Identification of differentially-expressed genes of rice in overlapping responses to bacte

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Identifying differentially expressed gene catogory

• Based on a large body of past research, some information is known about many of the genes represented on a microarray.
• The information might include tissues in which a gene is known to be expressed, the biological process in which a gene’s protein is known to act, or other general or quite specific details about the function of the protein produced by a gene.
• For example, we might ask if genes that are associated with the Molecular Function term muscle alpha-actinin binding are affected by a treatment of interest.
• The GO terms in each ontology can be organized in a directed acyclic graph (DAG) where each node represents a term and arrows point from general terms to more specific terms.
Identifying Differentially Expressed Gene Categories
3/24/2011

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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.。

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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Low temperatures impact dormancy status,ﬂowering competence,and transcript proﬁles in crown buds of leafy spurgeMu¨nevver Dog ˘ramac ı•David P.Horvath •Wun S.Chao •Michael E.Foley •Michael J.Christoffers •James V.AndersonReceived:31August 2009/Accepted:24February 2010/Published online:26March 2010Óernment 2010Abstract Leafy spurge (Euphorbia esula )is an herba-ceous perennial weed that produces vegetatively from an abundance of underground adventitious buds.In this study,we report the effects of different environmental conditions on vegetative production and ﬂowering competence,and determine molecular mechanisms associated with dor-mancy transitions under controlled conditions.Reduction in temperature (27–10°C)and photoperiod (16–8h)over a 3-month period induced a para-to endo-dormant transition in crown buds.An additional 11weeks of cold (5–7°C)and short-photoperiod resulted in accelerated shoot growth from crown buds,and 99%ﬂoral competence when plants were returned to growth-promoting conditions.Exposure of paradormant plants to short-photoperiod and prolonged cold treatment alone had minimal affect on growth potential and resulted in *1%ﬂowering.Likewise,end-odormant crown buds without prolonged cold treatment displayed delayed shoot growth and *2%ﬂowering when returned to growth-promoting conditions.Transcriptome analysis revealed that 373and 260genes were differen-tially expressed (P \0.005)during para-to endo-dormantand endo-to eco-dormant transitions,respectively.Tran-scripts from ﬂower competent vs.non-ﬂower competent crown buds identiﬁed 607differentially expressed genes.Further,sub-network analysis identiﬁed expression targets and binding partners associated with circadian clock,dehydration/cold signaling,phosphorylation cascades,and response to abscisic acid,ethylene,gibberellic acid,and jasmonic acid,suggesting these central regulators affect well-deﬁned phases of dormancy and ﬂowering.Potential genetic pathways associated with these dormancy transi-tions and ﬂowering were used to develop a proposed conceptual model.Keywords Bud dormancy ÁEndodormancy ÁFlowering ÁLeafy spurge ÁPhotoperiod ÁTranscriptome ÁVernalization ÁWeedIntroductionIn temperate climates,seasonal environmental signals affect well-deﬁned phases of dormancy in weedy peren-nials.Understanding how these environmental signals inﬂuence molecular networks regulating speciﬁc phases of dormancy could identify new targets for manipulating vegetative reproduction and reduce economic costs to land managers worldwide.Among herbaceous perennial weeds,leafy spurge (Euphorbia esula L.)has emerged as a model to investigate well-deﬁned phases of dormancy based on ease of propagation,abundance of crown and root buds (collectively referred to as underground adventitious buds),and availability of molecular resources (Anderson et al.2007;Chao et al.2005).Leafy spurge is a wild ﬂower common to road sides and pasture lands in central and eastern Europe,but has become an invasive perennial weedInvited paper for the 4th International Symposium on Plant Dormancy and special issue of Plant Molecular Biology.Electronic supplementary material The online version of this article (doi:10.1007/s11103-010-9621-8)contains supplementary material,which is available to authorized users.M.Dog˘ramac ıÁM.J.Christoffers Department of Plant Sciences,North Dakota State University,166Loftsgard Hall,Fargo,ND 58105-6050,USAD.P.Horvath ÁW.S.Chao ÁM.E.Foley ÁJ.V.Anderson (&)Biosciences Research Laboratory,USDA-Agricultural Research Service,1605Albrecht Blvd.,Fargo,ND 58105-5674,USA e-mail:james.anderson@Plant Mol Biol (2010)73:207–226DOI 10.1007/s11103-010-9621-8infesting range,right-of-way lands,and non-cultivated areas in the great plains of the US and Canada(Anderson et al.2010).The perennial nature of this noxious weed is mainly attributed to vegetative production from the abun-dant crown and root buds which exhibit phases of para-, endo-,and eco-dormancy(deﬁned by Lang et al.1987) during summer,fall,and winter,respectively(Anderson et al.2005).Thus,regulation of arrested development in the shoot apical meristem of crown and root buds of leafy spurge during various phases of dormancy ensures a sur-vival mechanism for initiating new shoot growth,not only following seasonal environmental changes,but also fol-lowing conventional control measures.Photoperiod and temperature are environmental signals that inﬂuence cross-talk between cellular and molecular processes in buds to regulate vegetative growth and/or ﬂoral development(Chouard1960;Franklin2009;Horvath et al.2003;Putterill et al.2004;Rohde and Bhalerao2007; Searle et al.2006;Kobayashi and Weigel2007;Michaels 2009;Nozue and Maloof2006;Penﬁeld2008).Proteins normally associated withﬂowering in Arabidopsis,such as FT,CENL1,PHYA and CO(see Table1for abbrevia-tions),have also been associated with regulating growth cessation and endodormancy in poplar(Populus spp.) (Bo¨hlenius et al.2006;Eriksson2000;Ruonala et al. 2008).Although theseﬁndings suggest that signal trans-duction pathways regulatingﬂowering and endodormancy likely converge(Horvath2009),there are still many unresolved questions on how these pathways interact. Emerging evidence suggests the circadian clock likely plays some role in integrating environmental signals to seasonal transitions in bud dormancy(Anderson et al. 2010;Horvath2009;Michael et al.2003;Michaels2009; Penﬁeld2008).Circadian regulating genes involved in feed-back loops and output affectingﬂoral development and growth in the winter annual Arabidopsis have been extensively studied,including their impact on integrating photoperiod and cold-temperature regulatedﬂowering through action of the autonomous,gibberellic acid(GA), photoperiod,thermosensory,and vernalization pathways (Franklin2009;Henderson and Dean2004;Michaels2009; Putterill et al.2004;Searle et al.2006).However,far less is known about how environmental signals impact molecular networks affecting bud dormancy in weedy perennials.Induction of genes involved in circadian regulation have been observed in leafy spurge during the transition from para-to endo-dormancy(Horvath et al.2008),in grape (Vitis ssp.),chestnut(Castanea sativa),and poplar during the transition from endo-to eco-dormancy(Mathiason et al.2008;Ramos et al.2005;Ruttink et al.2007),and cold-temperatures are known to disrupt the circadian clock in chestnut(Ramos et al.2005).Additionally,interplay between components of the circadian clock,such as CCA1and LHY,andﬂoral-regulating MADS-box proteins such as FLC and SVP(Salathia et al.2006)are known to reg-ulateﬂoral integrators such as FT,SOC1,and LFY (Helliwell et al.2006;Lee et al.2007).Since the circadian clock integrates low temperature responses in both annuals and perennials(Harmer2009;Penﬁeld2008;Ramos et al. 2005;Samach and Wigge2005),it should not be surprising that temperature can affect both dormancy induction(Foley et al.2009;Heide2008;Kwolek and Woolhouse1982; Svendsen et al.2007)andﬂowering time(Edwards et al. 2006;Foley et al.2009;Fowler et al.2005;Gould et al. 2006;Lee et al.2007;Michaels2009;Rensing and Ruoff 2002).Further,evidence for cross-talk between low tem-perature and phytochrome signaling(Allona et al.2008; Benedict et al.2006;Heschel et al.2007;Kim et al.2002; Olsen et al.1997;Penﬁeld2008;Smith2000)may link temperature responses to FT and CENL1(orthologue of TFL1)expression and endodormancy induction.Cold-temperature appears to be the main environmental signal impacting endodormancy induction and release in leafy spurge crown buds(Foley et al.2009).These authors also demonstrated that endodormancy is likely a pre-requisite for fulﬁllment ofﬂoral competence in response to ﬂoral-inducing,long-term cold treatment(vernalization). Theseﬁndings indicated that endodormancy induction and ﬂoral competence in leafy spurge are likely photoperiod-independent.Further,Horvath et al.(2008)proposed that a particular MADS-box transcription factor related to SVP of Arabidopsis and DAM genes from peach(Bielenberg et al. 2004)are differentially regulated in crown buds ofﬁeld-grown leafy spurge during well-deﬁned phases of dor-mancy and may play a direct role in endodormancy maintenance through regulation of FT/TFL1.During the transition from para-to endo-dormancy,these authors also observed paralleled up-regulation of cold-hardening genes such as ICE1,DREB/CBFs and the type3LEA s which coincided with decreasing night time temperatures.Since cold-induced expression of some DREB/CBF-family members are gated by the circadian clock(Fowler et al. 2005),these particular regulators of transcription might also have some impact or overlap with pathways affecting dormancy transitions in response to low-temperature,par-ticularly in plants such as apple(Heide and Prestrud2005) or leafy spurge(Foley et al.2009)which rely primarily on cold-induced endodormancy.To enhance our understanding of molecular processes affecting dormancy andﬂowering in leafy spurge,and to build on the two studies conducted by Horvath et al.(2008) and Foley et al.(2009),the objectives of this study were to (1)determine how mimicking average seasonal conditions (photoperiod and temperature)affect dormancy status and ﬂowering competence in crown buds of leafy spurge,and (2)compare global transcript proﬁles obtained from crownTable1Abbreviations(Abr.)of genes mentioned in the manuscriptAbr.Gene ID Abr.Gene IDABI ABSCISIC ACID INSENSITIVE HUA ENHANCER OF AGAMOUS-42ACS10ACC SYNTHASE10HVA22A HVA22HOMOLOGUE AAFO ABNORMAL FLORAL ORGANS HXK3HEXOKINASE3AGL AGAMOUS-LIKE HY5ELONGATED HYPOCOTYL5AOC4ALLENE OXIDE CYCLASE4ICE INDUCER OF CBF EXPRESSIONAP APETALA JAG JAGGEDARF AUXIN RESPONSE FACTOR KAN2KANADI2ARR5ARABIDOPSIS RESPONSE REGULATOR5LEA LATE EMBRYOGENESIS ABUNDANTASK ARABIDOPSIS SKP1LFY LEAFYBAM1BARELY ANY MERISTEM1LHCB LIGHT HARVESTING CHLOROPHYLL A/B-BINDINGPROTEINBETA-BETA-AMYLASE LHY LATE ELONGATED HYPOCOTYLAMYbHLH BETA HELIX LOOP HELIX LKP LOV KELCH PROTEINCAB CHLOROPHYLL A/B BINDING PROTEIN LTP LIPID TRANSFER PROTEINCBF C-REPEAT BINDING FACTOR MAF MADS AFFECTING FLOWERINGCCA1CIRCADIAN CLOCK ASSOCIATED1MAPK MITOGEN ACTIVATED PROTEIN KINASECDK CYCLIN-DEPENDENT KINASE MBF1C MULTIPROTEIN BRIDGING FACTOR1CCENL1CENTRORADIALIS-LIKE1MCM2MINICHROMOSOME MAINTENANCE2CHS CHALCONE SYNTHASE MYC2JASMONATE INSENSITIVE1CIPK25CBL-INTERACTING PROTEIN KINASE25PA PROTEASE-ASSOCIATEDCKX3CYTOKININ OXIDASE3PAL1PHE AMMONIA LYASECO CONSTANS PEP1PERPETUAL FLOWERING1COI1PUTATIVE CORONATINE-INSENSITIVE1PHOT1PHOTOTROPIN1COP1CONSTITUTIVE PHOTOMORPHOGENIC1PHYA PHYTOCHROME ACOR COLD-REGULATED PIE1PHOTOPERIOD-INDEPENDENT EARLY FLOWERING1 CRY1CRYPTOCHROME1PIF3PHYTOCHROME INTERACTING FACTORCUL CULLIN PRXR1PEROXIDASE1CYCA1;1CYCLIN A1;1PSAH PHOTOSYSTEM I SUBUNIT HCYCP4;1CYCLIN P4;1PSAN PHOTOSYSTEM I REACTION CENTER SUBUNIT PSI-N DAM DORMANCY ASSOCIATED MADS RBCS-3B RuBisCO SMALL SUBUNIT3BDREB DEHYDRATION RESPONSE ELEMENT RCC1REGULATOR OF CHROMOSOME CONDENSATION EIN ETHYLENE INSENSITIVE RD19A RESPONSIVE TO DEHYDRATION19AELF EARLY FLOWERING RD22RESPONSIVE TO DESSICATION22EMB2279EMBRYO DEFECTIVE2279RGA1REPRESSOR OF GA1ERD EARLY RESPONSIVE TO DEHYDRATION RGL2REPRESSOR OF GA-LIKE2RPA1A REPLICATION PROTEIN A1AERF ETHYLENE RESPONSIVE ELEMENT BINDINGFACTORETR1ETHYLENE RESPONSE1SAUR_D SMALL AUXIN UP RNAF3H FLAVANONE3-HYDROXYLASE SCF SKP1/CULLIN/F-BOXFKF1FLAVIN-BINDING,KELCH REPEAT,F BOX1SCL7SCARECROW-LIKE7FLC FLOWERING LOCUS C SLY1SLEEPY1FRO1FROSTBITE1SMC STRUCTURAL MAINTENANCE OF CHROMOSOMES FRS11FAR1-RELATED SEQUENCE11SOC1SUPPRESSOR OF CONSTANS1FT FLOWER LOCUS T SPA1SUPPRESSOR OF PHYA1GA2OX GIBBERELLIN2-OXIDASE1SVP SHORT VEGETATIVE PHASESWITCH2/SUCROSE NONFERMENTABLE2GAI GIBBERELLIC ACID INSENSITIVE SWI2/SNF2GASA2GAST1PROTEIN HOMOLOG2TFL1TERMINAL FLOWER1buds to identify potential overlapping mechanisms regu-lating transitions in well-deﬁned phases of seasonal dor-mancy andﬂowering competence.The results of this study were further used to develop a conceptual working model. Materials and methodsPlant materialLeafy spurge plants were propagated from the genetically uniform biotype1984-ND001and maintained in a green-house as described by Anderson and Davis(2004).Prior to the start of each experiment,plants were acclimated in a growth chamber(PGR15Model of Conviron,Winnipeg, Canada)for1week at27°C,16h:8h day:night photope-riod.Six plants from each replicate were used to determine the vegetative growth andﬂowering potential of crown buds,and the remaining plants were used to collect crown buds for studying transcriptome proﬁles.All samples were collected between1100and1300Central Standard times to avoid diurnal variation.Controlled environmental treatmentsParadormant crown buds from3-month old greenhouse plants were collected after1week of acclimation under growth chamber conditions and used as controls.Induction of endodormancy in crown buds was accomplished by subjecting plants to a ramp down(RD)treatment consisting of a reduction in temperature of1.42°C week-1(27–10°C) and a decreasing photoperiod of40min week-1(16–8h light)for12weeks as previously established by Foley et al. (2009).These conditions mimic the average seasonal environmental conditions experienced in Fargo,ND (46°540N,96°480W)during the transition from para-to endo-dormancy(Anderson et al.2005).To induce a tran-sition of crown buds from endo-to eco-dormancy,plants subjected to the RD treatment were given prolonged cold treatment for11weeks at5–7°C,under constant 8h:16h day:night cycle;lightﬂuencies were approxi-mately250l mol m-2s-1.To compare effects of prolonged