protochlorophyllide reductase,chloroplastic799 3.06E−1190.320.300.570.310.400.140.210.14 P012L72Purple acid phosphatase224448.81E−460.56 1.11 1.99 2.68 5.04 1.09 2.290.98 P012L56Retropepsin protein387 1.82E−420.660.500.810.80 1.50 2.28 2.16 1.54 P010J57Ribonuclease J460 5.78E−420.470.570.600.430.580.170.130.13 P008H80Ribosome-inactivating protein:Dianthin30315 2.69E−08 1.610.47 3.70 2.98 5.76 1.76 1.37 2.57 P012L14Secologanin synthase4417.93E−43 1.45 1.66 1.97 1.96 3.13 3.49 2.07 2.05 P022V70Senescence-inducible chloroplast stay-green protein198 2.07E–28 1.47 1.04 1.82 1.42 3.39 1.69 1.56 1.03 P014N12Serine carboxypeptidase18424 1.19E−35 1.660.89 1.74 1.77 1.990.530.240.42 P016P26Serine hydroxymethyltransferase2628.25E−260.370.580.360.570.450.660.69 1.04 CONTIG-05Transcription factor bHLH122462 3.25E−50 1.70 1.44 1.44 1.46 1.86 1.79 2.22 2.35 P012L30Transcription factor MYC2522 3.07E−40 1.22 1.04 1.420.650.450.570.690.42 P021U05Trehalose-phosphate synthase540 2.85E−760.400.690.500.420.400.370.34 P021U01Trehalose-phosphate synthase485 1.70E−870.470.630.550.620.360.630.470.68 CONTIG-16Uncharacterized protein LOC100255790600 1.53E−110.250.210.560.630.190.620.660.94 CONTIG-08Uncharacterized protein LOC100255790431 6.77E−120.270.160.480.510.150.600.63 1.00 P018R19Violaxanthin de-epoxidase440 2.31E−410.680.67 1.24 1.730.500.500.37with isopropyl alcohol and cleaned by 75%ethanol.Subsequently,the DNA was dried and resuspended in 15μl sterile water,ran on 1.0%aga-rose gel and examined by Beckman DU460UV Spectroscopy to ensure the quality and quantity,and ﬁnally stored in 384-well microtiter plates at −80°C.The PCR products were precipitated by adding 100μl of anhydrous ethanol and dissolved in EasyArray ™spotting solution (CapitalBio Corp,China),then printed onto amino silaned glass slides using SmartArrayer ™microarrayer (CapitalBio Crop,China).Each clone was printed in triplicate.Microarray slides were fabricated according to Ge et al.(2012).The gene expression pro ﬁles of red-purple pitaya under drought stress and the corresponding control were investigated by microarray analysis.Preparation of ﬂuorescent dye-labeled cDNA and hybridization were referred to Ouyang et al.(2007).2.4.Microarray data and EST sequence analysisAfter washing,the microarray slides were dried brie ﬂy and scanned with a confocal LuxScan ™scanner (CapitalBio Crop,China).Signal in-tensities of individual spots were quanti ﬁed from the resultant images using LuxScan ™3.0software (CapitalBio Crop,China).A spatial and intensity-dependent normalization (LOWESS)method was used for data analysis (Yang et al.,2002),and multiple test corrections were performed using false discovery rate (FDR;Benjamini and Hochberg,1995).Expression levels of expressed sequence tags (ESTs)in the microarray were represented as the intensity ratio based on the t-test.Microarray analysis was conducted to verify the expression changes of this set of clones upon drought treatment at various time points in com-parison with their corresponding controls (stress-free).Genes with the expected FDR of b 0.05and a fold change of N 2were identi ﬁed as DEGs.Sequence information of the differentially expressed clones were gained from the SSH cDNA libraries,and the sequences were screened against DDBJ Vector Screening System (http://vector.ddbj.nig.ac.jp )to remove vector and E.coli genome contaminations.The high quality se-quences were clustered and assembled by ContigExpress (Invitrogen,United States).The assembled sequences that matched annotated pro-teins from genetic model species were assigned gene ontology (GO)terms using Blast2GO (Conesa et al.,2005).Blast sequence matches against HomoloGene (/homologene )to assign Enzyme Commission (EC)numbers,and then iPath (Letunic et al.,2008;Yamada et al.,2011)was used to visualize metabolic pathways.2.5.Quantitative real-time PCR veri ﬁcationQuantitative real-time PCR (qRT-PCR)analyses were performed to validate microarray results,as well as to further examine the gene ex-pression patterns of several important genes with the ABI 7500Real Time System (PE Applied Biosystems,United States).Total RNA was isolated from succulent stems collected at 24h,3d,5d and 7d;ﬁrst-strand cDNA was synthesized using QuantScript RT Kit (Tiangen,China).Target RNA values were normalized using actin RNA as an en-dogenous control.The gene-speci ﬁc primers used herein were designed by Primer Premier 5.0.Then the target gene and actin gene were diluted in the Real Master Mix (Tiangen,China)and 10μl of the reaction mix was added to each well.The PCR conditions were as follows:1cycle at 95°C for 4min;26cycles at 95°C for 30s,62°C for 30s,and72°C for 30s;and an additional cycle at 72°C for 5min.qRT-PCR was carried out in three technical replicates for each sample.A compar-ative threshold cycle (Ct)was used to determine gene expression,and the Ct value of each sample normalized to geometric mean of the Ct values of three internal control genes (ACTB ,UBQ and UBC ).The expres-sion ratios were calculated using the ΔΔCt method corrected for the PCR ef ﬁciency for each gene.3.Results3.1.Construction of cDNA library and differential screening of microarray Forward and reverse subtractions were carried out between stem tissues under the drought stress and the stress-free,and SSH libraries enriched DEGs that were induced by water de ﬁciency were constructed.A total of 2112clones were randomly picked up from either forward (1056)or reverse (1056)SSH,and the inserts were con ﬁrmed by PCR with nested primers.The length of insert fragments in these clones ranged from 200bp to 1000bp (Fig.1),which implied the high subtrac-tion ef ﬁciency,and could be used for the subsequent research.These clones were used to construct a pitaya cDNA microarray.RNA samples from the stem in vitro-treated with 10%PEG for 12h,24h,3d,5d,7d,10d,15d and 20d,as well as the corresponding controls were used for microarray hybridization.Microarray analysis was conducted to verify the expression changes of this set of clones upon drought treat-ment at various time points in comparison with their corresponding controls.As a result,527genes differentially induced by drought stress were obtained.3.2.Sequencing and assembly of sequencesSubsequently,we proceeded with sequencing of the 309clones (153from forward library and 156from reverse library)which demonstrated more than twofold expression changes in at least two experiment points.The cDNA sequencing results were analyzed by Chromas Lite 2.1(Technelysium,Australia)and a total of 188high quality sequences were obtained after cleaning.Then these sequences were analyzed using the ContigExpress (Invitrogen),77sequences were assembled to 27contigs,and totally 138non-redundant sequences including 27contigs and 111singletons were obtained.3.3.Functional annotation of assembled sequencesThe 138non-redundant sequences were subjected to automatic an-notation using Blast2GO for the ﬁrst-pass annotation of putative func-tion of the pitaya non-redundant sequences.Among the 138ESTs,110(79.7%)matched at least one known protein with an E-value threshold of 1×10−10.Based on the microarray data,a total of 76ESTs signi ﬁcantly demon-strated differential expression pro ﬁles (fold change N 1.5)in at least four experiment sets,among which many,i.e.P007G05,CONTIG-02,CONTIG-05and CONTIG-21etc,involved in stress tolerances (Table 1),indicating they were likely to be associated with drought response in pitaya.A total of 73ESTs which showed effective data for all of the sam-pling points were subjected to clustering analysis using Cluster 3.0soft-ware (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm ).Among the analyzed DEGs,the expressions of 41(56.2%of the total)Table 1(continued )Clone IDAnnotationsLengthE-valueExpression pro ﬁles 12h24h 3d 5d 7d 10d 15d 20d CONTIG-02Xyloglucan endotransglycosylase hydrolase 1374 1.40E −95 2.190.69 3.47 1.78 1.79 1.58 1.470.51P019S22Zinc ﬁnger protein C2H25733.74E −171.381.431.511.951.563.693.713.95325Q.-J.Fan et al./Gene 533(2014)322–331were mainly up-regulated,conversely the rest 32(43.8%of the total)were basically down-regulated (Fig.2).Interestingly,the expression patterns of abscisic acid receptor PYL8(ABAR PYL8,P007G05),amino acid permease 8(AAP8,P010J65),as well as three unknown genes (CONTIG-07,CONTIG-12,CONTIG-15)were quite uniform at the eight sampling points (Fig.3),and the correlation coef ﬁcients of expression levels between these DEGs ranged from 0.8531(between CONTIG-07and P010J65)to 0.9995(between CONTIG-07and P007G05),revealing their liaison in molecular function over the drought stress.As part of the Blast2GO annotation process,each contig or singleton was assigned one or more GO terms.A total of 81ESTs were assigned to categorization of biological process,66ESTs were assigned to cellular component,and 82ESTs were assigned to molecular function (Fig.4).For biological process,categorization demonstrated that ESTs involving in stress (29),carbohydrate metabolic process (21)and catabolic pro-cess (18)accounted for a large proportion.In categorization ofmolecularFig.3.Expression patterns of ﬁve genes based on cDNA microarraydata.Fig.2.Cluster analysis of DEGs response to drought stress in pitaya.The same row represents the expression of an individual gene,and the up-regulated or down-regulated genes are indicated in red or green,respectively.Color brightness represents the degree of difference,as shown in color bar.326Q.-J.Fan et al./Gene 533(2014)322–331function,the predominant terms were protein binding (12ESTs)and nucleotide binding (11ESTs).A number of ESTs,e.g.,CONTIG-02(xyloglucan endotransglycosylase hydrolase,E-value: 1.40E −95),CONTIG-27(catalase,E-value:4.63E −124)and P013M56(phospho-ethanolamine N-methyltransferase,E-value:7.24E −86),were impli-cated in stress tolerance,and presumably,their putative functions were closely related to drought stress in pitaya.3.4.Analyses of metabolic pathwaysBased on the annotation result,totally 36genes encoding enzymes were mapped to 47KEGG pathways at P-value b 0.05.To clarify the distribution of the enzymes encoded by these genes in global metabo-lism pathway,iPath analysis was conducted,and it demonstrated that these genes involved in the major categories of metabolicpathways,Fig.4.The distribution of ESTs obtained from SSH library by functional categories shown by (A)biological process,(B)cellular component,and (C)molecular function.Preassembled ESTs were subjected to automated annotation and mapping with Blast2GO ( ).327Q.-J.Fan et al./Gene 533(2014)322–331。

Genes are differentially expressed in the epididymal fat of rats rendered obese by a high-fat diet

