Identification of differentially expressed genes associated with bud dormancy release in tree pe

Gene expression profiling of Sinapis alba leaves under drought stress and rewatering growth conditions with Illumina deep sequencingCai-Hua Dong •Chen Li •Xiao-Hong Yan •Shun-Mou Huang •Jin-Yong Huang •Li-Jun Wang •Rui-Xing Guo •Guang-Yuan Lu •Xue-Kun Zhang •Xiao-Ping Fang •Wen-Hui WeiReceived:20June 2011/Accepted:17December 2011ÓSpringer Science+Business Media B.V.2011Abstract Sinapis alba has many desirable agronomic traits including tolerance to drought.In this investigation,we performed the genome-wide transcriptional profiling of S.alba leaves under drought stress and rewatering growth conditions in an attempt to identify candidate genes involved in drought tolerance,using the Illumina deep sequencing technology.The comparative analysis revealed numerous changes in gene expression level attributable to the drought stress,which resulted in the down-regulation of 309genes and the up-regulation of 248genes.Gene ontology analysis revealed that the differentially expressedgenes were mainly involved in cell division and catalytic and metabolic processes.Our results provide useful infor-mation for further analyses of the drought stress tolerance in Sinapis ,and will facilitate molecular breeding for Brassica crop plants.Keywords Sinapis alba ÁDrought stress ÁIllumina sequencing ÁGene expression ÁDrought tolerance genesIntroductionDrought is a meteorological occurrence in practice which displays zero rainfall for a long time,it firstly causes the depletion of moisture in soil,and finally works the decrease of water potential of plant tissues for water deficit [1].In the light of the agricultural point of view,its operating definition would be the insufficient of water availability from the soil during the life cycle of crop plants,which restricts a full exertion of genetic potential of the plants.At present,it is one of the grand restrictive factors in agri-cultural production by inhibiting crop plants reaching the theoretical maximum yield genetically determined.Drought stress is one of the most common stress factors decreasing crop output.Plants changes adaptively in cell morphology,gene expression,physiological and bio-chemical metabolisms to mitigate the damage caused by drought stress,and form a variety of drought stress adap-tation in aspects of growth habit and physiological and biochemical habits during long-term interaction with the environment and during evolution [2].As plants experience drought,many drought stress response genes are induced and a large number of specific proteins are produced to regulate physiological and biochemical and metabolic changes of plants cooperatively.Cai-Hua Dong,Chen Li and Xiao-Hong Yan contributed equally to this work.Electronic supplementary material The online version of this article (doi:10.1007/s11033-011-1395-9)contains supplementary material,which is available to authorized users.C.-H.Dong ÁX.-H.Yan ÁS.-M.Huang ÁL.-J.Wang ÁR.-X.Guo ÁG.-Y.Lu ÁX.-K.Zhang (&)ÁX.-P.Fang (&)ÁW.-H.Wei (&)Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences,Key Laboratory of Oil Crop Biology and Genetic Breeding of the Ministry of Agriculture,Wuhan 430062,China e-mail:whwei@ X.-P.Fange-mail:xpfang@ X.-K.Zhange-mail:zhangxk@C.LiCollege of Food Science and Technology,Agricultural University of Hebei,Baoding 071001,China J.-Y.HuangDepartment of Bioengineering,Zhengzhou University,Zhengzhou 450001,ChinaMol Biol RepDOI 10.1007/s11033-011-1395-9The mechanisms of drought tolerance in plants have already been studied at the gene level.Gene expression profiles of plant tissuesfluctuated under drought stress,this stress response made plants obtain drought tolerance.The genes related to drought tolerance can be grouped into two categories,thefirst one encode the special functional pro-teins directly involving drought tolerance,including pro-tective factors,osmotic adjustment proteins,ion tunnel proteins,ion transport proteins,oxidation-resistant pro-teins,etc.The second one encode those regulatory proteins, such as transcription factors,protein kinases,protein phos-phatases,calmodulin-binding proteins,etc.The expression levels of these genes are up-regulated or down-regulated during drought stress,which regulates the intra-and intercellular environments,and plants exhibit the trait tolerant to drought[3].With Arabidopsis[4],rice[5,6]and other plant genome sequencing completed,the research of genes of plants have entered the era of functional genomics,which studies not only the structure and function of genes,but also the temporal and spatial expression of plant genes and the regulation network.For a comprehensive understanding of the genetic basis of drought tolerance,exploring drought tolerance genes,cultivating resistant and water saving species,it is significant to discuss the sorts of genes induced by drought stress by methods of molecular biol-ogy,to construct the expression profiling of drought-related genes,to acknowledge the metabolism mechanism of plants under drought stress condition from the overall level.Arabidopsis thaliana is the model species used to study the mechanisms of drought tolerance and used to clone the genes that might code for the mechanisms leading to the tolerance to drought,several hundreds of drought-toler-ance-related genes have been identified from it[7–9]. There are four pathways for these genes to respond to drought stress,of which two pathways are dependent to abscisic acid(ABA),and two are not[8].Another good material for drought stress research is Thellungiella sal-suginea.Wong et al.