cold on dormancy status andﬂowering compe-tence in crown buds of plants exposed or not exposed to a RD treatment,plants were subjected directly to an exten-ded cold treatment for11weeks at5–7°C,as previously described,without a RD treatment.Vegetative growth analysisSix plants replicate-1treatment-1were randomly selected to determine the inﬂuence of environmental treatment on vegetative growth andﬂowering potential of leafy spurge crown buds.After each treatment,plants were moved back to the greenhouse,the aerial portion of the shoots were removed from plants at the soil surface,and growth of shoots from crown buds and the number thatﬂowered was measured weekly for4weeks.Vegetative growth rate was determined by measuring the tallest two shoots of each container,averaging the values,analyzing the data using the generalized linear mixed model(PROC GLIMMIX) procedure of SAS9.2(SAS Institute,Cary,NC,2008),and generating95%conﬁdence intervals for treatment by week means.Percentﬂowering data were arcsine transformed and the data were analyzed using the PROC GLIMMIX. Treatment means were separated with a Tukey–Kramer test and then back transformed to percentﬂowering.RNA extraction and microarray analysisAt the end of each treatment,crown bud samples were collected,ﬂash froze in liquid N2,and stored at-80°C until RNA extraction.Crown bud samples were ground to a ﬁne powder in liquid N2,and RNA was extracted according to the pine tree RNA extraction protocol(Chang et al. 1993).RNA quality and quantity was conﬁrmed by spec-trophotometry and agarose gel electrophoresis.For microarray hybridizations,labeled cDNAs were prepared from30l g of total RNA using the Alexa Fluor cDNA labeling kit(Invitrogen,Carlsbad,CA,USA) according to manufacturer’s beled cDNAs were hybridized to a custom made*23K element microarray that contained19,808unigenes from a leafy spurge EST database(Anderson et al.2007)and anTable1continuedAbr.Gene ID Abr.Gene IDGI GIGANTEA-LIKE TOC1TIMING OF CAB1GID1B GA-INSENSITIVE DWARF1B UBC28UBIQUITIN-CONJUGATING ENZYME28H3HISTONE H3UFD1UBIQUITIN FUSION DEGRADATION1HLL HUELLENLOS XTR8XYLOGLUCAN ENDOTRANSGLYCOSYLASE-RELATED8HSP HEAT SHOCK PROTEIN ZTL ZEITLUPEadditional4,129unigenes from a cassava EST database (Lokko et al.2007).Comparison of gene expression between samples was accomplished using a rolling circle dye swap hybridization scheme(Churchill2002)to provide every biological replicate with technical replicates;each of the four biological replicates included four technical replicates using two different dyes,resulting in a total of16technical replicates for each treatment.Microarray hybridization was visualized using a GenePix4000B scanner and probe intensities and background were quan-tiﬁed using GenePix 6.0software(Molecular Devices, Sunnyvale,California,USA).A quality control value of ‘‘1’’was assigned to all probes that had intensity values greater than2times the standard deviation over average of the negative control and empty probe intensities(after deletion of1%of the most intense negative/empty probe values).Microarray data analysisGeneMaths XT5.1software(Applied Maths Inc.,Austin, TX,USA)was used for statistical analysis and clustering of the dataset.Hybridization intensities were log2trans-formed,and arrays were centered and normalized against each other;technical replicates within each biological replicate were averaged.Arrays were grouped by treatment and ANOVA was used to identify genes that were differ-entially expressed(P\0.005).Principle component anal-ysis was used to identify clustering within the datasets.To identify genes with signiﬁcant differential expression between treatments,T-tests were used to determine P values that a given gene was differential in one treatment, but not differential between the other treatments.Expres-sion data are deposited at Gene Expression Omnibus (/geo/)as GEO dataset query GSE19217.MIPS analysis was performed toﬁnd the signiﬁcantly (P\0.05)over-represented functional categories(Fun-Cats)for differentially expressed genes(P\0.005or P\0.01)among the four environmental treatments; P\0.005cutoff value was chosen between any two given treatments,or up-and down-regulated genes between two environmental treatments(i.e.para-vs.endo-dormant buds).The ratios of the normalized log2transformed val-ues of each comparison were used to sort up and down regulated genes(P\0.005).Likely Arabidopsis Ortho-logues were used to identify the functional distribution of gene lists(Ruepp et al.2004)in the MIPS database (http://mips.helmholtz-muenchen.de/proj/funcatDB/search_ main_frame.html).Ariadne Pathway Studio Software-Resnet Plant Version 2.0(Ariadne Genomics Inc.,Rockville,MD,USA)was used for Gene Set Enrichment Analysis(GSEA)and Sub Network Analysis(SNA).GSEA identiﬁes groups of genes that share common biological function,chromosomal location,or regulation,based on Gene Ontology(GO).The SNA algorithm identiﬁes networks by highlighting over or under-represented ontologies based on published gene regulation hierarchies,protein:protein interactions,or pro-tein modiﬁcation targets.As part of the protocol,prior to GSEA and SNA,T-tests were performed between treatments(i.e.para vs.endo) using the entire microarray dataset,or sub-sets of data limited to either up-and down-regulated genes for given comparisons.Pathways were visualized using the‘‘Direct Force Layout’’option which arranges entities with a dis-tance from each other based on relationship and entities types.Real-time PCR analysisExpression analysis by RT-PCR was done using DNAse treated total RNA.Synthesis ofﬁrst strand cDNA was performed using5l g of total RNA and a SuperScript III First-Strand Synthesis System RT-PCR Kit(Invitrogen, Carlsbad,CA,USA)according to manufacturer’s protocol. Primers were designed using the Primer Select program of DNASTAR Lasergene8software to speciﬁcally amplify genes(Electronic Supplementary Material-1)identiﬁed as differentially expressed by microarray analysis,using sequences from the leafy spurge EST database(Anderson et al.2007).To optimize annealing and data collection temperatures,and the amount of cDNA to be used for each primer set,primers wereﬁrst tested on cDNAs for disas-sociation at50,55and60°C.Based on these tests,2–9l l of cDNA was added to the following reaction mix:Power SYBR Green PCR Master Mix(Applied Biosystems, Warrington,UK;10l l),forward and reverse primers (10pmol each)in aﬁnal volume of20l l.SYBR green and the endogenous ROX reference dye were used to determine relative threshold cycle(C T)value of ampliﬁcation as described by Chao(2008).For only DAM1and DAM22l l of cDNA were used in a reaction mix containing TaqMan Universal PCR Master Mix(Applied Biosystems, Branchburg,NJ,USA;25l l)and TaqMan Gene Expres-sion Assays(Applied Biosystems,Foster City,CA,USA;2.5l l)in aﬁnal volume of50l l with default settings (50°C annealing and60°C data collection temperature).All reactions were run in duplicate.Heat-maps of the RT-PCR and microarray analysis results,based on log2values,were created using Eisen Lab software,Cluster and TreeView(Stanford University, Stanford,CA,USA)as described by Eisen et al.(1998).To create a heat-map for microarray analysis the ratios from averaged replicates of each treatment vs.averaged para-dormant treatment(as the baseline)were used.Folddifferences were used to create heat-maps for RT-PCR analysis,again paradormant values were used as the baseline;results were further normalized against the con-trol gene(Horvath et al.2010),and separate heat-maps were created for the genes of interest that were not on the microarray chip using data generated by RT-PCR analysis. ResultsVegetative growth andﬂowering competenceManipulation of photoperiod and temperature(Fig.1) caused transitions in well-deﬁned phases of dormancy and different levels ofﬂowering competence in crown buds of leafy spurge(Fig.2).Crown buds rapidly initiated newshoot growth but displayed noﬂowering when released from paradormancy(Fig.2).Even after paradormant crown buds on intact plants received11weeks of a pro-longed cold treatment(5–7°C),initiation of new shoot growth was similar to paradormant controls,and minimal ﬂowering(1%)was observed.Initiation of new shoot growth from crown buds was signiﬁcantly delayed when plants were exposed to a RD treatment,andﬂowering was minimal(2%);thus plants were considered endodormant. However,endodormant crown buds on plants exposed to 11weeks of prolonged cold treatment had accelerated shoot growth and99%ﬂowering,after decapitation and return to growth-promoting conditions.These results sug-gest that prolonged cold treatment of endodormant crown buds results in a duel response that breaks endodormancy, while at the same time fulﬁlling the vernalization requirement.Since prolonged cold treatment of para-and endo-dormant crown buds produced differentﬂowering competence results,ecodormant buds will be termed either ﬂowering competent(FC)for RD?prolonged cold treat-ment,or non-ﬂowering competent(NFC)for prolonged cold treatment only.Transcriptome analysis:differentially expressed genes Ninety-eight percent of the probes on the microarrays pro-duced signal intensities signiﬁcantly above background,and principle component analysis of differentially expressed genes(Electronic Supplementary Material-2)indicated a close relationship of the replicated treatments.Table2 shows the number of differentially expressed genes identi-ﬁed among all four treatments or the three well-deﬁned phases of dormancy(para-,endo-and FC eco-dormancy) using different P values,and the percentage of these genes among the*23K unigenes studied.Across all four treat-ments,a high level of stringency(P\0.001)produced299 genes that were differentially expressed and,as expected, lowering the level of stringency increased the number of differentially expressed genes as indicated:834(P\0.005), 1,290(P\0.01),and3,636(P\0.05).Among the three well-deﬁned phased of dormancy150,407,670,and2,112 differentially expressed genes were identiﬁed at P\0.001, \0.005,\0.01,and\0.05,respectively.Based on this data, we chose a stringency of P\0.005as the cutoff value for further analysis,since decreasing the stringency to P\0.01 produced only a marginal change in the number of genes (Table2),or MIPS FunCats among the differentially expressed genes identiﬁed for all four environmental treat-ments(Electronic Supplementary Material-3).However,the subcategories related to Flowering in FunCats Development, and Organ Differentiation(P\0.01)identiﬁed additional genes not present among the differentially expressed genes at P\0.005(Electronic Supplementary Material-3,see MIPS all P\0.01),including putative Arabidopsis homologues involved with circadian rhythm(ELF3),chromatin-remod-eling(PIE1),CLAVATA signaling pathway(BAM1,PEPPER ),E3based ubiquitin protein-lygase complexes (CUL ),and known transcription factors or transcriptional regulators (HUA2,KAN2,JAG ,AFO ).Para-to endo-dormancy transitionAt P \0.005,the para-to endo-dormancy transition identiﬁed 373differentially expressed genes.The distri-bution and number of unique and common genes identiﬁed between comparisons of transitional phases is represented in Fig.3;genes within each group are available in Elec-tronic Supplementary Material-4.For the para-to endo-dormant transition,301of the 373differentially expressed genes were unique (group A)to this transitional phase of dormancy (*60were Euphorbia speciﬁc),and 178of these were up-regulated in endodormant crown buds (Electronic Supplementary Material-4).Among the differentially expressed genes unique to group A,up-regulated genes in endodormant buds included putative Arabidopsishomologues involved with chromosome condensation (RCC1),light photoreceptors (PHOT1,PHYA ),photosyn-thesis (CAB ,LHCB ,PSAH1,RBCS -3B ),abscisic acid (ABA)response (HVA22a ),and stress responses (LEA ,COR413-PM2,ERD4),and included down regulated genes involved in cell cycle (CDK ),GA-mediated pathways (GA2OX1,GID1B ),transcription (DEAD box RNA HELI-CASE )and numerous MYB -LIKE transcription factors.MIPS analysis of differentially expressed genes (P \0.005)up-or down-regulated between treatments highlighted several FunCats unique to speciﬁc phases of dormancy (Table 3,and Electronic Supplementary Mate-rial-3).Up-regulated transcripts over-represented in Fun-Cats Protein Synthesis,and Cell Fate were unique only to the para-to endo-dormancy transition.Other FunCats of interest containing up-regulated and over-represented transcripts included Cellular Communication/Signal Transduction,and Tissue Differentiation.Down-regulated transcripts over-represented among the FunCats included Systemic Interaction with the Environment,and Organ Differentiation;including a gene with similarity to a chromatin-remodeling protein of the SWI2/SNF2family (PIE1,At3g12810;see Electronic Supplementary Material-3,Para vs.Endo Up &Down-TAIR),which is involved in expression of the ﬂower repressor FLC in Arabidopsis (Noh and Amasino 2003).Gene Set Enrichment Analysis was also used to identify signiﬁcantly (P \0.05)over-represented transcripts among the 17,230elements on the array with putative Arabidopsis homologues (representing *72%of the total number of elements on our arrays),and approximately 40%of these Arabidopsis homologues have a known GO process.Based on these GO classiﬁcations (Electronic Supplementary Material-5),some GO subcategories were unique for spe-ciﬁc phases of dormancy (Table 4).Among the over-represented transcripts of up-regulated genes,unique GO sub-categories of interest during the para-to endo-dormancy transition include Chlorophyll Binding,Dephosphorylation,Fruit Development,Photomorphogenesis,Proteolysis,and Responses to Cold,Hypoxia,and Light.Among theTable 2The number of differentially expressed (DE)genes obtained using different P values,and the percentage (%)of these genes from the *23K unigenes studied among all four environmental treatments,among the three phases of dormancy (para,endo,FC),and between individual transitional phases of dormancy,or compar-ison of FC and NFC crown buds P valueAll four treatments Three treatments Para/endo/FC Para to endo Endo to FC FC vs.NFC DE genes%DE genes %DE genes %DE genes %DE genes %\0.001299 1.21500.61110.5790.31450.6\0.005834 3.5407 1.7373 1.6260 1.1607 2.5\0.011,290 5.4670 2.8628 2.6458 1.91,143 4.8\0.053,63615.22,1128.82,2309.31,8127.64,28917.9Para vs.Endo (373)Endo vs. FC Eco (260)FC Eco vs. NFC Eco (607)301*A43*D9*G 20*F172*B542*C36*EFig.3Venn diagram showing the distribution of the differentially expressed (P \0.005)unique and common genes between all treatments.*A–*G:The list of genes for each group are listed in Electronic Supplementary Material-4。