Genes are differentially expressed in the epididymalfat of rats rendered obese by a high-fat dietYun Jung Kim,Taesun Park ⁎Department of Food and Nutrition,Brain Korea 21Project,Yonsei University,Seoul 120-749,KoreaReceived 8August 2007;revised 24February 2008;accepted 14March 2008AbstractThe aim of present study was to identify the visceral adipose tissue genes differentially expressed in a well-characterized rat model of high-fat diet (HFD)–induced obesity.Male Sprague-Dawley rats were fed either the HFD (17g lard +3g corn oil/100g)or the normal diet (5g corn oil/100g)for 9weeks.The HFD rats weighed 55%more and accumulated 85%to 133%greater visceral fats than did the normal-diet rats (P b .05).Animals given the HFD for 9weeks acquired dyslipidemia,fatty liver,insulin resistance,and hyperleptinemia along with the overexpression of several obesity-related genes,such as leptin,tumor necrosis factor α,resistin,peroxisome proliferator –activated receptor γ2,CCAAT/enhancer-binding protein α,and sterol regulatory element –binding protein-1c,in the epididymal adipose tissue.The differential gene expression profile obtained from the cDNA microarray analysis followed by the real-time polymerase chain reaction confirmation led to a recruitment of several uncharacterized adipose tissue genes responding to the HFD.We report herein,for the first time,that a series of genes which might be implicated in the insulin-stimulated glucose transporter 4translocation,such as protein phosphatase 2(formerly 2A),cell division cycle 42–interacting protein 4,syntaxin 6,linker of T-cell receptor pathways 10,as well as the genes which might be involved in cancer development,such as heat shock 10-kd protein 1,and ras-related C3botulinum toxin substrate 1,were differentially expressed in the epididymal adipose tissue of rats rendered obese by an HFD.©2008Elsevier Inc.All rights reserved.Keywords:cDNA microarray;Epididymal adipose tissue;High-fat diet;Gene expression;Insulin resistance;RatsAbbreviations:C/EBP α,CCAAT/enhancer-binding protein α;CIP4,cell division interacting protein 4;EFA,essential fatty acids;GLUT4,glucose transporter 4;HDL,high-density lipoprotein;HFD,High-fat diet;HSP10,heat shock 10-kd protein 1;Lnk,linker of T-cell receptor pathways 10;NADPH,nicotinamide adenine dinucleotide phosphate;ND,normal diet;PBS,phosphate buffer saline;PCR,polymerase chain reaction;PP2A,protein phosphatase 2(formerly 2A);PPAR γ2,resistin,peroxisome proliferator activated receptor γ2;Rac1,ras-related C3botulinum toxin substrate 1;SD,Sprague-Dawley;SREBP1c,sterol-regulatory-element-binding protein-1c;Stx6,syntaxin 6c;TNF α,tumor necrosis factor α.1.IntroductionObesity is a public health problem of global significance because of its high prevalence and its association with a wide range of chronic conditions,such as diabetes,hypertension,cardiovascular disease,and certain cancers [1].Significant advances have been made in understanding the mechanism of energy homeostasis and obesity in laboratory animals,mimicking the human condition [2].Animal models of obesity,which are based either on environmental factors such as diets or spontaneously/genetically engineered mutants,differ widely in food intake,metabolism,the presence of diabetes or insulin resistance,and the extent of the obesityAvailable online at Nutrition Research 28(2008)414–422Corresponding author.Tel.:+82221233123;fax:+8223653118.E-mail address:tspark@yonsei.ac.kr (T.Park).0271-5317/$–see front matter ©2008Elsevier Inc.All rights reserved.doi:10.1016/j.nutres.2008.03.015presented.Although there are rare examples of single gene mutations that are responsible for obesity in humans,it is now clear that most cases of human obesity are polygenic and represent the interaction between multiple genes and the environment,of which diet is a major component[3,4].Rats with high-fat diet(HFD)–induced obesity have been proven to be useful experimental models for human obesity [5].Dietary fat is calorically dense,extremely palatable,and causes less satiety than do carbohydrate and protein[6]. Chronic exposure to an HFD can affect the generation and reception of meal-related signals that control energy metabolism,the brain's generation and reception of adiposity-indicating signals that regulate food intake and metabolism,thereby causing obesity[7].The extent to which obesity is induced by diet varies depending on the length of the feeding period,the types and levels of dietary fat,and/ or the presence of other modifications in the dietary ingredients[7-10].The role of the adipose tissue in the etiology of obesity [11]and its pathophysiologic consequences[12]is increas-ingly recognized.The importance of adipose tissue in the regulation of energy homeostasis is exemplified by the discovery of its secreted hormone,leptin,whose deficiency causes obesity in ob/ob mice[13]and in a few people[14-16]. There may be other genes expressed in the adipose tissue that contribute to the etiology or pathophysiology of obesity. From a DNA microarray analysis of genes in epididymal fat pads from the cafeteria diet–induced obese rats,Lopez et al [17]observed that the expression levels of some important genes implicated in lipid metabolism were upregulated, whereas those related to the redox and stress protein were downregulated in obese animals compared with the control.The aim of present study was to identify the visceral adipose tissue genes differentially expressed in a well-characterized rat model of HFD-induced obesity.Identifying the regulatory processes that mediate the HFD-induced obesity is of fundamental importance and requires a well-established obese animal model.It was confirmed that the rats fed HFD in this study shared important characteristics with obese humans in terms of phenotype,and that multiple genes of visceral adipose tissues interacted with the HFD during the development of obesity.To seek the answers to many questions about molecular abnormalities of diet-induced obesity,the cDNA microarray technology was used.2.Methods and materials2.1.Animals and experimental dietsTwenty5-week-old male Sprague-Dawley(SD)rats (Orient Co,Gyeonggi-do,Korea)were individually housed in stainless steel rat cages,placed in a room where the temperature was kept at21±2.0°C,the relative humidity at 50%±5%,and12-hour light-dark cycle.All the rats were given a commercial diet(Purina Rodent Chow no.5001; St.Louis)and tap water ad libitum for1week before their division into2weight-matched groups:the normal-diet(ND) group and the HFD group.The ND was formulated based on the AIN-76rodent diet composition which is recommended by the American Institute of Nutrition as a standard rodent diet,and the HFD contained200g of fat per kilogram(170g of lard+30g of corn oil to provide essential fatty acids [EFA])and1%cholesterol by weight(Table1).The HFD was formulated to provide40%of the total energy from fat, by replacing carbohydrate energy with lard and corn oil,and had the same amount of vitamins and minerals per kilojoule as the ND.The compositions of the experimental diets are shown in Table1.All the diets were given in the form of a pellet for9weeks.The food intake of rats was recorded daily, and their body weights were measured every3days,from 10:00AM to11:00AM.At the end of the9-week feeding period,the rats were anesthetized with diethyl ether after an overnight fasting. Blood was drawn from the abdominal aorta into a vacuum tube,and the serum was obtained by centrifuging the blood at1000×g for15minutes at4°C.The livers and4different depots of visceral fat tissues(epididymal,perirenal,retro-peritoneal,and mesenteric fat pads)were removed,were rinsed with0.1mol/L phosphate buffer(pH7.4),and were weighed.The serum,liver,and fat pads were stored at−81°C until they were analyzed.This study adhered to the Guide for the Care and Use of Laboratory Animals developed by the Institute of Laboratory Animal Resources of the National Research Council and approved by the Institutional Animal Care and Use Committee of Yonsei University in Seoul, South Korea.2.2.Fat-cell size and numberThe slices of epididymal and mesenteric fat tissues were fixed for24hours at4°C,in0.1mol/L phosphate buffer saline (PBS)containing1%glutaraldehyde and4%paraformalde-hyde,and were washed3times in0.1mol/L PBS.The tissues Table1Composition of the experimental diets(g/kg)fed to ratsIngredients ND HFD Casein200200 DL-Methionine33 Corn starch150111 Sucrose500370 Cellulose5050 Corn oil5030 Lard–170 Mineral mixture a3542 Vitamin mixture b1012 Choline bitartrate22 Cholesterol–10 tert-Butylhydroquinone c0.010.04 Fat,%kJ11.540.0 Total energy,kJ/kg diet1643919315a Mineral mixture for AIN-76A rodent diet.b Vitamin mixture for AIN-76A rodent diet.c Antioxidant added at0.01g/50g lipid.415Y.J.Kim,T.Park/Nutrition Research28(2008)414–422were postfixed on ice,with the use of 1%osmium tetraoxide,for 10minutes,and were washed 3times in 0.01mol/L PBS.The tissues were then embedded in Epon solution after dehydration in ethanol.Semi-thin sections (1μm)were stained with 1%toluidine blue,were viewed with the use of an optical microscope (Axioplan 2;Zeiss,Oberkochen,Germany),were photographed,and were printed at a final magnification of ×200.The total fat-cell number of the epididymal or mesenteric fat pads was calculated from the ratio of the fat-pad volume to adipose-cell volume,using the method developed by Lemonnier [18].Briefly described,the fat-cell number was estimated from the ratio of the fat-pad weight to the mean adipose-cell volume ×0.92(0.92was the measured density of the pads).The mean fat-cell volume (V )was calculated from the mean fat-cell surface area (S ),determined by using the formula V ¼4S 3=2=3ﬃﬃﬃp p ðÞ.In this method,it is assumed that cells are spherical,and that their distribution is symmetrical and similar in all groups and tissues.2.3.Biochemical analysesSerum concentrations of total cholesterol,high-density lipoprotein (HDL)cholesterol,triacylglycerols (Young-dong Diagnostics,Seoul,Korea),and free fatty acids (Eiken Chemical,Tokyo,Japan)were determined enzymatically,using commercial kits.The serum very low density lipoprotein +low-density lipoprotein cholesterol concentra-tion was calculated by subtracting the HDL cholesterol from the total cholesterol concentration.The serum aspartate transaminase and alanine transaminase activities were measured using an automatic analyzer (Express Plus;Chiron Diagnostics,East Malpole,Mass)with reagents (Bayer,Wuppertal,Germany).Hepatic lipids were extracted using the method developed by Folch et al [19],and the dried lipid residues were dissolved in 1mL ethanol.The concentrations of cholesterol,triacylglycerols,and free fatty acids in hepatic lipid extracts were measured by using the same enzymatic kits that were used for the serum lipid analyses.The cytosolic phase of liver tissues was isolated by using the method developed by Murata et al [20].The hepatic fatty acid synthase activity was determined spectrophotometri-cally at 340nm in the cytosolic fraction as described by Kelley et al [21],using malonyl-CoA-and acetyl-CoA –dependent rate of NADPH,nicotinamide adenine dinucleo-tide phosphate (NADPH)oxidation (the formation of NADPH).Glucose-6-phosphate dehydrogenase activity in the cytosolic fraction was determined spectrophotometrically by measuring the NADPH formation from glucose-6-phosphate,as reported previously [22].The hepatic malic enzyme activity was determined by measuring the NADPH formation from L -malate at 340nm,as described by Hsu and Lardy [23].The activities of enzymes were expressed as nanomoles per minute per milligram of protein,and the protein concentrations of the cytosolic fractions were determined using the modified Bradford assay (Bio-Rad,Hercules,Calif)with bovine serum albumin as the standard.The serum insulin,C-peptide,and leptin levels were measured by radioimmunoassay using commercially avail-able kits (RIA rat insulin,rat C-peptide,and rat leptin kits;Linco Research,Charles,MO).The assay used 125I-labeled rat insulin and guinea pig antirat insulin serum to determine the total (free of,and bound to,serum proteins)insulin level in the serum.The serum insulin concentration was counted in a gamma counter (Packard COBRA 5010Quantum;Packard Co.,Downers Grove,IL)for 1minute and was calculated in nanograms per milliliter.The serum C-peptide and leptin concentrations were determined through the same procedure that was used for insulin measurement.The lower limits of sensitivity for the insulin,C-peptide,and leptin assays were 15pmol/L,25pmol/L,and 0.5μg/L,respectively.The serum glucose concentration was determined by using an automatic analyzer (Express Plus,Chiron Diagnostics)with reagents (Bayer).The insulin resistance index was calculated as the product of the serum insulin and glucose concentration (10−3pmol insulin ×mmol glucose ×L −2)for each rat [24].2.4.RNA isolation and real-time polymerase chain reaction Total RNA was isolated from the epididymal fat tissues of each rat using Trizol (Invitrogen,Carlsbad,Calif)and was reverse transcribed using the Superscript II kit (Invitrogen),according to the manufacturer's recommendations.The primers for real-time polymerase chain reaction (PCR)analysis were designed using the Whitehead Institute/MT Center for Genome Research's Primer3interface,which is available online.The sequences of the designed primers were as follows:sterol regulatory element-binding protein-1c (SREBP1c):sense-5′-GGAGCCATGGATTGCACATT-3′and antisense-5′-AGGAAGGCTTCCAGAGAGGA-3′;per-oxisome proliferator –activated receptor γ2(PPAR γ2):sense-5′-CTTGGCCATATTTATAGCTGTCATTATT-3′and antisense-5′-TGTCCTCGATGGGCTTCAC-3′;CCAAT/enhancer-binding protein α(C/EBP α):sense-5′-GGCGGGAACGCAACAA-3′and antisense-5′-TCCACG-TTGCGCTGTTTG-3′;leptin:sense-5′-CACAGAGG-TGGTGGCTCTGA-3′and antisense-5′-CCCGGTGGT-CTTGGAAACTT-3′;tumor necrosis factor α(TNF α):sense-5′-AGATCATCTTCTCAAAACTC-3′and antisense-5′-TAAGTACTTGGGCAGGTTGA-3′;resistin:sense-5′-ACTTCAGCTCCCTACTGCCA-3′and antisense-5′-GCTCAGTTCTCATCAATCAACCGTCC-3′;uncoupling protein 2(UCP2):sense-5′-ACAAGACCATTGCACGA-GAG-3′and antisense-5′-CATGGTCAGGGCACAGTGGC-3′;heat shock 10kd protein 1(HSP10):sense-5′-AACTTT-CACACTGACAGGCT-3′and antisense-5′-CTTCCGCTA-TTTGACAGAGT-3′;protein phosphatase 2(formerly 2A),regulatory subunit B,alpha isoform (PP2A):sense-5′-CGTAACATAGTCCCCCATTA-3′and antisense-5′-TCTTACTGTGGCTTGTAGCA-3′;ras-related C3botulinum toxin substrate 1(Rac1):sense-5′-GGCTGATAAGTTA-CAGGTGC-3′and antisense-5′-GCTGTTGAGAGACTTT-GTCC-3′;cell division cycle 42(Cdc42)-interacting protein 4(CIP4):sense-5′-GCAGAAGTATGAGGCTTGGT-3′and416Y.J.Kim,T.Park /Nutrition Research 28(2008)414–422antisense-5′-TTATCCTGGGA TCCACTGTT-3′;Syntaxin6 (Stx6):sense-5′-GAAGTAGCTGTCAGGTCCAG-3′and antisense-5′-CTAAGGAATGCACAGAAAG.G-3′;linker