[10]have studied gene expression of leaf tissue of T.salsuginea under drought stress using cDNA microarray,and revealed new abiotic stress response mechanisms in T.salsuginea.Sinapis alba(white mustard)is a crucifer classified into the genus Sinapis which includes about ten grass species.It is now widespread worldwide,although it probably originated in the Mediterranean region.It has many desirable agronomic traits including tolerance to drought[11,12].Until now,however,no research has been performed on the molecular mechanisms of drought tolerance in S.alba.It is necessary to analyze the gene expression profile of white mustard under drought stress. In recent years,various techniques,such as cDNA microarray(or cDNA chip),SSH and RT-PCR,were thought to be a powerful tool in the study of gene expression profiles induced by abiotic stress in plants [7,8,13–15].However,these techniques present some defects.They are laborious,rely on a prior knowledge of the sequence,or suffer from high noise or cross-hybrid-ization problem.With the Illumina sequencing(formerly Solexa sequencing)technique developed recently,this situation has changed,and it can execute quantitative and qualitative analyses of gene expression at low cost,even if the genome of a species have not been noted,and the Illumina sequencing data are highly replicable,with rel-atively little technical variation,it may suffice to sequence each mRNA sample only once[16,17].In this study,afine comparison of mRNA expression levels of S.alba leaves under rewatering growth conditions (SaW-A)and drought stress conditions(DL-B)was per-formed based on Illumina sequencing for thefirst time. These results provide novel information for studying the molecular mechanisms of drought tolerance in S.alba, since a number of candidate genes for drought tolerance were identified.Materials and methodsPlant materials and stress treatmentsPlants growth and stress treatment were performed as described by Wong et al.[10].Plants of S.alba were grown in controlled environments with a day/night temperature regime of22°C/10°C.An irradiance of250l mol photons m-2s-1over a21-h daylength was provided.When all the plants were4weeks old,some plants,named sample Saw-A,were subjected to drought stress treatment until they wilted visibly(3–4days),and then rewatered and allowed to recover.At the moment when sample Saw-A plants were rewatered,other plants,named sample DL-B,were sub-jected to drought treatment until they wilted visibly.0.1g leaves offive individual plants for each of Saw-A and DL-B were synchronously harvested8h after the lights came on in the growth chamber.Then the equal leaf samples fromfive individual plants were mixed together for RNA extraction of Saw-A and DL-B,respectively. Processing samples for sequencingTotal RNA was extracted using the TRIzol reagent (Invitrogen).After precipitation,RNA was purified with Qiagen’s RNeasy kit with on-column DNase digestion according to the manufacturer’s instructions.Purified RNA samples were dissolved in diethylpyrocarbonate-treated H2O,and the concentration determined spectroscopically. The quality of the RNA was assessed on1.0%denaturingMol Biol Repagarose gels in combination with the Bioanalyzer2100 (Agilent).Illumina sequencingIllumina sequencing was completed at Beijing Genomics Institute,with the use of an Illumina genome analyzer(San Diego,CA).Initially,we used poly(T)oligo-attached magnetic beads to isolate poly(A)mRNA from total RNA sample.First-and second-strand cDNA synthesis were performed while the RNA was bound to the beads.While on the beads,samples were digested with NlaIII to retain a cDNA fragment from the most30CATG to the poly(A)-tail.Subsequently,the GEX adapter1was ligated to the free50end of the RNA,and a digestion with MmeI was performed,which cuts17bp downstream of the CATG site.At this point,the fragments detach from the beads. After dephosphorylation and phenol extraction,the GEX adapter2was ligated to the30end of the tag.Finally,the short cDNA fragments were prepared for Solexa sequenc-ing on an Illumina genome analyzer(San Diego,CA), using the manufacturer’s protocol and reagents of the genomic DNA sequencing sample prep kit.The Illumina/Solexa approach involved sequencing of cDNA fragments,followed by counting of the number of times a particular fragment was observed.The terminators were labeled withfluorescent compounds of four different colors to distinguish among the different bases at the given sequence position.The template sequence of each cluster was deduced by reading the color at each successive nucleotide addition step.Image analysis and base calling were performed using the Illumina Pipeline,where high-throughput short-read sequence tags were obtained after purityfiltering.This was followed by sorting and counting the unique tags.Sequence annotation,comparison and functionalclassificationThe unigenes of Sinapis,Arabidopsis and Brassica were used as a reference sequence to align and identify the sequencing reads.To map the reads to the reference,the alignments and the candidate gene identification procedure were conducted using the mapping and assembly with qualities software package MAQ[18].Differentially expressed genes between two samples were analysed according to the digital gene expression detection methods reported by Audic and Claverie[19].To categorize transcripts by putative function,we have utilized the gene ontology(GO)classification