考研英语基因鉴定及其存在的问题原文

考研英语基因鉴定及其存在的问题原文全文共3篇示例，供读者参考篇1DNA Profiling and Its Existing IssuesDNA profiling, also known as genetic fingerprinting, has revolutionized the field of forensic science and criminal investigations. This technique involves analyzing specific regions of an individual's DNA to create a unique genetic profile, which can be used to identify individuals or establish biological relationships. While DNA profiling has proven to be a powerful tool in solving crimes and exonerating the innocent, it has also raised several ethical, legal, and social concerns that warrant careful consideration.One of the primary issues surrounding DNA profiling is privacy and civil liberties. The collection and storage of genetic information raise concerns about potential misuse or unauthorized access. Critics argue that DNA databases could be exploited by governments or other entities for purposes beyond law enforcement, such as genetic discrimination or surveillance. There are also fears that DNA profiles could be used to revealsensitive information about an individual's health, ancestry, or behavioral traits, violating their right to privacy.Another concern is the accuracy and reliability of DNA evidence. While DNA profiling is considered highly accurate, there is always a possibility of errors or contamination during the collection, handling, or analysis of samples. Such errors could lead to wrongful convictions or the exoneration of guilty individuals. Additionally, the interpretation of DNA evidence can be subjective and may be influenced by cognitive biases or inadequate training of forensic experts.The issue of racial and ethnic bias in DNA profiling is also a matter of concern. Some studies have suggested that certain ethnic groups may be disproportionately represented in DNA databases due to factors such as socioeconomic status, policing practices, or historical discrimination. This could lead to increased scrutiny and potentially unjust treatment of certain communities, further exacerbating existing disparities in the criminal justice system.Another ethical consideration is the use of DNA profiling in familial searching, where law enforcement officers search DNA databases for partial matches to identify potential relatives of a suspect. While this technique has been successful in solving coldcases, it raises questions about the privacy rights of individuals who are not directly involved in a criminal investigation. Critics argue that familial searching could lead to the genetic surveillance of entire families or communities without their consent.Furthermore, the retention and destruction policies for DNA samples and profiles vary across jurisdictions, raising concerns about the potential for long-term storage and misuse of genetic information. Some argue that DNA samples and profiles should be destroyed after a certain period or upon acquittal, while others believe that retaining such information could be valuable for future investigations or exonerations.Despite these issues, DNA profiling has undoubtedly played a crucial role in solving crimes, identifying missing persons, and exonerating the wrongfully convicted. However, it is imperative that the use of this technology be accompanied by robust legal and ethical frameworks to address the concerns mentioned above.One potential solution is the implementation of strict privacy and data protection laws, ensuring that DNA profiles are used solely for lawful purposes and that individuals' genetic information is adequately safeguarded. Additionally, ongoingtraining and oversight of forensic professionals, as well as the development of standardized protocols and quality control measures, could help mitigate the risk of errors and biases in the interpretation of DNA evidence.Addressing racial and ethnic biases in DNA databases may require a multifaceted approach, including comprehensive reviews of policing practices, criminal justice reforms, and efforts to increase diversity and representation within law enforcement agencies and forensic laboratories.Regarding familial searching, some experts suggest implementing strict guidelines and oversight mechanisms to ensure that this technique is used only in exceptional circumstances and with appropriate safeguards to protect the privacy rights of individuals and their families.Lastly, clear policies and regulations surrounding the retention and destruction of DNA samples and profiles should be established, striking a balance between preserving valuable evidence for future investigations and protecting individual privacy rights.In conclusion, while DNA profiling has proven to be a powerful tool in the pursuit of justice, it is essential to address the ethical, legal, and social issues surrounding its use. Byengaging in open and informed discourse, implementing robust legal and ethical frameworks, and fostering transparency and accountability, we can harness the benefits of this technology while mitigating its potential risks and upholding the principles of fairness, privacy, and civil liberties.篇2DNA Identification and Its Existing ProblemsAs a graduate student pursuing my studies in molecular biology, I have become increasingly fascinated by the field of DNA identification and its numerous applications. From forensic investigations to paternity testing and even genealogical research, the ability to analyze and interpret an individual's genetic makeup has revolutionized various domains. However, despite its immense potential, DNA identification is not without its challenges and ethical conundrums, which warrant careful consideration.At its core, DNA identification relies on the fundamental principle that each individual's genetic code is unique, barring identical twins. This uniqueness arises from the vast number of possible combinations of nucleotide sequences that make up our DNA. By analyzing specific regions of an individual's DNA,known as Short Tandem Repeats (STRs), scientists can create a genetic profile that serves as a molecular fingerprint.The application of DNA identification in forensic science has been nothing short of groundbreaking. Traditional methods of evidence collection and analysis often fell short in cases where physical evidence was scarce or contaminated. However, the advent of DNA profiling has provided investigators with a powerful tool to link suspects to crime scenes or exonerate the wrongly accused. The ability to extract and analyze minute traces of biological material, such as hair, skin cells, or bodily fluids, has significantly improved the accuracy and reliability of forensic investigations.Another significant application of DNA identification lies in the realm of paternity testing. Historically, establishing paternal relationships relied heavily on circumstantial evidence and presumptions, leading to potential inaccuracies and emotional turmoil. DNA testing has revolutionized this process by providing a scientifically robust method for determining biological relationships. This has not only facilitated the resolution of disputes but has also played a crucial role in ensuring the well-being of children and upholding their fundamental rights.Furthermore, DNA identification has opened up new avenues in genealogical research and ancestry tracing. By comparing an individual's genetic profile to databases containing DNA samples from various populations and ethnic groups, researchers can unravel intricate family histories and shed light on migration patterns and evolutionary trajectories. This knowledge has not only satisfied personal curiosities but has also contributed to our understanding of human diversity and the complex tapestry of our shared ancestry.Despite these remarkable achievements, DNA identification is not without its fair share of challenges and controversies. One of the primary concerns revolves around the issue of privacy and the potential misuse of genetic information. As our genetic code contains a wealth of personal data, including predispositions to certain diseases and traits, there is a justified fear that this information could be exploited for discriminatory purposes, such as in employment or insurance decisions.Moreover, the collection, storage, and handling of DNA samples raise significant ethical and legal questions. While strict protocols and guidelines exist to ensure the proper management of genetic data, instances of improper handling or unauthorizedaccess can have severe consequences, ranging from breaches of privacy to potential miscarriages of justice.Another contentious issue lies in the interpretation of DNA evidence itself. While DNA profiling is generally considered highly reliable, there have been instances where factors such as contamination, degradation, or human error have led to erroneous results. Additionally, the statistical interpretation of DNA evidence can be complex, and differing methodologies may yield varying probabilities, potentially influencing legal outcomes.Furthermore, the use of DNA identification in the criminal justice system has sparked debates regarding its potential for perpetuating systemic biases. Concerns have been raised about the disproportionate representation of certain ethnic and socioeconomic groups in DNA databases, which could lead to increased scrutiny and potential profiling.Despite these challenges, the field of DNA identification continues to evolve, driven by advances in technology and a deeper understanding of genetic principles. Ongoing research efforts are focused on improving the accuracy and efficiency of DNA analysis techniques, as well as expanding the range of applications.One promising area of development is the use ofNext-Generation Sequencing (NGS) technologies, which allow for the rapid and cost-effective analysis of entire genomes. This could potentially enhance the resolution and discriminatory power of DNA profiling, facilitating more precise identifications and shedding light on complex biological relationships.Additionally, the integration of DNA identification with other cutting-edge technologies, such as machine learning and artificial intelligence, holds significant promise. These advanced computational techniques could assist in the analysis and interpretation of vast amounts of genetic data, potentially uncovering previously undetected patterns and relationships.As we navigate the intricate landscape of DNA identification, it is imperative that ethical considerations remain at the forefront. Robust governance frameworks, rigorous scientific standards, and inclusive societal dialogues are essential to ensure that the benefits of this powerful technology are maximized while mitigating potential risks and addressing legitimate concerns.In conclusion, DNA identification has revolutionized various fields, from forensics to paternity testing and genealogical research. Its ability to unlock the secrets encoded within our genetic makeup has provided invaluable insights and facilitatedthe pursuit of justice, familial connections, and self-discovery. However, as with any powerful technology, DNA identification is not without its challenges and ethical dilemmas. By addressing these concerns through ongoing research, responsible governance, and inclusive discussions, we can harness the full potential of this transformative technology while upholding the principles of privacy, fairness, and human rights.篇3DNA Identification and Its Existing ProblemsAs a student pursuing a degree in molecular biology, I can't help but be fascinated by the incredible potential of DNA identification technology. From solving criminal cases to establishing paternity and even uncovering long-lost ancestral roots, the ability to analyze and interpret an individual's unique genetic blueprint has revolutionized various fields. However, like any powerful tool, DNA identification is not without its challenges and controversies.DNA, or deoxyribonucleic acid, is the hereditary material present in nearly all living organisms, encoding the instructions for their development, functioning, and reproduction. Every person's DNA is unique, with the exception of identical twins,making it an invaluable tool for identification purposes. The process of DNA identification, also known as DNA profiling or DNA typing, involves extracting and analyzing specific regions of an individual's DNA, known as loci, to create a unique genetic profile.The applications of DNA identification are far-reaching and have had a profound impact on various aspects of society. In the realm of criminal justice, DNA evidence has played a pivotal role in solving countless cases, exonerating the innocent, and identifying perpetrators with unprecedented accuracy. By comparing DNA samples collected from crime scenes with those in forensic databases, law enforcement agencies can establish crucial links or eliminate suspects, leading to more reliable convictions or acquittals.Beyond its forensic applications, DNA identification has also been instrumental in resolving paternity disputes, enabling individuals to establish biological relationships with certainty. This technology has brought closure to many families and provided a sense of identity to those who previously lacked it. Additionally, genealogical DNA testing has gained immense popularity, allowing people to trace their ancestral roots anduncover fascinating details about their ethnic origins and family histories.While the benefits of DNA identification are undeniable, there are several ethical, legal, and social concerns that need to be addressed. One of the primary issues is the potential for misuse or abuse of genetic information. DNA profiles can reveal sensitive personal information, such as an individual's predisposition to certain diseases or inherited traits, raising privacy concerns. There is a risk that this information could be used for discriminatory purposes in areas like employment, insurance, or social interactions, leading to potential infringements on civil liberties.Another significant challenge lies in the handling and storage of DNA data. As DNA databases continue to grow, concerns arise regarding data security, potential breaches, and the mishandling of sensitive genetic information. There is a need for robust protocols and strict regulations to ensure the proper collection, storage, and access to DNA data, safeguarding individual privacy while still allowing for legitimate use by authorized entities.Furthermore, the reliability and accuracy of DNA identification techniques have been called into question incertain cases. While the technology itself is highly accurate, issues can arise due to human error, contamination of samples, or improper handling and interpretation of data. These concerns highlight the importance of adhering to stringent quality control measures and ensuring that those involved in DNA analysis are properly trained and follow established protocols.Additionally, the use of DNA identification in various contexts raises ethical and legal questions. For instance, the practice of familial searching, where law enforcement agencies search DNA databases for partial matches to identify potential relatives of a suspect, has sparked debates around privacy rights and the boundaries of acceptable investigative techniques.Moreover, the application of DNA identification in areas such as immigration enforcement and targeted surveillance of certain communities has raised concerns about discrimination and potential violations of civil liberties.As a student exploring this fascinating field, I believe it is essential to strike a delicate balance between harnessing the power of DNA identification technology and addressing the legitimate concerns surrounding its use. Comprehensive legal frameworks and robust ethical guidelines must be established togovern the collection, storage, and utilization of genetic data, ensuring that individual privacy and civil liberties are protected.Ongoing research and dialogue among scientists, policymakers, legal experts, and the public are crucial to navigate the complex issues surrounding DNA identification. Ethical considerations, such as informed consent, data security, and non-discrimination, should be at the forefront of discussions. Additionally, education and public awareness campaigns can play a vital role in fostering a better understanding of the implications and responsible use of this technology.While DNA identification has undoubtedly revolutionized various aspects of our society, it is essential to approach it with caution and a deep appreciation for its potential consequences. By addressing the existing challenges and concerns, we can harness the incredible potential of this technology while upholding the fundamental rights and dignity of individuals.In conclusion, DNA identification is a powerful tool that has transformed numerous fields, from criminal justice to genealogy. However, its widespread application and the sensitive nature of genetic information demand a vigilant approach. By actively engaging in discussions, promoting ethical practices, and continuously refining legal frameworks, we can ensure that DNAidentification technology is used responsibly and for the betterment of society as a whole.。

UL 认证证书说明书

Certificate Number 20171128-E135493Report ReferenceE135493-A36-ULIssue Date2017-NOVEMBER-28Bruce Mahrenholz, Director North American Certification ProgramUL LLC Issued to:VICOR CORP25 FRONTAGE RDANDOVER, MA 01810-5424 UNITED STATESThis is to certify thatrepresentative samples ofPower Supplies for Information Technology Equipment Including Electrical Business Equipment See Addendum.Have been investigated by UL in accordance with the Standard(s) indicated on this Certificate.Standard(s) for Safety:UL 60950-1, (Information Technology Equipment - Safety - Part 1: General Requirements)CAN/CSA C22.2 No. 60950-1-07, (Information Technology Equipment - Safety - Part 1: General Requirements) Additional Information:See the UL Online Certifications Directory at /database for additional informationOnly those products bearing the UL Certification Mark should be considered as being covered by UL's Certification and Follow-Up Service.The UL Recognized Component Mark generally consists of the manufacturer’s identification and catalog number, model number or other product designat ion as specified under “Marking” for the particularRecognition as published in the appropriate UL Directory. As a supplementary means of identifying products that have been produced under UL’s Component Recognition Program, UL’s Recognized Component Mark: , may be used in conjunction with the required Recognized Marks. The Recognized Component Mark is required when specified in the UL Directory preceding the recognitions or under “Markings” for the individual recognitions.Recognized components are incomplete in certain constructional features or restricted in performancecapabilities and are intended for use as components of complete equipment submitted for investigation rather than for direct separate installation in the field. The final acceptance of the component is dependent upon its installation and use in complete equipment submitted to UL LLC.Look for the UL Certification Mark on the product.Certificate Number 20171128-E135493Report ReferenceE135493-A36-ULIssue Date2017-NOVEMBER-28Bruce Mahrenholz, Director North American Certification ProgramUL LLC This is to certify that representative samples of the product as specified on this certificate were tested according to the current UL requirements.DC-DC ConverterModel: Low Voltage 3814 VIA BCM and VIA NBM Series(see Additional Information or Miscellaneous Enclosure 7-01 for Model nomenclature)UL TEST REPORT AND PROCEDUREThis is to certify that representative samples of the products covered by this Test Report have been investigated in accordance with the above referenced Standards. The products have been found to comply with the requirements covering the category and the products are judged to be eligible for Follow-Up Service under the indicated Test Procedure. The manufacturer is authorized to use the UL Mark on such products which comply with this Test Report and any other applicable requirements of UL LLC ('UL') in accordance with the Follow-Up Service Agreement. Only those products which properly bear the UL Mark are considered as being covered by UL's Follow-Up Service under the indicated Test Procedure.The applicant is authorized to reproduce the referenced Test Report provided it is reproduced in its entirety.UL authorizes the applicant to reproduce the latest pages of the referenced Test Report consisting of the first page of the Specific Technical Criteria through to the end of the Conditions of Acceptability.Any information and documentation involving UL Mark services are provided on behalf of UL LLC (UL) or any authorized licensee of UL. Prepared by: James C. Powley Reviewed by: Lesley GreenLow Voltage VIA BCM and VIA NBM Model Matrix: AAA3814cddewwxxyzz Example: BCM3814V60E15A3T01。