of T-cell receptor pathways10(Lnk):sense-5′-CAGAGACGTGAGCAGTAACA-3′and antisense-5′-GAGTGAATCCCAGCACTAAG-3′;andβ-actin:sense-5′-ACCTTCAACACCCCAGCCATGTACG-3′and anti-sense-5′-CTGATCCACATCTGCTGGAAGGTGG-3′.Real-time PCR reactions were then carried out in a20-μL reaction mixture(2μL cDNA;16μL SYBR Green PCR Master Mix,which includes2μL1×LightCycler;2.4μL1.5 mM MgCl2and11.6μL H2O;and a1-μL0.5μmol/L specific gene primer pair)in a LightCycler(Roche Diagnostics, Indianapolis,Ind.).The PCR program was initiated at95°C for10minutes before40thermal cycles,each for10seconds at95°C,5seconds at55°C,and30seconds at70°C,were conducted.The data that were obtained were analyzed using the comparative-cycle threshold method and were normalized by theβ-actin expression value.Melting curves were generated for each PCR reaction to ensure the purity of the amplification product.2.5.cDNA microarray analysisThe cDNA microarray analysis was performed with the use of the GenePlorer TwinChip Rat-5K(Digital Genomics Co,Seoul,Korea)containing a total of5062genes,including 2255known genes,2775unknown genes,and32quality control probe sets according to the manufacturer's instruction (http://www.digital-genomics.co.kr).Purified RNA samples (30μg)were used for the hybridization of each microarray slide.To reduce individual variability in gene expression,the RNA probes were made by pooling an identical amount of epididymal adipose tissue RNA samples obtained from individual rats that were fed the ND(n=10)and the HFD(n=10),respectively.The cDNA microarray was hybridized witha mixture of fluorescence-labeled cDNA from the ND and HFD-fed rats at42°C for18hours and was then washed.The fluorescence-labeled cDNAwas prepared through the reverse transcription of total RNA in the presence of aminoallyl-dUTP,followed by the coupling of Cy3dye(for the RNA of the ND-fed rats)or Cy5dye(for the RNA of the HFD-fed rats)(Amersham Pharmacia Biotech,Seoul,Korea).cDNA chips were scanned using ScanArray Lite(Perkin-Elmer Life Sciences,Billerica,Mass),and the scanned images were analyzed with the use of the GenePix3.0 software(Axon Instruments,Union City,Calif)for gene expression ratios(the HFD rats vs the ND rats).The robust scatter plot and smoother LOWESS function were used in conducting the intensity-dependent normalization of gene expression.When the difference in relative gene expression between the2groups was above1.5fold,this was defined asa change of gene expression.2.6.Statistical analysesMicroarray data were expressed as the fold change that represents the median gene expression ratio from each of 4independently repeated microarray experiments,comparing the HFD-fed rats with the ND-fed rats.The RNA samples used for microarray analysis were pooled from10rats per group. Significance analysis of microarray was performed for the selection of genes with significant gene expression changes [25].Statistical significance of the differential expression of any gene was assessed by computing a q value(the lowest false discovery rate at which the gene is called significant)for each gene[26].The data of weight gain,visceral fat–pad weight,food intake,and serum and hepatic biochemical analyses were expressed as means±SEM of10rats.The real-time PCR data were presented as means±SEM of the triplicate analyses of the RNA samples pooled from10rats per group. Two-tailed Student t test(Microsoft Excel,Microsoft,Red-mond,Wash)was used to evaluate the differences between the values for the HFD and its control group(ND),and a P value ofb.05,b.01,or b.001was considered significant.3.Results3.1.Weight gain,visceral fat–pad weight,and food intakeRats fed the HFD exhibited a significantly heavier body weight than ND rats as early as the sixth day of the experiment,whereas the initial body weights were identical between the2groups(Table2and Fig.1).The cumulative body weight gain of rats at9weeks in the HFD group was 199%of the value for the ND rats(P b.001)(Table2).The daily food intake throughout the study was24%greater in the HFD rats than in the ND rats(P b.001)(Table2), although the HFD is calorically denser than the ND(19315 vs16439kJ/kg).Therefore,the cumulative energy intake for 9weeks was50%greater in the rats given the HFD than in the ND rats.The food efficiency ratio of the rats in the HFD group was significantly higher than that of the ND rats.The relative weights of the visceral adipose tissue depots were greater in the HFD rats than in the ND rats.The retroperitoneal,epididymal,mesenteric,and perirenal fat pads were67%(P b.01),85%(P b.01),133%(P b.001), and93%(P b.001)greater,respectively in the rats fed the HFD than those in the ND rats.The mesenteric fat tissues were the heaviest in rats from the HFD group.The adipocytes from the epididymal and the mesenteric fat pads were81%and79%larger,respectively,in the rats fed the HFD than in the ND rats.Feeding the HFD to rats for 9weeks resulted in a significant increase in the fat-cell number of the mesenteric fat pad compared to that for the ND rats(60%increase,P b.05)(Table2).3.2.Serum and hepatic biochemistriesThe serum total and low-density lipoprotein+very low density lipoprotein cholesterol concentrations were105% and268%higher,respectively,in the rats administered the HFD than in the ND rats(P b.001)(Table3).The serum HDL cholesterol concentration of the2groups did not differ, whereas the ratio of HDL cholesterol/total cholesterol of the417Y.J.Kim,T.Park/Nutrition Research28(2008)414–422HFD rats was 51%of the value for the ND rats (P b .01).Feeding the HFD to rats for 9weeks resulted in a 22%increase in the serum glucose concentration (P b .01).The serum insulin and C-peptide levels were 214%(P b .05)and 164%(P b .01)higher,respectively,and the insulin resistance index was 262%higher (P b .05)in the rats administered the HFD than in the ND rats.The serum leptinconcentration was 261%higher in the HFD rats than in the normal animals (P b .001)(Table 3).Feeding rats the HFD led to a significant increase in the relative weight of the liver compared with that in the ND rats (P b .01).The hepatic levels of the total lipids,triacylglycerols,cholesterol,and free fatty acids were significantly higher in the HFD rats than in the ND rats.The HFD rats exhibited significantly elevated activities of serum aspartate transami-nase and alanine transaminase compared to those for the ND rats.The hepatic activities of glucose-6-phosphate dehydro-genase (36%lower,P b .05)and malic enzyme (39%lower,P b .05)were significantly downregulated in rats given the HFD than in the ND rats,whereas the hepatic fatty acid synthase activity was not altered by the experimental diet (Table 3).3.3.High-fat diet –induced changes in the gene expression profileThe HFD-induced changes in the expression of multiple obesity-related genes,such as SREBP-1c,PPAR γ,C/EBP α,Fig.1.Changes in the body weight of rats fed the ND or the HFD for 9weeks.Values are means ±SEM for 10rats.*Significantly different from the value for rats fed the ND by Student t test at P b .05.Table 3Serum and hepatic biochemical measurements in rats fed experimental dietsNDHFDSerumTotal cholesterol (mmol/L)2.05±0.15 4.22±0.55⁎LDL +VLDL Cholesterol a (mmol/L)0.50±0.13 2.95±0.50⁎⁎⁎HDL Cholesterol (mmol/L) 1.25±0.05 1.26±0.09HTR b (%)62.3±3.3531.8±2.63⁎⁎Triglyceride (mmol/L)0.43±0.050.39±0.03Free fatty acid (mmol/L)0.64±0.080.58±0.04Aspartate transaminase (U/L)70.7±4.15183±29.0⁎Alanine transaminase (U/L)22.9±0.8651.2±11.4⁎Glucose (mmol/L)8.83±0.2910.8±0.61⁎⁎Insulin (pmol/L)162±30.6508±132⁎C-Peptide (pmol/L)706±59.41,862±262⁎⁎Leptin (ng/mL)3.04±0.3111.0±1.57⁎⁎⁎Insulin resistance index c1.47±0.355.32±1.51⁎LiverWeight (g/100g body weight) 2.67±0.13 5.01±0.18⁎⁎⁎Total lipid (mg/g liver)369±5.84581±13.3⁎⁎⁎Cholesterol (μmol/g liver)16.2±1.3241.1±0.75⁎⁎⁎Triglyceride (μmol/g liver) 3.67±0.277.07±0.19⁎⁎⁎Free fatty acid (μmol/g liver)3.46±0.32 6.66±0.81⁎FAS (nmol/min·per milligram of protein)11.6±0.7510.2±0.93G6PDH (nmol/min·per milligram of protein)40.0±4.4525.7±4.67⁎Malic enzyme (nmol/min·per milligram of protein)25.0±2.3015.2±3.01⁎Values are mean ±SEM (n =10).LDL indicates low-density lipoprotein;VLDL,very low density lipoprotein;FAS,fatty acid synthase;G6PDH,glucose-6-phosphate dehydrogenase.aLDL +VLDL cholesterol =total cholesterol −HDL cholesterol.bHTR (%)=[HDL cholesterol/total cholesterol]×100.cInsulin resistance index =10−3pmol insulin ×mmol glucose ×L −2.⁎Significantly different from the value for rats fed ND by Student t test at P b .05.⁎⁎Significantly different from the value for rats fed ND by Student t test at P b .01.⁎⁎⁎Significantly different from the value for rats fed ND by Student t test at P b .001.Table 2Body weight gain,food intake,and visceral fat –pad weights of rats fed experimental dietsNDHFDInitial body weight (g)187±2.30187±3.95Final body weight (g)420±5.75650±20.3Body weight gain (g/9wk)233±5.38463±23.3⁎⁎⁎Food intake (g/d)21.7±0.4227.6±0.54⁎⁎⁎Food efficiency ratioa0.183±0.0040.266±0.009⁎⁎⁎Cumulative energy (MJ/9wk)22.5±0.4433.6±0.67⁎⁎⁎Energy efficiency (g gain/MJ consumed)10.4±0.4313.7±0.51⁎⁎Visceral fat –pad weightEpididymal (g/100g body weight) 1.54±0.15 2.86±0.27⁎⁎Perirenal (g/100g body weight)0.44±0.050.85±0.05⁎⁎⁎Retroperitoneal (g/100g body weight) 2.00±0.18 3.33±0.22⁎⁎Mesenteric (g/100g body weight)0.98±0.07 2.29±0.10⁎⁎⁎Total visceral (g/100g body weight) 4.96±0.409.33±0.58⁎⁎⁎Visceral fat –cell sizeEpididymal (μm 2)3.97±0.087.17±0.43⁎⁎⁎Mesenteric (μm 2) 2.19±0.15 3.92±0.26⁎⁎Visceral fat-cell number Epididymal (×109) 1.11±0.07 1.41±0.19Mesenteric (×109) 1.72±0.15 2.76±0.47⁎Values are mean ±SEM (n =10).aFood efficiency ratio =body weight gain/food intake.⁎Significantly different from the value for rats fed ND by Student t test at P b .05.⁎⁎Significantly different from the value for rats fed ND by Student t test at P b .01.⁎⁎⁎Significantly different from the value for rats fed ND by Student t test at P b .001.418Y.J.Kim,T.Park /Nutrition Research 28(2008)414–422leptin,TNF α,resistin,and UCP2,were evaluated in the epididymal adipose tissue by using real-time PCR analyses.Expression of SREBP-1c,PPAR γ,and C/EBP αgenes,the key adipose transcription factors that play important roles in adipogenesis and insulin sensitivity,was upregulated 5.1-,2.6-,and 2.5-fold,respectively,in the epididymal adipose tissue of rats by feeding the HFD (Fig.2).Similarly,the expression of leptin,TNF α,and resistin genes in the epidydimal adipose tissue of the rats given the HFD was upregulated 7.0-,3.4-,and 1.5-fold,respectively,compared to that for the ND rats.The UCP2gene expression in the epididymal adipose tissue of rats was not different between the groups (Fig.2).cDNA microarray analyses were conducted to determine any changes in the gene expression profile of the edipidymal adipose tissue of rats rendered obese by the HFD.Among the 5030genes on the cDNA microarray chip used in the current study,23known genes were up-or downregulated more than 1.5-fold by feeding the HFD (Tables 4and 5).The epididymal adipose tissue genes upregulated by the HFD included the HSP10(2.9-fold),PP2A (1.96-fold),ubiquitin-conjugating enzyme E2N (1.8-fold),neuronatin (1.78-fold),cytoplasmic CAR retention protein (1.57-fold),and branched chain aminotransferase 2,mitochondrial (1.50-fold)(Table 4).Feeding the HFD to rats downregulated the 17known genes in the epididymal adipose tissue,in the range of fold change from 0.44to 0.66:the Rac1;milk fat globule-EGF factor 8protein;calponin 1;paired related homeobox 2;activity-dependent neuroprotective protein;Stx;transmembrane 4superfamily member 3;carbonyl reductase 1;protein kinase C and casein kinase substrate in neurons 2;Lnk;transcription factor 1;CD24antigen;CIP4;crystalline,βB3,nuclear receptor subfamily 1group H,member 2;βprime COP;and 3-hydroxy-3-methylglutaryl-CoA synthase 1(Table 5).The cDNA microarray results observed in the current study were verified by conducting real-time PCR analyses,using the identical RNA samples prepared from the epididymal adipose tissue,for selected genes,such as PP2A,Rac1,Lnk,CIP4,HSP10,and Stx 6.The results of real-time PCR analyses for these selected genes were consistent with the microarray data obtained in the present study,although the fold changes in the expression level differed somewhat between the 2analytical methods (Fig.3).4.DiscussionNumerous publications have described various phenoty-pic consequences between rodent models with HFD-induced obesity and their normal weight counterparts.Substantial evidence suggests that not only the level but also the type of fat can influence body weight,body composition,and plasma comorbidity factors [27].Based on a recent report by Kim et al [8]and the investigators unpublished observations,feeding SD rats an HFD containing 20%beef tallow as a single source of fat for 8to 9weeks induced only a slight increase (5%-6%)in the final body weight compared to the control rats that had been fed a regular AIN-76diet.The 20%fat diet (17%lard +3%corn oil)that was used in the current study,however,induced a much greater increase in thefinalFig.2.Gene expression level determined by real-time PCR analysis of the epididymal fat from rats fed experimental diets.Results were normalized to β-actin mRNA expression.The mRNA levels of rats fed the HFD were expressed as the fold changes of normal rats.Values are means ±SEM of triplicate analyses of RNA samples pooled from 10rats.Table 4Genes upregulated by feeding the HFD in the epididymal fat of rats GenBank Accession No.Gene descriptionGene function Fold change (HFD/ND)q value (FDR,%)AA874816Heat shock 10-kd protein 1Protein folding 2.918.21AA819691Protein phosphatase 2(formerly 2A),regulatory subunit B (PR 52),αisoform Signal transduction 1.96 5.10AA925340Ubiquitin-conjugating enzyme E2N (homologous to yeast UBC13)Ubiquitin-dependent protein catabolism 1.80 1.47AA858569NeuronatinBrain development 1.78 6.62AA924691Cytoplasmic CAR retention proteinProtein folding1.570.91AA819007Branched chain aminotransferase 2,mitochondrialAmino acid metabolism1.503.25The fold change represents the median gene expression ratio from each of 4independently repeated microarray experiments,comparing the HFD-fed rats with the ND-fed rats.The RNA samples used for microarray analysis were pooled from 10rats per group.The significance analysis of microarrays software program was used to evaluate the significance of differences in gene expression.The criterion for the inclusion of a gene in this table was that the observed fold change was N 1.5and the q value b 10%.The q value indicates the percentage of genes identified by chance,the false discovery rate (FDR).For example,if 100genes with 10%FDR were selected,10genes among 100genes are likely to be false positive.419Y.J.Kim,T.Park /Nutrition Research 28(2008)414–422。