scheme[20]. GO provides a dynamic controlled vocabulary and hierar-chy that unifies descriptions of biological,cellular and molecular functions across genomes.ResultsIllumina sequencing and gene annotationWe obtained4,123,307tags in sample Saw-A(accession: SRR353366)and4,340,054tags in sample DL-B(acces-sion:SRR352383)through Illumina sequencing(Fig.1a), the original data have been placed in public databases(http:// /sra/?term=SRA047029).204,279dis-tinct tags were obtained in sample Saw-A after eliminating low quality tags and single copy tags,and217,599distinct tags were obtained in sample DL-B(Fig.1b).Though some tag copy number is far more than100,this is not what we are interested in,because two samples may both have high expression genes.In the present study,our research focuses on those tags that have obvious differences between the two samples.Though different copy number of distinct tags displayed very similar distribution patterns on the whole,specific distinct tags are quite different in the two samples(Fig.2). In Fig.2,the regions on the left of the peak zones denote the distinct tag copy number of Saw-A are abovefivetimesMol Biol Repthe DL-B,these tags are down-regulation under drought stress.The regions on the right of the peak zones represent the distinct tag copy number of DL-B are above five times the Saw-A,these tags are up-regulation under drought stress.The peak zones differ in five times between two samples.Drought tolerance genes are probably found from these tags that have apparent change in expression.Through the comparison with the open reference sequences of Arabidopsis and Brassica ,all of the distinct tags were annotated.The expression levels of these annotated genes were quantitatively analyzed as their corresponding tag copy number,and they were classified into up-regulation,down-regulation and no significance change genes.Although this was a preliminary analysis of white mustard short-read data,we have gained valuable infor-mation,which lead to the identification of differentially expressed genes between Saw-A and DL-B samples.Figure 3shows the distribution of differentially expressed genes,the dexter and upper regions with dots reveal those genes with markedly expression difference,and the rest regions shows those genes with no obvious expression diversity.The upper region with dots displays the up-reg-ulated genes of sample DL-B after stress,248genes could be annotated.The dexter region with dots displays the down-regulated genes of sample SaW-A after stress,309genes could be annotated.More detailed information including data selection is provided in Supplementary Table S1.The unigenes of Arabidopsis and Brassica were used as a reference sequence,for Arabidopsis has good basis to study drought stress as model organism,and the application of Brassica will help to found drought-related genes for molecular breeding.GO classification and annotation of differentially expressed genesThese differentially expressed genes may involve different functions,and their function annotation is very helpful for us to roundly analyze the changes of the gene expression profiles under drought stress.Then GO classification of the differentially expressed genes was performed as their up-and down-regulation changes (Fig.4).A gene can be classified into different functional gene type,so the gene number shown in the chart is more than the total number of the differentially expressed gene.The GO classification results showed that there were not down-regulated genes in the classification involved in enzyme regulator and multi\-organism processes,and up-regulated genes were not found in the classification with functions of envelope and auxiliary transport protein.These results may reflect that up-and down-regulated genes participate in different metabolic pathways and are involved in different regulation mechanisms.The differentially expressed genes were mainly involved in the cell division and catalytic and metabolic processes.According to the annotation results,parts of the up-and down-regulated genes are related to the cDNAs and unigenes expressed under both biotic and abiotic stress,which shows that there may be some the same response mechanism for diverse stress.At the same time,there are a lot of unknown tags,some important functional genes will be found in them,especially those tags that have obvious change in expression level under stresscondition.Fig.2Distribution of ratio of distinct tag copy number between twolibrariesFig.3Down-regulation and up-regulation of gene expression in S.alba under drought stress conditionMol Biol RepDiscussionThe tolerance to biotic and abiotic stresses like low or high temperature,drought,salt and disease factors in plants is a defense response involving multiple genes.Drought stress causes a great change of gene expression profile,deep understanding of the cross-talk between the transcription factor of different pathways will help the improvement of the integrated characters of crops,it is important to study the changes of gene expression profiles from the overall level.The fine comparative analysis of mRNA expression levels of S.alba leaves under drought stress and rewatering growth conditions was performed by Illumina deep sequencing method in the present study.When the plants wilted,not only the expressions of the genes related to drought stress were changed,but also the expressions of partial genes related to plant growth and development were changed.When the wilting plants were re-watered,prob-ably the