tpo61三篇托福阅读TOEFL原文译文题目答案背景知识

tpo61三篇托福阅读TOEFL原文译文题目答案背景知识阅读-1 (2)原文 (2)译文 (5)题目 (7)答案 (13)背景知识 (15)阅读-2 (18)原文 (19)译文 (22)题目 (24)答案 (32)背景知识 (34)阅读-3 (39)原文 (39)译文 (42)题目 (45)答案 (53)背景知识 (54)阅读-1原文Physical Properties of Minerals①A mineral is a naturally occurring solid formed by inorganic processes. Since the internal structure and chemical composition of a mineral are difficult to determine without the aid of sophisticated tests and apparatus , the more easily recognized physical properties are used in identification.②Most people think of a crystal as a rare commodity, when in fact most inorganic solid objects are composed of crystals. The reason for this misconception is that most crystals do not exhibit their crystal form: the external form of a mineral that reflects the orderly internal arrangement of its atoms. Whenever a mineral forms without space restrictions, individual crystals with well-formed crystal faces will develop. Some crystals, such as those of the mineral quartz, have a very distinctive crystal form that can be helpful in identification. However, most of the time, crystal growth is interrupted because of competition for space, resulting in an intergrown mass of crystals, none of which exhibits crystal form.③Although color is an obvious feature of a mineral, it is often anunreliable diagnostic property. Slight impurities in the common mineral quartz, for example, give it a variety of colors, including pink, purple (amethyst), white, and even black. When a mineral, such as quartz, exhibits a variety of colors, it is said to possess exotic coloration. Exotic coloration is usually caused by the inclusion of impurities, such as foreign ions, in the crystalline structure. Other minerals —for example, sulfur, which is yellow, and malachite, which is bright green —are said to have inherent coloration because their color is a consequence of their chemical makeup and does not vary significantly.④Streak is the color of a mineral in its powdered form and is obtained by rubbing a mineral across a plate of unglazed porcelain. Whereas the color of a mineral often varies from sample to sample, the streak usually does not and is therefore the more reliable property.⑤Luster is the appearance or quality of light reflected from the surface of a mineral. Minerals that have the appearance of metals, regardless of color, are said to have a metallic luster. Minerals with a nonmetallic luster are described by various adjectives, including vitreous (glassy) pearly, silky, resinous, and earthy (dull).⑥One of the most useful diagnostic properties of a mineral is hardness, the resistance of a mineral to abrasion or scratching. This property is determined by rubbing a mineral of unknown hardness against one ofknown hardness, or vice versa. A numerical value can be obtained by using Mohs' scale of hardness, which consists of ten minerals arranged in order from talc, the softest, at number one, to diamond, the hardest, at number ten. Any mineral of unknown hardness can be compared with these or with other objects of known hardness. For example, a fingernail has a hardness of 2.5, a copper penny 5, and a piece of glass 5.5. The mineral gypsum, which has a hardness of two, can be easily scratched with your fingernail. On the other hand, the mineral calcite which has a hardness of three, will scratch your fingernail but will not scratch glass. Quartz, the hardest of the common minerals, will scratch a glass plate.⑦The tendency of a mineral to break along planes of weak bonding is called cleavage. Minerals that possess cleavage are identified by the smooth, flat surfaces produced when the mineral is broken. The simplest type of cleavage is exhibited by the micas. Because the micas have excellent cleavage in one direction, they break to form thin, flat sheets. Some minerals have several cleavage planes, which produce smooth surfaces when broken, while others exhibit poor cleavage, and still others exhibit no cleavage at all. When minerals break evenly in more than one direction, cleavage is described by the number of planes exhibited and the angles at which they meet. Cleavage should not be confused with crystal form. When a mineral exhibits cleavage, itwill break into pieces that have the same configuration as the original sample does. By contrast, quartz crystals do not have cleavage, and if broken, would shatter into shapes that do not resemble each other or the original crystals. Minerals that do not exhibit cleavage are said to fracture when broken. Some break into pieces with smooth curved surfaces resembling broken glass. Others break into splinters or fibers, but most fracture irregularly.译文矿物的物理性质①矿物质是由无机过程形成的天然固体。