Analysis of microarray gene expression data

Analysis of microarray gene expression dataWolfgang Huberw.huber@dkfz.deGerman Cancer Research CenterDivision of Molecular Genome Analysis69120HeidelbergAnja von Heydebreckanja.heydebreck@molgen.mpg.deMax-Planck-Institute for Molecular Genetics14195BerlinMartin Vingronmartin.vingron@molgen.mpg.deMax-Planck-Institute for Molecular Genetics14195BerlinApril2,2003AbstractThis article reviews the methods utilized in processing and analysis of gene expression data generated using DNA microarrays.This type of experiment allows to determine relativelevels of mRNA abundance in a set of tissues or cell populations for thousands of genessimultaneously.Naturally,such an experiment requires computational and statistical analysistechniques.At the outset of the processing pipeline,the computational procedures are largelydetermined by the technology and experimental setup that are used.Subsequently,as morereliable intensity values for genes emerge,pattern discovery methods come into play.Themost striking peculiarity of this kind of data is that one usually obtains measurements forthousands of genes for only a much smaller number of conditions.This is at the root ofseveral of the statistical questions discussed here.1IntroductionIn the context of the human genome project,new technologies emerged that facilitate the par-allel execution of experiments on a large number of genes simultaneously.The so-called DNA1microarrays,or DNA chips,constitute a prominent example.This technology aims at the mea-surement of mRNA levels in particular cells or tissues for many genes at once.To this end,single strands of complementary DNA for the genes of interest-which can be many thousands-are im-mobilized on spots arranged in a grid(”array”)on a support which will typically be a glass slide, a quartz wafer,or a nylon membrane.From a sample of interest,e.g.a tumor biopsy,the mRNA is extracted,labeled and hybridized to the array.Measuring the quantity of label on each spot then yields an intensity value that should be correlated to the abundance of the corresponding RNA transcript in the sample.Two schemes of labeling are in common use today.One variant labels a single sample,either ra-dioactively orﬂuorescently.Radioactive labeling is used,e.g.,in conjunction with hybridization on nylon membranes[1].The company Affymetrix synthesizes sets of short oligomers on a glass wafer and uses a singleﬂuorescent label([2],see also ).Alternatively,two samples are labeled with a green and a redﬂuorescent dye,respectively.The mixture of the two mRNA preparations is then hybridized simultaneously to a common array on a glass slide.This technology is usually refered to as the Stanford technology[3].Quantiﬁcation utilizes a laser scanner that determines the intensities of each of the two labels over the entire array.Recently, companies like Agilent have immobilized long oligomers of60to70basepairs length and used two-color labeling.The parallelism in this kind of experiment lies in the hybridization of mRNA extracted from a single sample to many genes simultaneously.The measured abundances,though,are not obtained on an absolute scale.This is because they depend on many hard to control factors such as the efﬁciencies of the various chemical reactions involved in the sample preparation,as well as on the amount of immobilized DNA available for hybridization.The class of transcripts that is probed by a spot may differ in different applications.Most com-monly,each spot is meant to probe a particular gene.The representative sequence of DNA on the spot may be either a carefully selected fragment of cDNA,a more arbitrary PCR product am-pliﬁed from a clone matching the gene,or one of a set of oligonucleotides speciﬁc for the gene. Another level of sophistication is reached when a spot represents,e.g.,a particular transcript of a gene.In this case,or for the distinction of mRNA abundances of genes from closely related gene families,careful design and/or selection of the immobilized DNA is required.Likewise, the selection of samples to study and to compare to each other using DNA microarrays requires careful planning as will become clear upon consideration of the statistical questions arising from this technology[4,5,6].There are many different ways for the outline of a microarray experiment.In many cases,a development in time is studied leading to a series of hybridizations following each other.Alter-natively,different conditions like healthy/diseased or different disease types may be studied.We generally refer to a time point or a state as a condition and typically for each condition several replicate hybridizations are performed.The replicates should provide the information necessary to judge the signiﬁcance of the conclusions one wishes to draw from the comparison of the dif-ferent conditions.When going deeper into the subject it soon becomes clear that this simple outline constitutes a rather challenging program.This article is organized along the various steps of analysis of a microarray experiment.Statistical problems ariseﬁrstly as a consequence of the many technical peculiarities and their solution is a2prerequisite to any meaningful subsequent interpretation of the experiment.Section2describes some of the issues related to quality control.Visualization methods are introduced because they may greatly help both in detecting and removing obviously failed measurements,as well as in ﬁnding more subtle systematic biases associated with variations in experimental conditions. Microarray measurements are subject to multiple sources of experimental variation,the math-ematical treatment of which are discussed in Section3.Some of the variations are systematic, and may be explicitly corrected for,others are random,and may be accounted for through an error model.The correction for systematic effects is refered to as calibration or normalization. We will discuss two error models:One involving a constant coefﬁcient of variation,i.e.a purely multiplicative noise term,and one allowing for a more general variance-to-mean dependence, with a noise term that has both multiplicative and additive components.From these models we derive measures of relative abundance of mRNA.The goal of many microarray experiments is to identify genes that are differentially transcribed with respect to different biological conditions of cell cultures or tissue samples.Section4fo-cuses on these issues,paying particular attention to the notoriously low numbers of repeated hybridizations per condition in relation to the high numbers of genes about which one wants to make conclusions.Section5proceeds to highlight some of the issues in pattern discovery in microarray data.Here,again,classical methods of data analysis need to be carefully evaluated with respect to their applicability to the particular type of data at hand.A short summary will be given of the methods that have so far been successfully applied.Emphasis is given to exploratory approaches that allow the subsequent formulation of hypothesis that can be tested either through further analysis or further experiments.2Data visualization and quality controlA microarray experiment consists of the following components:a set of probes,an array on which these probes are immobilised at speciﬁed locations,a sample containing a complex mix-ture of labeled biomolecules that can bind to the probes,and a detector that is able to measure the spatially resolved distribution of label after it has bound to the array[7].The probes are chosen such that they bind to speciﬁc sample molecules;for DNA arrays,this is ensured by the high sequence-speciﬁcity of the hybridization reaction between complementary DNA strands. The array is typically a glass slide or a nylon membrane.The sample molecules may be labeled through the incorporation of radioactive markers,such as33P,or ofﬂuorescent dyes,such as phy-coerythrin,Cy3,or Cy5.After exposure of the array to the sample,the abundance of individual species of sample molecules can be quantiﬁed through the signal intensity at the matching probe sites.To facilitate direct comparison,the spotted array technology developed in Stanford[3] involves the simultaneous hybridization of two samples labeled with differentﬂuorescent dyes, and detection at the two corresponding wavelengths.Fig.1shows an example.3Figure1:The detected intensity distributions from a cDNA microarray for a region comprising around80probes.The total number of probes on an array may range from a few dozens to tens of thousands.Left panel:grey-scale representation of the detected labelﬂuorescence at635nm (red),corresponding to mRNA sample A.Right panel:labelﬂuorescence at532nm(green), corresponding to mRNA sample B.Spots that light up in only one of the two images correspond to genes that are only transcribed in one of the two samples.Middle panel:false-color overlay image from the two intensity distributions.The spots are red,green,or yellow,depending on whether the gene is transcribed only in sample A,sample B,or both.2.1Image quantiﬁcation.The intensity images are scanned by the detector at a high spatial resolution,such that each probe spot is represented by many pixels.In order to obtain a single overall intensity value for each probe,the corresponding pixels need to be identiﬁed(segmentation),and the intensities need to be summarized(quantiﬁcation).In addition to the overall probe intensity,further auxiliary quantities may be calculated,such as an estimate of apparent unspeciﬁc“local background”intensity,or a spot quality measure.A variety of segmentation and quantiﬁcation methods is implemented in available software packages.They differ in their robustness against irregularities and in the amount of human interaction that they require.Different types of irregularities may occur in different types of microarray technology,and a segmentation or quantiﬁcation algorithm that is good for one platform is not necessarily suitable for another.For instance,the variation of spot shapes and positions that the segmentation has to deal with depends on the properties of the support(e.g.glass or nylon),on the probe delivery mechanism(e.g.quill-pen type,pin and ring systems,ink jetting),and on the detection method(optical or radioactive).Furthermore,larger variations in the spot positioning from array to array can be expected in home-made arrays than in mass produced ones.An evaluation of image analysis methods for spotted cDNA arrays was reported by Yang et al.[8].For a microarray project,the image quantiﬁcation marks the transition in the workﬂow from “wet lab”procedures to computational ones.Hence,this is a good point to spend some effort looking at the quality and plausibility of the data.This has several aspects:conﬁrm that positive and negative controls behave as expected;verify that replicates yield measurements close to each other;and check for the occurrence of artifacts,biases,or errors.In the following we present a4−20002004006008000100300Figure 2:Histogram of probe intensities at the green wavelength for a cDNA microarray similar to the one depicted in Fig.1.The intensities were determined,in arbitrary units,by an image quantiﬁcation method,and “local background”intensities were subtracted.Due to measurement noise,this lead to non-positive probe intensities for part of the genes with low or zero abundance.The x -axis has been cut off at the 99%quantile of the distribution.The maximum value is about 4000.number of data exploration and visualization methods that may be useful for these tasks.2.2Dynamic range and spatial effectsA simple and fundamental property of the data is the dynamic range and the distribution of intensities.Since many experimental problems occur at the level of a whole array or the sample preparation,it is instructive to look at the histogram of intensities from each sample.An example is shown in Fig.2.Typically,for arrays that contain quasi-random gene selections,one observes a unimodal distribution with most of its mass at small intensities,corresponding to genes that are not or only weakly transcribed in the sample,and a long tail to the right,corresponding to genes that are transcribed at various levels.In most cases,the occurence of multiple peaks in the histogram indicates an experimental artifact.To get an overview over multiple arrays,it is instructive to look at the box plots of the intensities from each sample.Problematic arrays should be excluded from further analysis.Crude artifacts,such as scratches or spatial inhomogeneities,will usually be noticed already from the scanner image at the stage of the image quantiﬁcation.Nevertheless,to get a quick and potentially more sensitive view of spatial effects,a false color representation of the probe intensities as a function of their spatial coordinates can be useful.There are different options for the intensity scaling,among them the linear,logarithmic,and rank scales.Each one will highlight different features of the spatial distribution.Examples are shown in Fig.3.2.3ScatterplotUsually,the samples hybridized to a series of arrays are biologically related,such that the tran-scription levels of a large fraction of genes are approximately the same across the samples.This can be expected e.g.for cell cultures exposed to different conditions or for cells from biopsies of5a)b)c)d)e)f)Figure3:False color representations of the spatial intensity distributions from three different 64×136spot cDNA microarrays from one experiment series.a)probe intensities in the red color channel,b)local background intensities,c)background-subtracted probe intensities.In a) and b),there is an artifactual intensity gradient,which is mostly removed in c).For visualization, the color scale was chosen in each image to be proportional to the ranks of the intensities.d) For a second array,probe intensities in the green color channel.There is a rectangular region of low intensity in the top left corner,corresponding to one print-tip.Apparently,there was a sporadic failure of the tip for this particular array.Panels e)and f)show the probe intensities in the green color channel from a third array.The color scale was chosen proportional to the logarithms of intensities in e)and proportional to the ranks in f).Here,the latter provides better contrast.Interestingly,the bright blob in the lower right corner appears only in the green color channel,while the half moon shaped region appears both in green and red(not shown).6GR Figure 4:Scatterplot of probe intensities in the red and the green color channel from a cDNA array containing 8000probes.−4−202A =log 2R +log 2G M =l o g 2R −l o g 2G a)−4−202A =log 2(R +c )+log 2G M =l o g 2(R +c )−l o g 2Gb)Figure 5:a)the same data as in Fig.4,after logarithmic transformation and clockwise rotation by45◦.The dashed line shows a local regression estimate of the systematic effect M 0(A ),see text.b)similar to a),however a constant value c =42has been added to the red intensities before log transformation.After this,the estimated curve for the systematic effect M 0(A )is approximately constant.the same tissue type,possibly subject to different disease conditions.We call this the majority of genes unchanged property.Visually,it can be veriﬁed from the scatterplot of the probe intensities for a pair of samples.An example is shown in Fig.4.The scatterplot allows to assess both measurement noise and systematic biases.Ideally,the data from the majority of the genes that are unchanged should lie on the bisector of the scatterplot.In reality,there are both systematic and random deviations from this [9].For instance,if the label incorporation rate and photoefﬁciency of the red dye were systematically lower than that of the green dye by a factor of 0.75,the data would be expected not to lie on the bisector,but rather on the line y =0.75x .Most of the data in Fig.4is squeezed into a tiny corner in the bottom left of the plot.More informative displays may be obtained from other axis scalings.A frequently used choice is the double-logarithmic scale.An example is shown in Fig.5.It is customary to transform to new7variables A=log R+log G,M=log R−log G[10].Up to a scale factor of √2,this correspondsto a clockwise coordinate system rotation by45◦.The horizontal coordinate A is a measure of average transcription level,while the log-ratio M is a measure for differential transcription. If the majority of genes are not differentially transcribed,the scatter of the data points in the vertical direction may be considered a measure of the random variation.Fig.5a also shows a systematic deviation of the observed values of M from the line M=0,estimated through a local regression line1.There is an apparent dependence M0(A)of this deviation on the mean intensity A.However,this is most likely an artifact of applying the logarithmic transformation:as shown in Fig.5b,the deviation may be explained sufﬁciently well through a constant M0(A)=M0if an appropriate offset is added to the R values before taking the logarithm.Note that a horizontal line at M=M0in Fig.5b corresponds to a straight line of slope2M0and with intercept c in Fig.4.Fig.5shows the heteroskedasticity of log-ratios:while the variance of M is relatively small and approximately constant for large average intensities A,it becomes larger as A decreases. Conversely,examination of the differences R−G,for example through plots like in Fig.4, shows that their variance is smallest for small values of the average intensity R+G and increases with R+G.Sometimes,one wishes to visualize the data in a manner such that the variance is constant along the whole dynamic range.A data transformation that achieves this goal is called a variance-stabilizing transformation.In fact,homoskedastic representations of the data are not only useful for visualization,but also for further statistical analyses.This will be discussed in more detail in Section3.Two extensions of the scatterplot are shown in Figs.6and7.Rather than plotting a symbol for every data point,they use a density representation,which may be useful for larger arrays.For ex-ample,Fig.6shows the scatterplot from the comparison of two tissue samples based on152,000 probes2.The point density in the central region of the plot is estimated by a kernel density esti-mator.Three-way comparisons may be performed through a projection such as in Fig.7.This uses the fact that the(1,1,1)-component of a three-way microarray measurement corresponds to average intensity,and hence is not directly informative with respect to differential transcription. Note that if the plotted data was pre-processed through a variance-stabilizing transformation,its variance does not depend on the(1,1,1)-component.2.4Batch effectsPresent day microarray technology measures abundances only in terms of relative probe inten-sities,and generally provides no calibration to absolute physical units.Hence,the comparison of measurements between different studies is difﬁcult.Moreover,even within a single study, the measurements are highly susceptible to batch effects.By this term,we refer to experimental factors that(i)add systematic biases to the measurements,and(ii)may vary between different subsets or stages of an experiment.Some examples are[9]:1We used loess[11]with default parameters span=0.75,degree=2.2The arrays used were RZPD Unigene-II arrays(www.rzpd.de).8N0 + normalc−mycnme1trail 024681012141618−6−4−2024Figure 6:Scatterplot of a pairwise comparison of non-cancerous colon tissue and a colorectal tumor.Individual transcripts are represented by ’x’symbols.The x -coordinate is the average of the appropriately calibrated and transformed intensities (cf.Section 3).The y -coordinate is their difference,and is a measure of differential transcription.The array used in this experiment contained 152,000probes representing around 70,000different clones.Since plotting all of these would lead to an uninformative solid black blob in the centre of the plot,the point density is visualized by a color scale,and only 1500data points in sparser regions are individually plotted.1.spotting:to manufacture spotted microarrays,the probe DNA is deposited on the surfacethrough spotting ually,the robot works with multiple pins in parallel,and the ef-ﬁciency of their probe delivery may be quite different (e.g.Fig.3d or [10]).Furthermore,the efﬁciency of a pin may change over time through mechanical wear,and the quality ofthe spotting process as a whole may be different at different times,due to varying temper-ature and humidity conditions.2.PCR ampliﬁcation:for cDNA arrays,the probes are synthesized through PCR,whoseyield varies from instance to instance.Typically,the reactions are carried out in parallelin 384-well plates,and probes that have been synthesized in the same plate tend to havehighly correlated variations in concentration and quality.An example is shown in Fig.8.3.sample preparation protocols:The reverse transcription and the labeling are complex bio-chemical reactions,whose efﬁciencies are variable and may depend sensitively on a num-ber of hard-to-control circumstances.Furthermore,RNA can quickly degrade,hence theoutcome of the experiment can depend sensitively on when and how conditions that pre-vent RNA degradation are applied to the tissue samples.4.array coating:both the efﬁciency of the probe ﬁxation on the array,as well as the amountof unspeciﬁc background ﬂuorescence strongly depend on the array coating.5.scanner and image analysis9normaltumor N0tumor N1c−myc nme1trail −6−5−4−3−2−1012345−4−20246Figure 7:Scatterplot of a triple comparison between non-cancerous colon tissue,a lymph-node negative colorectal tumor (N0),and a lymph-node positive tumor (N1).The measurements from each probe correspond to a point in three-dimensional space,and are projected orthogonally on a plane perpendicular to the (1,1,1)-axis.The three coordinate axes of the data space correspond to the vectors from the origin of the plot to the three labels “normal”,“tumor N0”,and “tumor N1”.The (1,1,1)-axis corresponds to average intensity,while differences between the three tissues are represented by the position of the measurements in the two-dimensional plot plane.For instance,both c-myc and nme1are higher transcribed in the N0and in the N1tumor,compared to the non-cancerous tissue.However,while the increase is approximately balanced for c-myc in the two tumors,nme1(nucleoside diphosphate kinase A)is more upregulated in the N1tumor than in the N0tumor,a behavior that is consistent with a gene involved in tumor progression.On the other side,the apoptosis inducing receptor trail-r2is down-regulated speciﬁcally in the N1tumors,while it has about the same intermediate-high transcription level in the non-cancerous tissue and the N0tumor.Similar behavior of these genes was observed over repeated experiments.10−50510152025−10−505t u m o r − n o r m a l 10203040P C R p l a t e s −22468I n t e n s i t i e s −22468PCR plates: tumor I n t e n s i t i e s Figure 8:Top panel:scatterplot of intensities from a pair of cDNA arrays,comparing renal cell carcinoma to matched non-cancerous kidney tissue.Similar to Fig.6,the x -coordinate represents average,and the y -coordinate differential signal.In the bottom of the plot,there is a cloud of probes that appear to represent a cluster of strongly down-regulated genes.However,closer scrutiny reveals that this is an experimental artifact:the bottom panels show the boxplots of the intensities for the two arrays,separately for each of the 41PCR plates (see text).Probes from plates no.21,22,27,and 28have extraordinarily high intensities on one of the arrays,but not on the other.Since the clone selection was quasi-random,this points to a defect in the probe synthesis that affected one array,but not the other.The discovery of such artifacts may be helped by coloring the dots in the scatterplot by attributes such as PCR plate of origin or spotting pin (for technical reasons,the print version of this ﬁgure is shown in grey-scale).While the example pre-sented here is an extreme one,caution towards batch artifacts is warranted whenever arrays from different manufacturing lots are used in a single study.11These considerations have important consequences for the experimental design:ﬁrst,any vari-ation that can at any means be avoided within an experiment should be avoided.Second,any variation that cannot be avoided should be organized in such a manner that it does not confound the biological question of interest.Clearly,when looking for differences between two tumor types,it would not be wise to have samples of one tumor type processed by one laboratory,and samples of the other type by another laboratory.Points1and2are speciﬁc for spotted cDNA arrays.To be less sensitive against these variations, the two-color labeling protocol is used,which employs the simultaneous hybridization of two samples to the same array[3].Ideally,if only ratios of intensities between the two color channels are considered,variations in probe abundance should cancel out.Empirically,they do not quite, which may,for example,be attributed to the fact that observed intensities are the sum of probe-speciﬁc signal and unspeciﬁc background[12].Furthermore,in the extreme case of total failure of the PCR ampliﬁcation or the DNA deposition for probes on some,but not all arrays in an experimental series,artifactual results are hardly avoidable.If any of the factors3–5is changed within an experiment,there is a good chance that this will later show up in the data as one of the most pronounced sources of variation.A simple and instructive visual tool to explore such variations is the correlation plot:Given a set of d arrays, each represented through a high-dimensional vector Y i of suitably transformed andﬁltered probe intensities,calculate the d×d correlation matrix corr( Y i, Y j),sort its rows and columns according to different experimental factors,and visualize the resulting false color images.3Error models,calibration and measures of differential ex-pressionThe relation between a measured intensity y ki of probe k and the true abundance x ki of molecule type k in sample i may be described asy ki=a ki+b ki x ki.(1) The gain factor b ki represents the net result of the various experimental effects that come between the count of molecules per cell in the sample and theﬁnal readout of the probe intensity,such as:number of cells in the sample,the mean number of label molecules attaching to a sample molecule,hybridization efﬁciency,label efﬁciency,and detector gain.The additive term a ki accounts for that part of the measured intensity that does not result from x ki,but from effects such as unspeciﬁc hybridization,backgroundﬂuorescence,stray signal from neighboring probes, and detector offset.The parameters a ki and b ki are different for each probe k and for each hybridization i.It is not practical to determine them exactly,but neither is it necessary.Rather,one is content with obtaining statistical statements about relative abundances.To this end,one may build stochastic models for the effects a ki and b ki.Different variations on this theme have been proposed,as will be presented below.First,however,we would like to discuss the functional form of Eqn.(1),whose major statement is that when the true abundance x ki increases,the measured signal y ki increases proportionally.12Could it be necessary to consider more complex non-linear relationships?Clearly,this cannotbe ruled out for all possible experiments,or for future technologies.However,a linear operatingrange over several orders of magnitude has been reported by a number of authors for currentmicroarray technologies(e.g.[13,14,15]).At the lower end,this range is limited only by therequirement that x ki be non-negative.At the upper end,the linear range is limited by satura-tion effects such as quenching,limited probe abundance,and detector saturation.However,forrealistic concentrations of sample molecules,the upper limit is not reached in well-conductedexperiments.3.1Multiplicative calibration and noiseIn a seminal paper in1997,Chen et al.[16]introduced a decomposition of the multiplicativeeffect(cf.Eqn.(1)),b ki=b iβk(1+εki).(2) Here,βk is a probe-speciﬁc coefﬁcient,the same for all samples.For each sample i,the nor-malization factor b i is applied across all probes.The remaining variation in b ki that cannot beaccounted for byβk and b i is absorbed byεki.Furthermore,since the measured intensities y ki arealready“background-corrected”by the image analysis software’s local background estimation,Chen et al.assumed the additive effects a ki to be negligibly small.They further simpliﬁed theproblem in two steps:First,they noted that one is mainly interested in relative comparisons between the levels of thesame gene under different conditions,i.e.,in the ratios x ki/x kj.Hence the probe-speciﬁc effectsβk can be absorbed,µki=βk x ki,simply rescaling the units in which molecule abundances aremeasured,and need not be determined.Second,they turned to a stochastic description,and modeledεki as a normally distributed noiseterm with mean zero and standard deviation c,independent of i and k.Thus,in the model ofChen et al.the measured intensity Y ki is a random variable and depends on the true levelµki asfollows:Y ki=b iµki(1+εki),εki∼N(0,c2).(3) Note that Y ki has constant coefﬁcient of variation c.Chen et al.speciﬁcally considered two-color cDNA microarrays,where i=1,2represents thered and the green color channel,respectively.For a given true ratioµk1/µk2,Chen et al.derivedthe distribution of the observed,normalized ratio M k=Y k2/Y k1×b1/b2.It only depends on the values of c and b1/b2,and Chen et al.gave an algorithm for the estimation of these param-eters from the data.Based on this,they were able to formulate a statistical test for differentialexpression,i.e.a test against the hypothesisµk1=µk2.Hence,the ratios M k were regarded asa sufﬁcient summary of the results from a single microarray slide,and they,or their logarithms,would then be used as the input for further higher level analyses of data from multiple slides.To allow for a more systematic analysis of multiple slide experiments,Kerr et al.proposed anapproach based on the ANOV A technique[17].They modeled the measured intensity Y kjlm ofprobe k on slide j,in the color channel of dye l,from a sample that received treatment m,as log Y kjlm=g k+s j+d l+v m+[gs]kj+[gv]km+εkjlm.(4)13。