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。

【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.。

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。

Profiling of differentially expressed genes in hepatopancreas of white shrimp

Pro ﬁling of differentially expressed genes in hepatopancreas of white shrimp (Litopenaeus vannamei )exposed to long-term low salinity stressWeihua Gao a ,Beiping Tan b ,⁎,Kangsen Mai a ,Shuyan Chi b ,Hongyu Liu b ,Xiaohui Dong b ,Qihui Yang ba The Key Laboratory of Mariculture (Ministry Education of China),Ocean University of China,Qingdao 266003,PR ChinabLaboratory of Aquatic Animal Nutrition and Feed,College of Fisheries,Guangdong Ocean University,Zhanjiang 524025,PR Chinaa b s t r a c ta r t i c l e i n f o Article history:Received 14April 2012Received in revised form 6August 2012Accepted 17August 2012Available online 24August 2012Keywords:Litopenaeus vannamei Hepatopancreas StressSuppression subtractive hybridization mRNA expressionThe Paci ﬁc white shrimp,Litopenaeus vannamei ,is a euryhaline crustacean capable of tolerating a wide range of ambient salinity (0.5–40psu).To investigate the effect of long-term low salinity stress on gene ex-pression in the hepatopancreas in shrimp,we performed suppression subtractive hybridization (SSH)in ju-venile L.vannamei exposed to long-term low salinity.The shrimp (initial body weight,0.27±0.01g)were cultured at salinity 2psu and salinity 30psu for 56days.We then constructed forward and reverse subtrac-tive cDNA libraries.We used bioinformatics tools and vector screening to select a total of 200(80from for-ward,120from reverse)randomly selected clones over 100nucleotides in length for further analysis.Nineteen contigs and 54singletons were generated from a total of 73consensuses.The consensuses,upon a sequence homology search using BLASTX (NCBI),revealed that 24.66%(18/73)of them had no sig-ni ﬁcant match to reported sequences in the database,suggesting that they had not previously been found and that they were probably associated with stress-regulated functions.The remaining 75.34%(55/73)of the consensuses encoded proteins were matched to a wide range of functions including immune-related functions,metabolism,ribosomal activity,transfer activity,and apoptosis.The most common group in these SSH libraries was immune-related proteins and enzymes (11/17).Quantitative RT-PCR results con-ﬁrmed that the relative expression of 5differentially expressed genes encoding hemocyanin,chitinase,ecdysteroid-regulated protein,trypsin and chymotrypsin 1was decreased 2-,1.45-,11.11-,1.33-and 1.54-fold,respectively,in the reverse library.This subtractive cDNA library provides a basis for the study of the genetic response of shrimp to environmental stress.©2012Elsevier B.V.All rights reserved.1.IntroductionSalinity is a fundamental environmental factor.Under intensive cul-ture,variations in salinity may break homeostasis and lead to signi ﬁcant stress in penaeid shrimp (Lee and Wickins,1992).Several studies have discussed the molecular and cellular responses of ion-transport pro-teins and osmoregulatory enzymes in the gills of crustaceans and telosts under acute salinity stress (Chung and Lin,2006;de la Vega et al.,2007;Deane and Woo,2004;Henry et al.,2002,2003,2006;Scott et al.,2004,2005).However,little research has been performed on the mechanism of the physiological response to stress of shrimp under long-term low salinity conditions.Litopenaeus vannamei is distributed on the Paci ﬁc coast of the Americas from the northern Mexico to northern Peru.It is capable of tol-erating a wide range of ambient salinity (0.5–40psu)(Castille and Lawrence,1981).Recently,L.vannamei has become a promising cultivar for inland low salinity farming in much of the world because of diseases and water pollution in coastal areas.However,there are three prominentproblems in long-term low salinity cultivation:slow growth (Li et al.,2007;Samocha et al.,1998),low survival rate (Li et al.,2007),and high susceptibility to pathogens (Wang and Chen,2005,2006).These prob-lems have restricted the large-scale popularization of inland desalting cultivation.The SSH technique is an effective and PCR-based method,which has been widely used in identifying differentially expressed genes (Diatachenko et al.,1996).In the present study,the SSH technique was used to construct forward and reverse subtracted cDNA libraries of hepatopancreatic genes in shrimp cultured at low salinity and in sea water.The hepatopancreas,known as the midgut gland or diges-tive gland,is not only the principal organ for digestion,metabolism,absorption,and storage of nutrients in decapod crustaceans (Gibson and Barker,1979),also plays a key role in stress and immune responses (Jiang,2009).For this reason,we considered the gene ex-pression in the hepatopancreas to be a good indicator of gene re-sponses to stress in this species.The gene expression pro ﬁle within the hepatopancreas may help us identify the genes involved in the shrimp anti-stress system.The relative expression of 5genes in a re-verse library was con ﬁrmed by quantitative real-time polymerase chain reaction (qRT-PCR)and compared to gene expression in theAquaculture 364–365(2012)186–191⁎Corresponding author.Tel./fax:+867592362262.E-mail address:bptan@ (B.Tan).0044-8486/$–see front matter ©2012Elsevier B.V.All rights reserved./10.1016/j.aquaculture.2012.08.024Contents lists available at SciVerse ScienceDirectAquaculturej ou 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 /a q u a -o n l i n eshrimp exposed to short-term(24h)low salinity stress(30→2psu) in order to investigate the differences in the mechanisms of the shrimp response to long-term and short-term low salinity stress.2.Materials and methods2.1.Experimental design and daily managementTen-day-old L.vannamei postlarvae(PL10)were obtained from the Haixingnong Biotechnology Limited Company,Guangdong province, China.Natural seawater(South Sea)was used at a temperature of 27.5±1.5°C and initial salinity of30±0.5psu.The shrimp were placed in three cement pools and some of them were allowed to accli-mate slowly to the target salinity of2psu:salinity was reduced by 1–2psu per day by the addition of tap water that had been aerated for at least24h before addition.After one week of acclimation,the intermolt and healthy juvenile shrimp(mean initial weight,0.27±0.01g)cultured at normal salinity(30psu,SW)and low salinity (2psu,LLS)were selected randomly and distributed into6indoor polyethylene-aquaria(310L)equipped withﬂowing water under a 14h/10h light/dark cycle.Three replicates were performed per salin-ity level over the course of a56d feeding trial.During the experimen-tal period,the shrimp were fed self-formulation feed(42.34%crude protein,8.02%crude lipid,8.75%ash,and9.23%moisture)four times a day(6:00,11:00,17:30and22:30)(Davis and Arnold,1993;Liu and Lawrence,1997).The water was aerated and40%of it was changed daily and replaced with adjusted seawater.Based on the amount of feed remained on the following day,daily rations were ad-justed to approximate a feed input in slight excess of satiation.The uneaten feed was removed daily with a siphon tube.2.2.SamplingAt the end of the56d experimental period,all shrimps from2 groups(LLS,SW)were deprived of feed for24h and weighted. Then16shrimps were taken from each replication of SW and placed directly into salinity2psu for short-term salinity stress testing.These shrimp made up the short-term low salinity treatment group(SLS). Nine individual shrimp(3from each replicate)from each treatment group were anesthetized on ice,and the hepatopancreases were removed into1.5ml Eppendorf centrifuge tube(RNase-free),imme-diately frozen in liquid nitrogen,and then stored at−80°C for subse-quent analysis.2.3.RNA extraction,mRNA isolation and SSHTotal RNA from each sample was extracted using Unizol reagent (GENEray Biotechnology,China)according to the manufacturer's recommendation and total RNA concentration was detected by Pharmacia GENEQUANT II(Eppendorf,Germany)ultraviolet spec-trometer.Poly(A)+mRNA was isolated from total RNA using the Oligotex mini mRNA kit(Qiagen,Germany)according to the man-ufacturer's recommended protocols.SSH libraries were constructed from the hepatopancreases of L.vannamei using a PCR-Select TM cDNA Subtraction Kit(Clontech,U.S.)according to the manufacturer's instructions.The cDNA of LLS was used as the tester to construct a for-ward subtraction library,and the cDNA of SW was used as the tester to construct a reverse subtractive library.Then tester and driver cDNA populations were digested with Rsa I,and divided into two equal sub-populations.Each was ligated with the adaptor1and adaptor2R separately,then hybridized with an excess of driver cDNA at68°C for 8h.After theﬁrst hybridization,the two products(adaptor1and adap-tor2R)were mixed together and immediately hybridized again over-night with an excess amount of fresh denatured driver cDNA at68°C. Two rounds of hybridization generated a normalized population of tester-speciﬁc cDNA with different adaptors on each end.Then,differentially expressed sequences were selectively ampliﬁed by two rounds of PCR using the PCR Primer1and Nester primer1,Nester prim-er2R,respectively.2.4.cDNA cloning,sequencing and homology analysisThe subtracted cDNA products were puriﬁed(Axygen PCR Clean-up Kit,U.S.)and cloned into an Easy Digestion T-vector(pED-T)(Sinobio, Shanghai).They were then transformed into E.coli DH5αcompetent cells(Takara,Tokyo).Two hundred positive clones(80from forward SSH library,120from reverse SSH library)were picked and commercial-ly sequenced at both5′and3′extremities by Shanghai Jierui Bio-technique Corporation.These sequences were edited to remove vector and other redundant sequences using VecScreen programs of NCBI(). They were then assembled into the longest possible consensus se-quences using the CAP3online program(http://deepc2.psi.iastate. edu/aat/cap/cap.html).The consensus sequence of each cluster was used as a query sequence to search the non-redundant protein(nr) and non-redundant nucleotide(nt)databases at the National Center for Biotechnology Information(NCBI)using BlastX and BlastN(http:// /BLAST).