ANALYSIS OF DIFFERENTIAL GENE EXPRESSION

专利名称：ANALYSIS OF DIFFERENTIAL GENEEXPRESSION发明人：THOMAS, NICHOLAS,WAGGONER, ALAN 申请号：GB0000807申请日：20000309公开号：WO0055363A3公开日：20001221专利内容由知识产权出版社提供摘要：The invention provides methods for detecting the differential expression or presence of two analytes, and more specifically to procedures which provide for rapid and efficient analysis of gene expression in biological systems. In particular, the invention provides a method of detecting and analysing differences between nucleic acids from two sources, which method comprises: a. providing nucleic acids from two sources as labelled probes; b. forming a mixture of the labelled probes with pooled reagents wherein each reagent is a population of beads carrying a polynucleotide target, the target of one reagent being different from the target of another reagent, the beads of one reagent being distinguishable from the beads of another reagent; c. incubating the mixture under conditions to promote specific hybridisation between probes and targets; and, d. analysing beads in the mixture by flow cytometry.申请人：AMERSHAM PHARMACIA BIOTECH UK LTD,AMERSHAM PHARMACIA BIOTECH INC,THOMAS, NICHOLAS,WAGGONER, ALAN更多信息请下载全文后查看。

如何识别和应对假冒产品英语作文

如何识别和应对假冒产品英语作文Identifying and Dealing with Counterfeit ProductsCounterfeit products are becoming increasingly prevalent in today's global market. These products are often of inferior quality and can pose serious health and safety risks to consumers. Therefore, it is important for consumers to be able to identify and deal with counterfeit products effectively.There are several ways to identify counterfeit products. One of the most common methods is to carefully examine the packaging and labeling of the product. Look for any misspelled words, poor quality printing, or irregularities in the packaging. Many counterfeit products also have a lack of proper trademarks or logos. In addition, consumers can also check the product's retail price against the market average. If the price seems too good to be true, it probably is.Another way to identify counterfeit products is to purchase from reputable and authorized retailers. This can greatly reduce the risk of purchasing counterfeit products.Authorized retailers often have strict quality control measures in place and are less likely to sell counterfeit goods.If consumers suspect that they have purchased a counterfeit product, there are several steps that they can take to deal with the situation. First, they should document their purchase with a receipt or proof of purchase. Next, they should contact the retailer or manufacturer to report the issue. It is important to provide as much detail as possible, including the product's packaging and any relevant serial or model numbers. In some cases, theretailer or manufacturer may offer a refund or replacement for the counterfeit product.Consumers can also report the counterfeit product to the relevant authorities, such as consumer protection agenciesor intellectual property rights organizations. By reporting the counterfeit product, consumers can help to prevent others from falling victim to the same scam. Additionally, reporting counterfeit products can help authorities to identify and shut down the sources of counterfeit goods.In conclusion, it is crucial for consumers to be able to identify and deal with counterfeit products effectively. By carefully examining packaging and labels, purchasing from authorized retailers, and taking appropriate action when counterfeit products are suspected, consumers can protect themselves and others from the dangers of counterfeit goods.识别和应对假冒产品假冒产品在当今全球市场中变得越来越普遍。

惯量辨识英语

惯量辨识英语**Inertia Identification in English: A New Approach to Overcome Language Barriers**In the age of globalization, effective communication has become an integral part of our daily lives, especially in the realm of international business, education, and technology. However, language barriers often pose significant challenges that hinder the smooth flow of ideas and information. Among the various strategies to address these challenges, inertia identification in English has emerged as a promising approach to bridging the language divide.Inertia identification, at its core, refers to the process of recognizing and understanding the patterns and trends inherent in a given language, allowing speakers to more effectively convey their messages. In the context of English, this involves a deep understanding of the language's syntax, vocabulary, and cultural context, enabling fluent communication despite linguistic differences.The significance of inertia identification in English lies in its ability to overcome the limitations of traditional language learning methods. Traditional methods often focus on rote memorization and grammar rules, ignoring the dynamic and contextual nature of language. By contrast, inertia identification emphasizes the importance of intuitive understanding and immersion in the language, fostering a more natural and authentic communication style. One of the key benefits of inertia identification isits adaptability to different communication scenarios. Whether it's a business negotiation, an academic presentation, or a casual conversation, inertia identification allows speakers to adjust their language use to fit the context and audience. This flexibility iscrucial in today's interconnected world, where adaptability and cultural sensitivity are paramount.Moreover, inertia identification promotes a more inclusive and diverse communication environment. By acknowledging the unique characteristics of different languages and cultures, it encourages speakers to embrace their own linguistic identities while respecting those ofothers. This approach not only enhances cross-cultural understanding but also fosters a more inclusive and harmonious global community.In summary, inertia identification in English offers a fresh perspective on language learning and communication. By emphasizing intuitive understanding, cultural sensitivity, and adaptability, it enables speakers to overcome language barriers and connect with diverse audiences across the globe. As we move forward in an increasingly interconnected world, inertia identification will play a pivotal role in promoting effective and inclusive communication.**惯量辨识英语：跨越语言障碍的新方法**在全球化的时代，有效的沟通已成为我们日常生活中不可或缺的一部分，特别是在国际商业、教育和技术领域。

这两个作文不一样英文

这两个作文不一样英文Sure, I understand that you want two different essaysin English without revealing your prompt. Here are two unique compositions for you:Essay 1: Exploring Cultural Identity。

Cultural identity is a complex and multifaceted aspect of human existence. It encompasses the beliefs, traditions, values, and behaviors that define a particular group of people. In today's globalized world, individuals often find themselves negotiating between various cultural influences, leading to a rich tapestry of identities.One of the primary factors shaping cultural identity is heritage. Heritage refers to the customs, language, and history passed down through generations within a community. For example, someone raised in a family with strong Italian roots may feel a deep connection to Italian culture, including its cuisine, language, and traditions. This senseof heritage can form the foundation of an individual's cultural identity.Another influential factor is environment. The environment in which a person grows up plays a significant role in shaping their cultural identity. For instance, someone raised in a diverse urban neighborhood may have exposure to a wide range of cultures, leading to a more eclectic identity. On the other hand, someone raised in a rural community with limited diversity may have a more homogeneous cultural identity reflective of their immediate surroundings.Moreover, cultural identity can be influenced by experiences and interactions with others. Traveling, studying abroad, or even engaging with different cultural communities within one's own country can broaden one's perspective and shape their identity. For example, a person who volunteers with immigrant communities may develop a deeper appreciation for cultural diversity and incorporate elements of those cultures into their own identity.However, navigating multiple cultural identities can also pose challenges. It can lead to feelings of belongingness to neither here nor there, commonly known as cultural dissonance. This phenomenon occurs whenindividuals feel disconnected from their ancestral culture while also not fully assimilating into the dominant culture of their current environment. Such individuals may struggle to reconcile conflicting cultural norms and expectations, leading to a sense of identity crisis.In conclusion, cultural identity is a dynamic and multifaceted aspect of human existence shaped by heritage, environment, and experiences. While it enriches our lives by providing a sense of belonging and connection to others, it can also present challenges as individuals navigate the complexities of multiple cultural influences. Embracing diversity and fostering understanding are essential in building a more inclusive society where everyone's cultural identity is respected and celebrated.Essay 2: The Impact of Technology on Education。

火焰原子吸收法测定污水中微量汞

【技术与方法】文章编号:1001-5914(2000)05-0298-02火焰原子吸收法测定污水中微量汞陈保善　刘宁英(宁夏回族自治区卫生防疫站,银川750004) 摘要:试图采用一种新的测试方法进行微量汞的测试。

即在原子吸收分光光度计的燃烧头上加装一支开缝石英管并且在样品及标准列中加一定量的基体改进剂,可使原子吸收分光光度计火焰法直接测定污水中的微量汞。

其优点是样品的前处理只需消化,加入基体改进剂定容,用火焰法直接进行测定,不需中间转化过程,从而减少了系统误差。

方法的回收率98.6%,相对标准偏差1.02%,检出限为0.01L g/mL,且测定样品快速、方便、准确。

关键词:原子吸收;石英管;汞中图分类号:R123.3 文献标识码:BDeterminat ion of Trace Mercury in Sewage by Flame Atomic Absorption Spectrophotometry　CHEN Bao-shan,L IUN ing-Ying.Sanitation and A nti-ep id emic S tation of N ingx ia H uiz u A utonomus R egion,Y inchuan250004 Abstract:A modified method for detecting trace mer cur y in sewag e w as developed in t his assay.T he tr acemer cur y co uld be directly det ermined by flame atomic absor pt ion spectr ophoto metr y without any inter mediatetr ansfo rma tio n,by means of inst alling a gaped quar tz t ube on the bur ner head of flam e ato mizer and adding a matr ixpro moto r int o the digested samples a nd wo r king standar d ser ies solut ion fo r v olumet ic indentficat ion.T headvantag es o f this method w er e simplification o f t he pretr eatment pr o cesses o f samples only by dig est ion,vo lumetr icidentification by a dding pro moto r o f basal bo dy,dir ect deter mination by flame ato mizer w itho ut t ransfor mation andsig nificant reduct ion o f systemic er ro rs.T his metho d,w ith r eco ver y r ate o f98.6%,relativ e standar d deviatio n of1.02%and detectio n limit of0.01L g/m L,pr esent ed r apid,co nv enient and accur ate for det ect ion of tr ace mercuryin sewa ge samples.Key words:A to mic absor ption;Q ua rtz tube;M ercur y 用火焰原子吸收法本不能直接用于测定汞,而使用改进后的方法,使火焰法直接测定汞成为可能。

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Identi ﬁcation of differentially expressed genes in cucumber (Cucumis sativus L.)root under waterlogging stress by digital gene expression pro ﬁleXiao-Hua Qi,Xue-Wen Xu,Xiao-Jian Lin,Wen-Jie Zhang,Xue-Hao Chen ⁎School of Horticulture and Plant Protection,Yangzhou University,48Wenhui East Road,Yangzhou,Jiangsu 225009,Chinaa b s t r a c ta r t i c l e i n f o Article history:Received 11October 2011Accepted 23December 2011Available online xxxx Keywords:CucumberDigital gene expression Transcriptome pro ﬁle Waterlogging qRT-PCRHigh-throughput tag-sequencing (Tag-seq)analysis based on the Solexa Genome Analyzer platform was ap-plied to analyze the gene expression pro ﬁling of cucumber plant at 5time points over a 24h period of water-logging treatment.Approximately 5.8million total clean sequence tags per library were obtained with 143013distinct clean tag sequences.Approximately 23.69%–29.61%of the distinct clean tags were mapped unambiguously to the unigene database,and 53.78%–60.66%of the distinct clean tags were mapped to the cucumber genome database.Analysis of the differentially expressed genes revealed that most of the genes were down-regulated in the waterlogging stages,and the differentially expressed genes mainly linked to car-bon metabolism,photosynthesis,reactive oxygen species generation/scavenging,and hormone synthesis/signaling.Finally,quantitative real-time polymerase chain reaction using nine genes independently veri ﬁed the tag-mapped results.This present study reveals the comprehensive mechanisms of waterlogging-responsive transcription in cucumber.©2011Elsevier Inc.All rights reserved.1.IntroductionCucumber (Cucumis sativus L.),an agriculturally and economically important crop worldwide,is easily affected by heavy rain and subse-quent periods of soil ﬂooding in summer,especially in South China.Under waterlogging or submergence,plants are exposed to a reduc-tion in oxygen (O 2)supply because of the slow diffusion rate of O 2in water and its limited solubility [1].During prolonged periods of soil ﬂooding,a decrease in root hydraulic conductance causes impair-ment in water uptake.One of the early responses to prevent water loss appears to involve closing of the stomata with subsequent down-regulation of the photosynthetic machinery,which eventually leads to leaf wilting and chlorosis [2].Waterlogging is a compound stress of interacting changes inside plant cells induced by ﬂoodwater surrounding the plant [3],and re-sults in a decreased level of O 2in the plant root zone caused by the low diffusion rate of molecular O 2in water.One of the major cellular pathways dependent on O 2is mitochondrial respiration.To maintain energy generation under conditions of decreased O 2availability,plants switch from respiratory to fermentative metabolism.Fermen-tation allows regeneration of NAD+in the absence of respiration,thereby maintaining glycolysis and ATP generation under anaerobicconditions.The signi ﬁcantly lower energy yield of alcoholic fermenta-tion,compared with mitochondrial respiration,causes an energy cri-sis in anaerobic tissues [4].Responses to low O 2levels (hypoxia)have been studied in Arabidopsis thaliana and rice,as well as in a number of crop species [5–8].Low O 2levels cause rapid changes in gene tran-scription,protein synthesis and degradation,and cellular metabolism [4].Global gene expression studies in Arabidopsis,rice,poplar,and cotton have revealed complex responses to low O 2involving signi ﬁ-cant changes in approximately 5%–10%of all the genes assayed [5–8].The hypoxia-induced genes identi ﬁed include those encoding anaerobic polypeptides (ANP)proteins,most of which are enzymes involved in sugar metabolism,glycolysis,and fermentation pathways [9].In addition to those encoding ANPs,additional hypoxia-induced genes that were identi ﬁed include transcription factors [10]and sig-nal transduction components [11],as well as those involved in ethyl-ene biosynthesis [6,12],nitrogen metabolism [6],sulfur metabolism [13],reactive oxygen species (ROS)generation ⁄scavenging [12,14],and cell wall loosening [15].Waterlogging stress should be seen as a compound stress com-posed of several underlying changes in substances such as ethylene,CO 2,O 2,ROS,and phytotoxins within the plants and from external sources [3].Although studies on model plant responses to low O 2have provided crucial insights,they were often performed under highly arti ﬁcial conditions (e.g.,entire plants were placed in low O 2atmospheres,sometimes supplemented with sugars,and sometimes in the dark),which do not completely simulate the effects seen in soil under ﬁeld conditions.Previous transcriptome pro ﬁling studies based on microarray data have some limitations as well becauseGenomics xxx (2012)xxx –xxxAbbreviations:DGE,digital gene expression;ERF,ethylene responsive factor;POD,peroxidase;CAT,catalase;SOD,superoxide dismutase;ETR,ethylene receptor;RBOH,respiratory burst oxidase homolog.⁎Corresponding author.Fax:+8651487347537.E-mail address:xhchen@ (X.-H.Chen).YGENO-08371;No.of pages:9;4C:0888-7543/$–see front matter ©2011Elsevier Inc.All rights reserved.doi:10.1016/j.ygeno.2011.12.008Contents lists available at SciVerse ScienceDirectGenomicsj o u r n a l h o m e p a ge :w ww.e l s e v i e r.c o m/l o c a t e /y g e n ogenes are represented by unspeci ﬁc probe sets,and,at low expression levels,they cannot be reliably detected.Deep-sequencing technology has recently become a powerful tool that allows the concomitant se-quencing of millions of signatures to the genome and identi ﬁcation of speci ﬁc genes and the abundance of gene expression in a sample tis-sue [16].This method provides a more qualitative and quantitative description of gene expression than previous microarray-based as-says [17].The aim of this study was to gain insight into the molecular mech-anisms of cucumber's responses to waterlogging,using one waterlogging-tolerant cucumber line (‘Zaoer N ’).In this work,a genome-wide analysis of gene expression pro ﬁling at 5time points during a 24h waterlogging treatment was performed on cucumber plants using massively parallel deep-sequencing by Solexa Illumina.Our results yielded sets of up-regulated and down-regulated genes in response to waterlogging stress,and some candidate genes related to waterlogging tolerance in cucumber were discussed.2.Results2.1.Analysis of digital gene expression (DGE)librariesTo investigate the transcriptome response to waterlogging stress in cucumber plant,the Solexa Genome Analyzer was used to perform high throughput Tag-seq analysis on cucumber root libraries that were constructed at 5time points before and during the 24h period of waterlogging treatment.The samples which were collected at 0,2,4,8,and 24h after waterlogging treatments were named libraries R1,R2,R3,R4,and R5,respectively.The major characteristics of these ﬁve libraries are summarized in Table 1.Approximately 6mil-lion total sequence tags per library were obtained with 354147dis-tinct tag sequences.Prior to mapping these tag sequences to the reference sequences,adaptor tags were ﬁltered (low quality tags and tags with one copy),producing approximately 5.8million total clean sequence tags per library with 143013distinct clean tag se-quences.The distribution of the total and distinct clean tag copy num-bers show highly similar tendencies in the ﬁve libraries (Fig.1).Among the distinct clean tags,6%had copy numbers higher than 100counts,32%of the distinct clean tags present had between 5and 50copies,and more than 60%of the distinct clean tags had 2–5copies.The R4library had the highest number of both distinct tags and distinct clean tags;the other four libraries had similar counts.Moreover,the R4library showed the highest ratio of number of dis-tinct clean tags to total clean tags,and the lowest percentage of dis-tinct high copy number tags (Fig.S1).These data suggested that more genes were detected in the R4library than in the other four li-braries,and more transcripts were expressed at lower levels in the R4library.2.2.Analysis of tag mappingA reference gene database that included 76096sequences of the C.sativus unigene was preprocessed for tag mapping.Among the se-quences,the genes with a CATG site accounted for 45.43%.To obtain the reference tags,all the CATG+17tags in the gene were taken as gene reference tags.Finally,60349total reference tag sequences with 57383unambiguous reference tags were obtained.Based onTable 1Categorization and abundance of tags.Clean tags are tags that remained after ﬁltering out dirty tags (low quality tags)from raw data.Distinct tags are different kinds of tags.Unambiguous tags are clean tags remaining after the removal of tags mapped to reference sequences from multiple genes.Summary R1R2R3R4R5Raw data Total57522225996381592670662202606216567Distinct tags 333045346322355635386490349246Clean tagsTotal number55566665785566570440359879276003702Distinct tag numbers 139540139757137670158301139798All tag mapping to geneTotal number35077123482645352274433823963167340Total %of clean tags 63.13%60.20%61.75%56.49%52.76%Distinct tag numbers4356544612453724122139632Distinct Tag %of clean tags 31.22%31.92%32.96%26.04%28.35%Unambiguous Tag mapping to geneTotal number29332502897217292129128907002688894Total %of clean tags 52.79%50.08%51.21%48.28%44.79%Distinct tag numbers3942240300407623749835984Distinct Tag %of clean tags 28.25%28.84%29.61%23.69%25.74%All tag-mapped genesNumber1921019209189781855318032%of ref genes 25.21%25.21%24.91%24.35%23.67%Unambiguous tag-mapped genes Number1689016911166961630615758%of ref genes 22.17%22.19%21.91%21.40%20.68%Mapping to genomeTotal number16631521823136177706822377722423404Total %of clean tags 29.93%31.51%31.15%37.37%40.37%Distinct tag numbers7533775158744739602180291Distinct tag %of clean tags 53.99%53.78%54.10%60.66%57.43%Unknown tagsTotal number385802479785404591367759412958Total %of clean tags 6.94%8.29%7.09% 6.14% 6.88%Distinct tag numbers2063819987178252105919875Distinct tag %of clean tags14.79%14.30%12.95%13.30%14.22%Fig.1.Distribution of total clean tag (ﬁlled)and distinct clean tag (open)copy numbers from the ﬁve libraries.2X.-H.Qi et al./Genomics xxx (2012)xxx –xxxthe criteria,23.69%–29.61%of the distinct clean tags were mapped unambiguously to the unigene database,53.78%–60.66%of the dis-tinct clean tags were mapped to the cucumber genome database, and12.95%–14.79%of the distinct clean tags did not map to the uni-gene virtual tag database(Table1).To estimate whether or not the sequencing depth was sufﬁcient for the transcriptome coverage,the sequencing saturation was ana-lyzed in theﬁve libraries.The genes that were mapped by all clean tags and unambiguous clean tags increased with the total number of tags.However,when the sequencing counts reached2million tags or higher,the number of detected genes was saturated(Fig. S2).Given that Solexa sequencing can distinguish transcripts origi-nating from both DNA strands,using the strand-speciﬁc nature of the sequencing tags obtained,we found evidence of bidirectional transcription in7073to7907of all detectable unigenes,and3451 to3688antisense strand-speciﬁc transcripts(Table S1).In compari-son,the ratio of sense to antisense strand of the transcripts was approximately1.17:1for all the libraries.This suggests that antisense genes also play important roles in the transcriptional regulation of waterlogging response in cucumber plant.2.3.Waterlogging caused changes in global gene expression in rootThe cucumber plants were waterlogged for2,4,8,and24h,and the global gene expression was assayed at each time point sample using the0h time point as a common reference.The time-course de-sign was made based on the preliminary quantitative RT-PCR(qRT-PCR)results which showed large increases in gene expression from 2h to24h for glyceraldehyde-3-phosphate dehydrogenase (gapdh3),alcohol dehydrogenases(adh),and pyruvate kinase(pdc). These genes(gapdh,adh,and pdc)exhibited expression patterns in these preliminary assays that were essentially the same as those in later assays of these same genes presented in Table2.To obtain theTable2Selected genes with altered expression in roots of waterlogged cucumber plants.Functional group Cucumber unigeneaccessionGene annotation a TPM(transcript per million clean tag)0h2h4h8h24hPhotosynthesisCU10576Photosystem II subunit S(PsbS)8.82 1.38 2.450.67 3.33Contig885Photosystem I psaG/psaK18.36 3.28 1.75 2.17 2.5CU9779Ferredoxin34.197.618.940.013TCA cycleCU13500Phosphoenolpyruvate carboxykinase12.967.2610.5210.6969.12CU6042Isocitrate dehydrogenase15.8430.777.367.52 3.33CU10529Succinyl-CoA ligase subunit alpha-280.2676.2282.7427.2223.15CU7303Pyruvate dehydrogenase E1alpha subunit10.629.6810.34 3.01 4.33GlycolysisCU11911Hexokinase2 6.6628.1716.6530.5648.8CU24433Phosphofructokinase11.3462.5767.32181.248.97CU27835Fructose-1,6-bisphosphate aldolase0.5412.797.3621.0412.66CU9751Glyceraldehyde-3-phosphate dehydrogenase737.672200.133527.637731.069252.62CU14197Phosphoglycerate kinase 2.8810.0215.089.85 3.33CU4080Pyruvate kinase34.19471.69528.71356.38457.22CU4073Alcohol dehydrogenases 5.76450.43471.74409.49405.08CU29145Alcohol dehydrogenase14.769.6810.52 1.17 1.17Pentose phosphate pathwayCU27835Fructose-1,6-bisphosphate aldolase0.5412.797.3621.0412.66HuangGua-Contig113Fructose-bisphosphate aldolase676.661005.781029.91229.29298.65HuangGua_2_014_17Phosphogluconate dehydrogenase 5.5814.3524.898.02 5.33Sucrose and starch metabolismCU69760Hexokinase20.36 1.9 1.93 6.68 5.66CU4126Sucrose synthase(SUS5)29.5130.5932.61 6.855CU6267alpha-1,4-glucan phosphorylase L isozyme 5.589.517.190.840.67HuangGua_1_017_77Glycosyl hydrolase36.8946.8465.7420.3714.82Hormone/signalingCU4787CS-ETR233.29147.09173.73521.05162.07CU24956Myb 4.1411.4112.2710.0223.15CU7162ACC oxidase2216.32395.81298.37254.18500.36CU11418Auxin response factor317.2818.3227.7 3.3430.15CU8095Ethylene-responsive transcription factor29.6942.6920.8658.9528.65CU19620WRKY transcription factor3061.7366.3756.1152.1452.3CU13592Auxin response factor254.7166.7270.321.7163.79HuangGua_1_026_72Ein3-binding f-box protein336.5319.0111.2220.888.83CU18697ERF transcription factor39.59 6.22 2.28 1.84 3.33Reactive oxygen species(ROS)generation/scavengingCU26027Peroxidase49.31143.81174.4326.5529.48CU10997Catalase isozyme212.24 6.4 5.96 1.170.5HuangGua-Contig135Chloroplast Cu/Zn superoxide dismutase73.6165.8591.5124.727.83HuangGua-Contig33Cytosolic ascorbate peroxidase668.21560.36561.32394.7969.96HuangGua_1_028_30Respiratory burst oxidase-like protein77.2292.11316.95348.03229.86HuangGua-Contig852Glutathione S-transferase36.5310.8911.2244.094HuangGua-Contig134Type-2metallothionein1045.41923.851219.06521.88193.88 a Putative function from GenBank BLASTX search().3X.