(Altschul et al.,1997).Only homologous sequences over100nu-cleotides in length that showed an E-values less than1×10−6were considered for further analysis.The assembled sequences were classi-ﬁed using previously published documents.2.5.Conﬁrmation of gene expression by qRT-RCRThe relative expression of5genes in reverse library(hemocyanin, chitinase,ecdysteroid-regulated protein,trypsin and chymotrypsin1) were conﬁrmed by qRT-PCR and compared with the shrimp induced by the short-term low salinity stress(30→2psu)at the same time with the ABI PRISM®7500Real Time Thermal Cycler(Applied Bio-systems)using the SYBR®PrimeScript®RT-PCR Kit(TaKaRa)according to the manufacturer's instructions.β-actin,which had previously been shown not to change with different treatments,was chosen as a refer-ence gene(Tine et al.,2008).The speciﬁc primers of qRT-PCR were syn-thesized by Shanghai Jierui Biotechnique Corporation(Table1).qRT-PCR reaction mixture included2μl cDNA template,12.5μl 2×SYBR Premix Ex Taq,0.5μl forward and reverse primers,respec-tively,and9.5μl ddH2O.The thermal proﬁle consisted of an initial step at95°C for2min and45cycles of denaturing at94°C for15s, annealing at60°C for15s,and elongation at72°C for20s.The spec-iﬁcity of PCR ampliﬁcation was conﬁrmed using a melting curve pro-gram.A dissociation curve was generated95°C for15s and59°C for 15s.Then the temperature was slowly elevated to95°C for5min. Three parallels were to be made by each replication of3treatments, results are shown as changes in relative expression normalized toTable1Primers for quantitative RT-PCR of hemocyanin,chitinase,ecdysteroid-regulated protein,trypsin,chymotrypsin1andβ-actin genes of white shrimp,L.vannamei.Target gene GenBanknumberForward/reverseSequence(5’→3’)Hemocyanin X82502.1F ACGCAAGTCCACGGAATCTTCR GAGTCGGCATCACCATCAGTC Chitinase AY576684.1F GGTCTCTACGCTCATCCTCTCR TCATCCACTACGGTCCATTCC Ecdysteroid-regulatedproteinDQ398569.1F CTCTCATCGCCATCGCTTCAACR GCAATCACCCACGGTCAAGTC Trypsin X86369.1F ACGGTCATCCTCTCCAAGR GTCCTCAATGTCGCTCTG Chymotrypsin1X66415.1F TACTTCTGCGGCGGTTCCR TGGCTGGCTTCGTTCTGGβ-actin AF300705.2F AATCGTTCGTGACATCAAGGAGR TTGTAGGTGGTCTCGTGGATG187W.Gao et al./Aquaculture364–365(2012)186–191the reference gene(β-actin)using the2−ΔΔCt method described by Livak and Schmittgen(2001).The qRT-PCR amplication efﬁciency for each of theﬁve serial dilutions(1/4,1/16,1/64,1/128,1/1024) of the cDNAs(LLS,SLS,and SW)and the RT-PCR efﬁciency(E)were both calculated from the given slope of the standard curve using to the equation E=10(−1/slope).2.6.Statistical analysisThe indexes used for the assessment of growth performance were calculated as follows:Weight gain(%)=100×(W t−W0)/W0(W0is the initial weight and W t is theﬁnal weight)Survival rate(%)=100×(number of shrimp at the end of the experiment)/(number of shrimp at the beginning of the experiment)Speciﬁc growth rate(%)=100×(ln W t−ln W0)/t(W0is the ini-tial weight and W t is theﬁnal weight,t is the breeding time)All data were subjected to one-way analysis of variance(ANOVA) and correlation analysis where appropriate using SPSS17.0for Win-dows.The differences between the means were tested by Tukey's multi-ple range test and the results were presented as mean±S.E.M.(standard error of the mean).P b0.05was considered as statistically signiﬁcant. 3.Results3.1.Effects of salinity on the growth performance and survival rateAfter56days,there was signiﬁcant difference in both growth per-formance and survival rate between SW and LLS(100%vs.79.2%; P b0.05).Weight gain and speciﬁc growth rate(SGR)were signiﬁ-cantly lower in LLS than in SW(P b0.01).There were no mortalities in either the SW or SLS tanks during the feeding period(Table2). 3.2.Suppression subtractive hybridizationA subtracted cDNA library rich in genes differentially expressed dur-ing long-term low salinity stress was generated using SSH in L.vannamei. The quantity and quality of the RNA is crucial for the qualiﬁed SSH librar-y.OD260/280ratio of total RNA at LLS and SW was2.01and1.96respec-tively.RNA integrity was conﬁrmed by agarose gel electrophoresis.After two rounds of subtractive hybridization and suppressive PCR,forward and reverse subtraction libraries were constructed.Subtraction efﬁciency was evaluated by the constitutively expressed geneβ-actin.The amount ofβ-actin transcript was hardly detectable in the subtracted library rela-tive to that in the unsubtracted sample,suggesting that the SSH proce-dure successfully suppressed cDNAs common to the normal and acute-low-salinity stress shrimp.3.3.Analysis and clariﬁcation of expressed sequence tags(ESTs)Two hundred randomly selected positive clones,including80 from the forward library and120from the reverse library,were se-quenced at both5′and3′extremities.This yielded179high-quality ESTs,including64from the forward library and115from the reverse library.The ESTs of the forward library were assembled into26con-sensus sequences,which consisted of19singletons and7contigs, and the ESTs of the reverse library were assembled into47consensus sequences,each consisting of35singletons and12contigs(Table3).The homology search revealed that69.23%(18/26)of forward li-brary and78.72%(37/47)of reverse library unigenes showed signiﬁ-cant homology to known protein sequences in the GenBank database, while24.66%(18/73)had no similarity to any known protein se-quence in the public database.The matched unigenes represented 17different genes in5categories,as determined using the primary functions of their encoded proteins(Tables4and5).In the forward SSH library(putatively up-regulated in long-term hyposmotic L.vannamei),18out of26ESTs showed signiﬁcant homolo-gy to protein sequences in the GenBank database.Analysis of the genes in the forward library showed that7identiﬁed genes were up-regulated under hyposmotic conditions.In the reverse SSH library(putatively down-regulated in long-term hyposmotic L.vannamei),37out of47 ESTs showed signiﬁcant homology to protein sequences in the GenBank database.Analysis of the genes in the reverse library showed that10 identiﬁed genes were down-regulated under long-term hyposmotic conditions.Among these17genes(7from the forward library,10 from the reverse library),11were found to encode immune-related proteins and enzymes,including hemocyanin,ecdysteroid-regulated protein,C-type lectin1,cathepsin L,chitinase,cathepsin C,zinc protein-ase mpc1,trypsin,trypsin gene2,chymotrypsin1and lysozyme.3.4.Validation of the selected differentially expressed genesTo conﬁrm the differential expression of genes identiﬁed from the SSH libraries and the differences between short-term and long-term hyposmotic conditions,5immune-related genes(hemocyanin,chitinase, ecdysteroid regulated protein,trypsin and chymotrypsin1)from the re-verse library were selected for qRT-PCR-based evaluation of their relative expression levels(Fig.1).Theseﬁve representative genes were selected because they would indicate whether the SSH library had been con-structed successfully.Results conﬁrmed that these5target genes were indeed signiﬁcant-ly down-regulated in the hepatopancreases of LLS.In LLS,the relative expression levels of these5genes(encoding hemocyanin,chitinase, ecdysteroid-regulated protein,trypsin,and chymotrypsin1)were signiﬁcantly different from that in SW.Expression decreased2,1.45, 11.11, 1.33,and 1.54-fold,respectively.For the SLS,the2genes encoding chitinase and ecdysteroid-regulated protein showed signiﬁ-cant up-regulation relative to SW.They increased2.47-and3.07-fold, respectively.The other3genes,those encoding hemocyanin,trypsin, and chymotrypsin1,showed no signiﬁcant difference.Table2Growth performance and survival rate of L.vannamei at different treatments.Growth performance indexes SalinitySW LLS SLSInitial weight(g)0.26±0.060.28±0.06–Final weight(g)8.66±0.54b 5.51±0.27a–Weight gain(%)3188.08±211.90b1893.34±103.16a–Speciﬁc growth rate(%·day−1)6.23±0.11b 5.34±0.09a–Survival Rate(%)100.00±0.00b79.17±6.29a100.00±0.00 Note:All values represent the mean±S.E.M.(n=3),different letters in the same column series indicate signiﬁcant in the test(p b0.05;Tukey's test),same letters indicate no difference.Table3Sequences composition of forward and reverse SSH libraries.Items Forward library Reverse library TotalNumber of clones sequences80120200 Valid number of cloned sequences64115179 Unigenes264773 Contigs71219 Singletons193554 Matched genes183755 Unmatched genes81018188W.Gao et al./Aquaculture364–365(2012)186–1914.DiscussionThe Paci ﬁc white shrimp is a euryhaline crustacean,which can toler-ate of a wide range of salinity levels,from 0.5to 40psu (Castille and Lawrence,1981).However,previous studies reached different conclu-sions regarding the effects of long-term exposure to low salinity on growth performance and survival of L.vannamei .In the present study,signi ﬁcant differences in growth and survival were observed between 2and 30psu.In contrast,Ogle et al.found no difference in the growth of 22-day-old L.vannamei postlarvae grown at 2psu and those grown at 16psu (1992).However,the experiment lasted only 4weeks.Bray et al.found no difference in survival of 2g L.vannamei juveniles be-tween 5and 40psu but did not test lower salinities (1994).Samocha et al.observed no difference in growth or survival between 2and 8psu but they worked with 2g L.vannamei juveniles rather than postlarvae (1998).The research performed by Laramore et al.showed that L.vannamei postlarvae and juveniles can be grown successfully at 4psu.Salinities below 4psu negatively affected both postlarvae survival and growth,but juveniles were able to survive as well at 2psu as at 30psu (2001).According to these studies,the different developmental stages of L.vannamei showed different levels of adaptability to low sa-linity.The level of stress and physiological adaptation of penaeids to different salinities can be monitored through their osmoregulatory ca-pacity.Osmoregulatory capacity is correlated with size,nutritional con-ditions,and developmental stage (Lignot et al.,2000).The present work used SSH to construct differential cDNA libraries in the shrimp hepatopancreas in order to investigate the transcription levels of shrimp exposed to long-term extreme low salinity.The large numbers of immune-related genes (11/17)in the SSH libraries and down-regulated genes in the reverse library (8/11)provide strong circumstantial evidence for the molecular basis of the close connec-tion between the environment and immune capacity in shrimp (Tables 4and 5),which may provide direction for further research into about the effects of the low salt breeding on the immune performanceof L.vannamei .In this category,hemocyanin,chitinase,ecdysteroid reg-ulated protein,trypsin and chymotrypsin 1were the most frequently encountered clones and could be affected by infection with viruses or vibrioes.4.1.Defense-related protein and enzymesHemocyanin is a multifunctional protein.It is not only the oxygen-carrier in the circulatory systems of many crustaceans but it also pos-sesses multiple functions in the immune system (Adachi et al.,2005;Decker and Jaenicke,2004).Previous studies have shown that the he-mocyanin gene is up-regulated during osmic stress and down-regulated after hyperthermic and hypoxic stress in Penaeus monodon hemocytes (de la Vega et al.,2007).The down-regulation of hemocyanin in response to hypoxia had been previously reported in the hepatopancreas of the blue crab Callinectes sapidus using SSH (Brouwer et al.,2004).Trypsin and chymotrypsin are alkaline proteolytic enzymes present in the shrimp hepatopancreas.They belong to the serine protease family.In insects,chymotrypsin-like serine protease plays an important role in immune de-fense against bacteria and rhabdovius (de Morais Guedes et al.,2005;Finnerty et al.,1999).Trypsin related with the activation of crustaceans prophenoloxidase and involved in hydrolysis of lysine and arginine to perform the digestion function (Kishimura et al.,2007;Lai et al.,2005;Thomas-Guyon et al.,2009).In the present study,the expression of hemo-cyanin,trypsin and chymotrypsin in L.vannamei did not change after short-term (24h)stress.This may be connected to stress recovery.The above 3genes were down-regulated after long-term (56days)salinity stress,which may be related to the immunodepression.The underlying mechanism merits further study.Chitinase is an enzyme that allows crustaceans to digest chitinous food.It plays an important role in ensuring that the shells of the crustaceans molt smoothly (Funke and Spindler,1989).And the ecdysteroid-regulated protein gene is associated with ecdy-physiology (Mykles,2011).Previous studies have indicated that chitinase andTable 4SSH cDNA clones showing signi ﬁcant similarity to sequences in the databases (putatively up-regulation).GeneBank accession no.Clone numberHomologous to genes in databaseSpeciesLength of the longest fragment(bp)/Clones E-value (%identity)Immune-related protein and enzyme ADO65981.153/58Cathepsin CEriocheir sinensis268/28.53E −53(72%)ABD65301.15/16Zinc proteinase mpc1Litopenaeus vannamei 461/20(84.5%)ABD65298.117/59/110Lysozyme(i)Litopenaeus vannamei 986/38.67E −117(94%)Protein synthesis and processing FJ943451.171/83/150Ribosomal protein 28S Farfantepenaeus duorarum 744/30(99%)AF124597.122/64Ribosomal protein 18S Penaeus vannamei 821/20(99%)DQ85891315/78/116Ribosomal protein L3Spodoptera frugiperda 654/3 6.00E −88(86.2%)Apoptosis-related protein FJ766846.1128/173/117Qm proteinPenaeus monodon350/32.07E −144(81.1%)Table 5SSH cDNA clones showing signi ﬁcant similarity to sequences in the databases (putatively down-regulation).GeneBank accession no.Clone numberHomologous to genes in databaseSpeciesLength of the longest fragment(bp)/Clones E-Value (%identity)Immune-related protein and enzyme CAB859658/11/12/27/42/49/65/112/123HemocyaninLitopenaeus vannamei 1162/90(76.2%)DQ3985693/48/116/134Ecdysteroid-regulated protein Litopenaeus vannamei 440/4 3.95E-180(99%)ADW08726.150/83C-type lectin 1Litopenaeus vannamei 466/20(90%)CAA5844166Cathepsin L Eriocheir sinensis332/19.00E-136(99%)AAT80625.12/4/25/47/148Chitinase Litopenaeus vannamei 644/50(81.13%)CAA60129.11/9/120TrypsinLitopenaeus vannamei 1198/30(90%)CAA75309.113/28Trypsin gene 2Litopenaeus vannamei 654/20(91.75%)CAA47046.118/19/43/73/89/118Chymotrypsin 1Penaeus vannamei 455/68.46E-170(84.25%)metabolism-related enzymes CAB65552.234/111Amylase,exons2-10Penaeus vannamei 223/2 1.67E-25(84.5%)Transfer proteinNP_001037378.132/40/70Sterol carrier protein-XBombyx mori532/37.21E-140(74.1%)189W.Gao et al./Aquaculture 364–365(2012)186–191ecdysteroid-regulated protein play an important role in the innate im-mune response (Badariotti et al.,2007;Liu et al.,2007;Pan et al.,2005;Wang et al.,2007;Zhao et al.,2007).In this study,the transcrip-tion level of chitinase and ecdysteroid-regulated protein conditions were down-regulated under long-term low salinity conditions but up-regulated under short-term low salinity conditions.The difference in the level of chitinase and ecdysteroid-regulated protein gene expres-sion between short-term and long-term hyposmotic stress suggested different response mechanisms.Yeh et al.thought that the reason for the up-regulation of immune factors at the transcription level was the result of innate immune protection following acute salinity stress (35→25psu)in L.vannamei (2010).So the up-regulation of chitinase and ecdysteroid-regulated protein gene expression was the result of innate immune protection under short-term hyposmotic stress condi-tions.Down-regulation was found to be the result of immuodepression under long-term hyposmotic stress conditions.4.2.Metabolism-related enzymesAmylase is an important glucose glycosidase enzyme.It plays a crucial role in carbohydrate digestion.Research performed by Jiang et al showed that the up-regulation of α-amylase could trigger the in-crease of glucose to maintain the ATP supply during hypoxia stress (2009).These SSH libraries included many energy metabolism-related genes under short-term stress conditions,such as hypoxia,os-motic stress,and hyperthermic stress (de la Vega et al.,2007;Tine et al.,2008).Little was found in our libraries.One reason for this may be that too few clones were used.The other may be associated with en-ergy consumption required for the shrimp to adapt to variations in salinity at the onset of salinity stress.This shows that long-term and short-term extreme low salinity stress involve different response mechanisms.4.3.Other proteinsZinc proteinase Mpc 1belongs to family of metalloendopeptidases that have many different roles in biological systems,like connective tissue remodeling and removal of signal sequences from nascent pro-teins (Gearing et al.,1994).In general,zinc proteinase is involved in animal immune responses and in activating and degrading signal molecules (Dumermuth et al.,1991).Previous studies have shownthat the zinc proteinase gene is up-regulated during hypoxic stress in Fenneropenaeus chinensi hepatopancreases (Jiang et al.,2009).Zhao et al.evaluated the up-regulation of zinc proteinase in L.vannamei in WSSV-resistant and WSSV-susceptible shrimp (2007).Results indi-cated that zinc proteinase played a crucial role not only in immune re-sponse but also in stress response.Lectins are important pattern recognition receptors.They are in-volved in innate immunity.They bind speci ﬁcally to carbohydrate com-ponents on the surfaces of microbes and activate immune responses to eliminate invading pathogens (Vasta et al.,2004).To date,C-type lectins from shrimp have been reported to be involved in antibacterial,antifun-gal,and antiviral processes.Fclectin showed up-regulated expression after challenge by bacteria,lipopolysaccharide (LPS)and WSSV (Liu et al.,2007).Apoptotic protein was identi ﬁed in the subtractive library,indicat-ing that programmed cell death might also be involved in hyposmotic stress.The apoptotic protein found in our library was QM protein,a putative tumor suppressor that has been shown to associate with ap-optosis (Wiens et al.,1999).In response to viral infection,QM protein may be up regulated in virus-resistant shrimp,leading to increased apoptosis (Pan et al.,2005).In summary,while the precise regulatory mechanism of long-term hyposmotic stress requires further research,current results show that SSH analysis was successfully employed as a discovery approach to help determine the direction that such studies should take.Our study provides a ﬁrm basis for further investigation into the function-al characterization of stress-regulated genes.The signi ﬁcant differ-ences in transcription-level expression of 5genes in response to long-term and short-term salinity stress showed that different re-sponse mechanisms are activated under different types of salinity stress conditions.The transcription levels of two immune-related genes encoding ecdysteroid-regulated protein and chitinase showed protective up-regulation under acute salinity stress conditions,but they were down-regulated under long-term low salinity stress condi-tions because of immunodepression.Cloning and functional studies of some novel genes identi ﬁed in the present study are in progress.AcknowledgementsThe authors would like to extend their thanks to those who have taken the time to review this paper as well and to those who helped support this research.This work was supported by grants No.NSFC 30871928from the National Natural Science Foundation of China and 201003020from the Special Fund for Agro-scienti ﬁc Research in the Public Interest,and by GDUPS (2011)from Guangdong Prov-ince Universities and Colleges Pearl River Scholar Funded Scheme.ReferencesAdachi,K.,Endo,H.,Watanabe,T.,Nishioka,T.,Hirata,T.,2005.Hemocyanin in the exo-skeleton of crustaceans:enzymatic properties and immunolocalization.Pigment Cell Research 18(2),136–143.Altschul,S.F.,Madden,T.L.,Schaffer,A.A.,Zhang,J.,Zhang,Z.,Miller,W.,Lipman,D.J.,1997.Gapped BLAST and PSI-BLAST:a new generation of protein database search programs.Nucleic Acids Research 25(17),3389–3402.Badariotti,F.,Thuau,R.,Lelong,C.,Dubos,M.P.,Favrel,P.,2007.Characterization of anatypical family 18chitinase from the oyster Crassostrea gigas :evidence for a role in early development and immunity.Developmental and Comparative Immunolo-gy 31(6),559–570.Bray,W.A.,Lawrence,A.L.,Leung-Trujillo,J.R.,1994.The effect of salinity on growthand survival of penaeus vannamei ,with observation on the interaction of IHHN virus and salinity.Aquculture 122,133–146.Brouwer,M.,Larkin,P.,Brown-Peterson,N.,King,C.,Manning,S.,Denslow,N.,2004.Ef-fects of hypoxia on gene and protein expression in the blue crab,Callinectes sapidus .Marine Environmental Research 58(2–5),787–792.Castille,F.L.,Lawrence,A.L.,1981.The effect of salinity on the osmotic,sodium andchloride concentrations in the hemolymph of euryhaline shrimp of the genus parative Biochemistry and Physiology.Part A,Molecular &Integra-tive Physiology 68(1),75–80.a aaa baa b ca bca b aa ba0.511.522.5SW LLSSLS R e l a t i v e m R N A e x p r e s s i o nβ-actin hemocyaninchitinaseecdysteroid-regulated proteintrypsin chymotrypsin Fig.1.Quantitative RT-PCR analysis for 5genes from hepatopancreas in L.vannamei after different salinity treatment.Different letters in the same column series indicate signi ﬁcant in the test (p b 0.05;Tukey's test),same letters indicate no difference.190W.Gao et al./Aquaculture 364–365(2012)186–191。