-H.Qi et al./Genomics xxx(2012)xxx–xxxtranscriptional changes in waterlogged cucumber root,the rigorous algorithm method was applied to identify differentially expressed genes from the normalized DGE data by pairwise comparisons across all differential time points during the waterlogging stress treatment. The results showed that5787genes had P-values b0.05,false discov-ery rates(FDR)b0.01,and estimated absolute|log2Ratio|≥1in at least one of the pairwise comparisons,which were declared to be differen-tially expressed during the waterlogging course(Table S2).There were1180genes that were differentially expressed in the2h water-logged and non-waterlogged roots.Among these genes,551(46.7%) were up-regulated and629(53.3%)were down-regulated in response to waterlogging stress.Most of the genes were down-regulated in the following waterlogging stages,900(55.3%),2116(67%),and2153 (66%)genes for4,8,and24h waterlogged cucumber roots,respec-tively(Table3).The expression of most genes was suppressed at the latter part of the waterlogging period.Genes with altered expression responses spanned a wide variety of regulatory and metabolic processes.The differentially expressed genes in cucumber in R1and R5stages were classiﬁed into several categories based on their allocated GO terms using GOSlim Assign-ments(/)(Fig.2).The genes related to metabolic,cellular,cellular metabolic,primary metabolic,and mac-romolecular metabolic processes were abundant in the differentially expressed genes.Approximately26%and13%of the differentially expressed genes were categorized as gene response to stimulus and stress,respectively(Fig.2).To characterize the functional consequences of gene expression changes associated with waterlogging stress,a pathway analysis of the differentially expressed genes based on the KEGG database was performed.Only signiﬁcant pathway categories that had a P-value of b0.05and an FDR of b0.05were selected.An obvious increase in the expression of genes was associated with glycolysis,which is often a hallmark of low O2stress.In addition,genes associated with the citrate cycle,pantothenate and CoA metabolism,fatty acid metabolism,and glyoxylate were down-regulated(Fig.3).Genes as-sociated with the pentose phosphate pathway were up-regulated within theﬁrst4h of waterlogging stress treatment,and then down-regulated in the following stages(Table S3).2.4.Genes associated with major metabolism and ROS generation/scav-enging were affected in waterlogged cucumber rootThe gene expression results indicated that waterlogging could af-fect carbon metabolism,photosynthesis,ROS generation/scavenging, and ethylene synthesis/signaling in cucumber plant.Previous studies on waterlogging have also noted changes in photosynthetic rates,and carbohydrate content inﬂooded plants[18,19].In the present study, several genes implicated in photosynthesis,the expressions of which were changed signiﬁcantly under waterlogging conditions, were identiﬁed.The induced down-regulated genes included those encoding photosystem II subunit S(PsbS),photosystem I psaG/psaK, and ferredoxin proteins(Table2).The changes in carbon metabolism genes that were apparent in root,sucrose,and starch metabolism were also found to be affected by waterlogging(Table S3).Starch and sucrose production slowed down as transcript levels of the starch-branching enzyme and sucrose synthase decreased.In contrast,many genes associated with glycoly-sis and fermentation were induced by waterlogging.These included up-regulated genes encoding phosphofructo kinase,fructose-1,6-bisphosphate aldolase,glyceraldehyde-3-phosphate dehydrogenase, phosophoglycerate kinase,and pyruvate kinase,as well as down-regulated genes encoding phosphoglucomutase and galactose mutar-otase(Fig.S3).Consistent with these changes in transcript levels,the total soluble sugar content in roots was altered during waterlogging (Fig.4).As suggested from the decelerated sucrose and starch synthase and accelerated catabolism,the total soluble sugar content showed a slight,but not signiﬁcant,decrease after5d of waterlog-ging.Increased carbohydrate levels in the roots ofﬂooded plantsTable3Differentially expressed genes across all libraries.All the genes mapped to the reference sequence and genome sequence were examined for their expression differences across the different libraries.Numbers of differentially expressed genes represent across sense transcripts,using threshold values FDR≤0.001and|log2Ratio|≥1for controlling false discovery rates.R1,R2,R3,R4,and R5represent the samples which were collected at0,2,4,8,and24h after waterlogging treatments,respectively.R1vs.R2R1vs.R3R1vs.R4R1vs.R5R2vs.R3R2vs.R4R2vs.R5R3vs.R4R3vs.R5R4vs.R5Total118016273160326425826042525269625471243Up-regulated5517271044111157730684820763585 Down-regulated629900211621532011874184118761784658Fig.2.Gene classiﬁcation based on gene ontology(GO)for differentially expressed genes in cucumber in R1and R5stages.The frequency of GO terms was analyzed using GO Slim Assignment.The y-axis and x-axis indicate the names of clusters and the ratio of each cluster,respectively.Only the biological processes were used for GO analysis.4X.-H.Qi et al./Genomics xxx(2012)xxx–xxxhave been previously observed in wheat and Lotus japonicus [20,21],and have been attributed to reduced carbon demands caused by a de-cline in root growth and nitrogen metabolism.In contrast,the results of the present study suggest that an increase in sugar supply to hyp-oxic roots occurs to compensate for the increased carbohydrate de-mand under these conditions.Interestingly,similarly reduced carbohydrate concentrations have been reported in other studies in which various plant species,including Arabidopsis (A.thaliana ),bar-ley (Hordeum vulgar e),wheat (Triticum aestivum ),soybean (Glycine max ),potato (Solanum tuberosum ),and cotton were exposed to hyp-oxia [13,22–25],highlighting the importance of sugar delivery to roots in tolerating periods of O 2de ﬁciency.The genes involved in citrate cycles such as pyruvate dehydroge-nase,citrate hydrolyase,isocitrate dehydrogenease,succinyl-CoA li-gase,succinate dehydrogenenase,and malate dehydrogenase weredown-regulated in the waterlogged root of cucumber (Fig.S4,Table S3).However,the genes involved in pentose phosphate and glyoxy-late pathways were up-regulated after 8h,and then down-regulated.This observation indicates that the citrate cycle was sup-pressed by the low O 2condition,whereas the pentose phosphate and glyoxylate pathways were activated by waterlogging.The generation of ROS is characteristic of hypoxia [14].In the pre-sent study,several ROS generation/scavenging-relating genes were changed.Up-regulated genes included those encoding respiratory burst oxidase homolog (RBOH)and peroxidase (POD),and down-regulated genes included those encoding catalase (CAT),chloroplast Cu/Zn superoxide dismutase (SOD),cytosolic ascorbate POD,glutathi-one S-transferase,and type-2metallothionein (Table 2).2.5.Tag-mapped genes were con ﬁrmed by qRT-PCRTo con ﬁrm the tag-mapped genes in cucumber roots,nine genes were selected for qRT-PCR analysis over the time-course of the water-logging treatment from 0to 24h.Representative genes selected for the analysis were those involved in antioxidant system,as well as gly-colysis,mitochondrial electron transport,and ethylene production/signaling pathways because ethylene have been associated with low O 2stress [26].The expressions of seven genes (CS-ETR [ethylene re-ceptor],AOX2[alternative oxidase 2],ERF ,hypoxia-responsive pro-tein,MYB ,POD ,and AHB2[Arabidopsis hemoglobin 2])by using qRT-PCR agreed well with the Tag-seq analysis pattern.Only two genes (hexokinase 2and hypothetical protein)did not show consis-tent expression between qRT-PCR and Tag-seq data sets (Fig.5).3.Discussion3.1.Global gene transcription changes in the waterlogged cucumber root A global analysis of transcriptome could facilitate the identi ﬁca-tion of systemic gene expression and regulatory mechanisms for the waterlogging tolerance of a plant.In the present study,a transcrip-tome pro ﬁling of root was performed to identify genes that are differ-entially expressed in the early stage of waterlogging cucumber.A sequencing depth of 5.6–6.0million tags per library was reached (Table 1),and more than 85%unique tags were matched with the cu-cumber unigenes or genomic sequence,suggesting that the database selected is relatively complete.The global gene transcription in the stressed root signi ﬁcantly altered 1%–4%of the genes assayed during the 24h waterlogging treatment;the number of differentially expressed genes increased with the duration of the stress (Table 3).Many of the genes are known as responsive to stress and stimuli (Fig.2).Similar to root waterlogging experiments in poplar and cot-ton [8,13],increased expression of glycolysis,fermentation,and some catabolism pathways,and decreased expression of synthesis pathways,cell wall,and secondary metabolism-associated genes were observed.The gene transcription responses to waterlogging in cucumber resemble that of in Arabidopsis subjected to O 2deprivation [5],indicating that the major factor in waterlogging stress is lack of O 2,at least initially.3.2.Carbon and energy metabolism were affected by waterlogging Plants initiate several responses to alleviate the harm of O 2depri-vation during ﬂooding or waterlogging periods.The hypoxic re-sponses include the down-regulation of a suite of energy-related and O 2-consuming metabolic pathways [23].Examples of such meta-bolic adaptations to hypoxia include the down-regulation of storage metabolism [23],the shift from invertase to Suc synthase routes of Suc hydrolysis [27],and the inhibition of mitochondrial respiration [28].In the roots of waterlogged cucumber plants,many genes with potential roles in carbon and energy metabolism were identi ﬁed asStarch+ Glycolysis + GluconeogenesisChlorophyllEthanol+Fatty acid-Fig.3.Overview of major metabolic pathways in response to waterlogging in cucum-ber,as suggested by the overall expression pattern of waterlogging-responsive genes involved in primary biochemical pathways.Expression pro ﬁle of each individual gene is presented in Supplemental Table S3.Pathways with transcripts that were up-or down-regulated are indicated with +or −.Overview of major metabolic pathways in response to waterlogging in cucumber,as suggested by the overall expression pattern of waterlogging-responsive genes involved in primary biochemicalpathways.Expres-sion pro ﬁle of each individual gene is presented in Supplemental Table S3.Pathways with transcripts that were up-or down-regulated are indicated with +or −.Fig.4.The effect of waterlogging on total soluble sugar content in the root of C.sativus .Means and standard errors are shown from three replications.DAT,days after water-logging treatment.5X.-H.Qi et al./Genomics xxx (2012)xxx –xxxhaving signi ﬁcant transcriptional response to stress.The most notable examples are the up-regulation of genes involved in glycolysis and fermentation,and the down-regulation of genes involved in sucrose and starch metabolism,citrate cycle,mitochondrial electron trans-port,and photosynthesis (Table 2).Expressions of key genes in sucrose and starch metabolism,which include sus5,alpha-1,4-glucan phosphorylase L isozyme,and glycosyl hydrolase,were found to increase immediately after the waterlogging treatment and then decline after 8h,indicating that the sucrose and starch metabolism was initially activated,and then suppressed as the stress lasted.In addition,the total sol-uble sugar content in the waterlogged cucumber was not signi ﬁ-cantly affected by the treatment;however,it decreased on the ﬁfth day.The results are similar to those observed in mung bean and pigeon pea,where the total sugar content remained almost constant on the fourth day,and decreased on the sixth day [29,30].Under waterlogging conditions,O 2limits oxidative phosphoryla-tion,and plant cells depend on alternative metabolic pathways to produce ATP.In the present study,gene expressions involved in glycolysis and fermentation increased quickly between 2and 24h.This ﬁnding indicates that the major energy source is the glycolytic pathway,which produces two ATP and two pyruvate molecules per unit of hexose while concomitantly reducing NAD+to NADH.To maintain glycolysis under anoxic conditions,NAD+must be contin-uously regenerated from NADH via fermentative reactions [31].Most recent studies on carbon utilization for waterlogging tolerance responses focused on adh in this complicated cycle of pathways.In-deed,the adh gene was found to exhibit bidirectional functions inregulating waterlogging responses in the present study.It was not surprising that phenotypic analysis of over-expression of adh in rice [32],Arabidopsis [33],cotton [34],and other species gave a dif-ferent conclusion.3.3.ROS generation/scavenging-related genes were affected by waterloggingOne of the major sources of ROS in plants is a reaction mediated by NAPDH oxidase,which is responsible for the conversion of O 2to su-peroxide anion (O 2-),thereby leading to the production of hydrogen peroxide (H 2O 2).RBOH,a plant homolog of gp91phox in mammalian NAPDH oxidase,has an important role in ROS-mediated signaling,such as the defense response,programmed cell death,and develop-ment in plants [35–37].In maize,one waterlogging-induced up-regulated RBOH gene is involved in H 2O 2production and H 2O 2-in-duced cell death in root cortical cells [12].In the present study,the cucumber RBOH gene was signi ﬁcantly up-regulated during the waterlogging treatment,indicating that ROS accumulation was quick-ly increased (Table 2).The CsRBOH gene shows high homology to AtR-BOHC [38],Nicotiana benthamiana RBOH [39]and maize RBOH [12].Genes encoding some enzymes involved in ROS scavenging system were signi ﬁcantly changed by the waterlogging stress.The gene encoding POD was up-regulated whereas those encoding Cu/Zn SOD,CAT,and glutathione S-transferase were down-regulated under stress.Based on the results,it is possible that the acclimation to waterlogging may not be dependent on SOD,CAT,and glutathione S-transferase,but onPOD.Fig.5.Quantitative RT-PCR validation of tag-mapped genes from cucumber roots.TPM,transcription per million mapped reads.6X.-H.Qi et al./Genomics xxx (2012)xxx –xxx。