新目标九年级英语unit4whatwouldyoudo

02 Overview of Unit Content
Text Analysis
Text structure
Identify the main idea, supporting details, and conclusion of the text.
Theme and motifs
Analyze the underlying themes and recurrent motifs in the text.
words and expressions
Vocabulary
Expand your vocabulary by learning new words and expressions from the text.
Word relationships
Identify and analyze the relationships between words, such as synonyms, antonyms, homonyms, and polysemes.
Reading exercises
实践应用
通过阅读练习，学生可以巩固所学知识，提高阅读速度和理解能力。这些练习包括选择题、填空题、简答题等。
05 Oral expression
Oral expression skills
Speaking clearly
The ability to speak clearly and distinctly, with proper pronunciation and enunciation, is essential for effective oral communication.
New Goal 9th Grade English Unit4 What Would You Do?

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。

IDENTIFICATION OF DIFFERENTIALLY METHYLATED MULTI

专利名称：IDENTIFICATION OF DIFFERENTIALLYMETHYLATED MULTIPLE DRUG RESISTANCELOCI发明人：DUFFY, Hao-Peng, Xu,SHAN, Ji-dong,YUAN, Li-ming,BUDMAN, Daniel,CALABRO, Anthony 申请号：US1999027630申请日：19991118公开号：WO00/029625P1公开日：20000525专利内容由知识产权出版社提供摘要：The present invention provides genomic loci which are hypermethylated and differentially expressed in drug resistant cells compared to non-drug resistant cells. These genomic loci are homologous to the rab6 locus but map to a different chromosomal position. The present invention also provides nucleic acids isolated from these genomic loci by Methyl-Differential Display (MDD) methods, including genomic DNAs and cDNAs. The present nucleic acids are useful as probes for detecting mutations and the methylation patterns of the newly identified genomic loci, and of homologous nucleic acids. Nucleic acids of the present invention are also useful for detecting expression of mRNA from herein identified genes, for measuring expression of those and homologous genes sequences, and for determining suitability of therapeutic treatment. The disclosed nucleic acids and their homologs are useful for inhibition of multiple drug resistance. Cells are disclosed which are useful for identification or modulators of multidrug resistance.申请人：NORTH SHORE - LONG ISLAND JEWISH RESEARCH INSTITUTE地址：350 Community Drive Manhasset, NY 11030 US 国籍：US代理机构：TSEVDOS, Estelle, J.更多信息请下载全文后查看。

人类骨骼肌胞体（HSkM-S）产品说明书

For research use only.Life Technologies Corporation ∙ 5791 Van Allen Way ∙ Carlsbad ∙ CA 92008 ∙ Tel: 800.955.6288 ∙ HSkM-SCat. no. A12555Product DescriptionHSkM-S (Cat. no. A12555) are normal human skeletal myoblasts developed to undergo highly efficientdifferentiation directly following plating of cryopreserved cells. The cells are: ∙ Tested for mycoplasma, bacteria, yeast, or other fungi, Hepatitis B, Hepatitis C, and HIV-1 viruses.∙Performance tested: guaranteed to differentiate >50% following 48 hours of incubation.∙ Guaranteed to be ≥70% viable (as determined by trypanblue) Each vial of HSkM-S contain sufficient number of cells to fully seed ¼ of a single multi-well dish (ranging in format from 6-well to 384-well).Storage and StabilityCryopreserved HSkM are shipped frozen on dry ice. If the cells are not to be used immediately, store the vial in the vapor phase of a liquid nitrogen freezer. Wearing protective eyewear, gloves, and a laboratory coat, remove the vial from its shipping container and place it immediately in the liquid nitrogen freezer. Although the viability of cryopreserved cells decreases with time in storage, useful cultures can usually be established even after 2 years of storage at liquid nitrogen temperatures.Caution: Although cryopreserved cells from Invitrogen have been tested for the presence of various hazardous agents, diagnostic tests are not necessarily 100% accurate. In addition, human cells may harbor other known or unknown agents or organisms which could be harmful to your health or cause fatal illness. Treat all human cells as potential pathogens. Wear protective clothing and eyewear. Practice appropriate disposal techniques for potentially pathogenic or biohazardous materials.Initiating Cultures from Cryopreserved CellsPREPARE DIFFERENTIATION MEDIUMGibco ®HSkM Differentiation Medium (DM) consists ofD-MEM Basal Medium (Cat. no. 11885-084) supplemented with 2% Horse Serum (Cat. no. 16050-130). To prepare500 mL bottle of Differentiation Medium, add 10 mL of Horse Serum to a 500 mL of D-MEM. Mix gently and date the bottle. Store at 4°C protected from light. Use theDifferentiation Medium within 30 days of preparation.THAW AND SEED CELLS1. Add 10 mL of Differentiation Medium to a sterile50-mL conical tube.2. Remove a vial of HSkM from liquid nitrogen storage,taking care to protect hands and eyes.3. Dip the lower half of the vial into a 37°C water bath tothaw. 4. When the contents of the vial have thawed, wipe theoutside of the vial with disinfecting solution and move to the cell culture hood. 5. Open the vial and transfer cell suspension to conicaltube containing Differentiation Medium. 6. Rinse the cryovial once with approximately 1 mL ofDifferentiation Medium and combine with cells in the conical tube. 7. Centrifuge for 5 minutes at 180 x g at roomtemperature.8. Aspirate the medium, taking care not to disturb pellet. 9. Add 6.25 mL of fresh Differentiation Medium andresuspend the pellet by gently pipetting up and down (typically 4–6 times with a 10 mL pipette). 10. Add an appropriate volume of cell suspension perwell based on the Multiwell Plate Seeding Guide below. 11. Return the cells to a humidified, 37 C, 5%CO 2incubator.12. Incubate the cells for 48 hours to enable rapiddifferentiation.For optimal performance, we highly recommend seeding cells recovered from cryopreservation at the densities described in the Seeding Guide table above.For research use only.Life Technologies Corporation ∙ 5791 Van Allen Way ∙ Carlsbad ∙ CA 92008 ∙ Tel: 800.955.6288 ∙ E-mail:***************************Quick Start Guide for HSkM-SIntended UseCryopreserved HSkM are intended for use by researchers investigating the molecular and biochemical bases of various normal and disease processes.This product is for research use only. Not intended for human or animal therapeutic or diagnostic use.Limited Use Label License No. 5: Invitrogen TechnologyThe purchase of this product conveys to the buyer the non-transferable right to use the purchased amount of the product and components of the product in research conducted by the buyer (whether the buyer is an academic or for-profit entity). The buyer cannot sell or otherwise transfer (a) this product (b) its components or (c) materials made using this product or its components to a third party or otherwise use this product or its components or materials made using this product or its components for Commercial Purposes. The buyer may transferinformation or materials made through the use of this product to a scientific collaborator, provided that such transfer is not for any Commercial Purpose, and that such collaborator agrees in writing (a) not to transfer such materials to any third party, and (b) to use such transferred materials and/or information solely for research and not for Commercial Purposes. Commercial Purposes means any activity by a party for consideration and may include, but is not limited to: (1) use of the product or its components in manufacturing; (2) use of the product or its components to provide a service, information, or data; (3) use of the product or its components for therapeutic, diagnostic or prophylactic purposes; or (4) resale of the product or its components, whether or not such product or its components are resold for use in research. For products that are subject to multiple limited use label licenses, the terms of the most restrictive limited use label license shall control. Life Technologies Corporation will not assert a claim against the buyer of infringement of patents owned or controlled by Life Technologies Corporation which cover this product based upon the manufacture, use or sale of a therapeutic, clinical diagnostic, vaccine or prophylactic product developed in research by the buyer in which this product or its components was employed, provided that neither this product nor any of its components was used in the manufacture of such product. If the purchaser is not willing to accept the limitations of this limited usestatement, Life Technologies is willing to accept return of the product with a full refund. For information about purchasing a license to use this product or the technology embedded in it for any use other than for research use please contact Out Licensing, Life Technologies, 5791 Van Allen Way, Carlsbad, California 92008; Phone (760) 603-7200 or e-mail: ************************* .The trademarks mentioned herein are the property of Life Technologies Corporation or their respective owners.©2010 Life Technologies Corporation. All rights reserved.For research use only. Not intended for any animal or human therapeutic or diagnostic use.。

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