expressions of the genes related to plant growth and development still maintained the changed levels at the early stage.In addition,RNA-changes are not strictly correlated to protein levels,osmotic relations or membrane characteristics [21].So we could rightly screen the genes related to drought stress when the re-watering plants and wilting plants were used as the tested materials.Illumina sequencing,different from Sanger sequence method,can provide giant sequencing data with saving time and lower cost.It is also helpful for the study of molecular breeding,evolution and development,and stress response to envi-ronment in crop plants.In the present study,557annotated genes and a large number of no matched tags were found to be involved in drought stress response,some genes encode signaling components,transcription regulators or other proteins,these proteins are necessary for cell growth and develop-ment under drought stress [22].These results indicate that it is effective to analyze the gene expression profiles under drought stress by high-throughput sequencing technologies and many novel tags have been found,however,more reliable results in the present study could be obtained with biological repetition experiments.Lee et al.[23]has ana-lyzed 24,000unigenes using a B.rapa oligo microarray and many unigenes were found to be involved in the abiotic stresses,however,this technology relied on a prior knowledge of the sequences.It is now hypothesized that halophytes use salt-tolerance effectors and regulatory pathways very similar to those in glycophytes and that subtle differences in their regulation can account for large variations in salt sensitivity [24–26],other researchers have begun to test this hypothesis [27].Plants have many common response mechanisms under abiotic stress such as salt stress and drought stress.Molecular regulation mechanisms of salt stress and drought stress can be found through comparative analysis and genetic function analysis between halophytes and glyco-phytes,and new functions will be found in the genes that have been identified in glycophytes.Arabidopsis ,a relative of white mustard,was annotated completely in genomics,its genome was used as a reference to find some known and unknown functional genes related to drought stress in white mustard as possible as we can do.At the same time,transcriptome analysis using high-throughput short-read sequencing need not be restricted to the genome of model organisms [28,29].The gene expression profiles (Supplementary Table S1)showed that the annotated genes could be grouped into two categories,the first one encode protective proteins,such as oxidoreductase,the second one encode regulatory proteins,such as transcription factors.In the up-regulated genes,theFig.4Percentagerepresentation of GO mappings for drought-tolerance correlated clustersMol Biol RepFATTY ACID REDUCTASE1gene(AT5G22500.1)has the fatty-acyl-CoA reductase activity involved in salt stress,it is grouped into the protective protein.Another gene AT4G20890.1has GTPase activity,it is grouped into the regulatory protein.A lot of the annotated genes have not been found to be involved in drought tolerance,their function need to be identified in the future research.Brassica plants are also the relatives of white mustard, 329of the557genes related to drought stress were anno-tated as the reference sequences of Brassica,these329 genes include plenty of genes with unknown function.With the deep research on gene function we will know more about these genes in the role of drought tolerance,and at last those drought tolerance genes can be applied to the genetic improvement of Brassica crop plants with mass transforming.Acknowledgments This work was supported by the National Nat-ural Science Foundation of China(30671312),the Natural Science Foundation of Hubei Province(2008CDA083and2009CDB191),the Natural Science Foundation of Henan Province(114100510013),the Chenguang Program of Wuhan City(201050231022),the Interna-tional Science and Technology Cooperation Item(S2012GR0080), and the Science and Technical Innovation Project of Hubei Province. 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Report1Annexpage 1Annex 1Tool for the demonstration and assessment of additionality1. This document provides for a step-wise approach to demonstrate and assess additionality. Thesesteps include:• Identification of alternatives to the project activity;• Investment analysis to determine that the proposed project activity is not the mosteconomically or financially attractive;• Barriers analysis;• Common practice analysis; and• Impact of registration of the proposed project activity as a CDM project activity.Based on information about activities similar to the proposed project activity, the common practiceanalysis is to complement and reinforce the investment and barriers analysis. The steps are summarizedin the flow-chart at the end of this document.2. The document provides a general framework for demonstrating and assessing additionality and isto be applicable to a wide range of project types. Particular project types may require adjustments to thisframework.3. Project participants proposing new baseline methodologies may incorporate this consolidatedtool in their proposal. Project participants may also propose other tools for the demonstration ofadditionality to the Executive Board for its consideration.Step 0. Preliminary screening based on the starting date of the project activityThe Marrakesh Accords and decision 18/CP.9 provide guidance on the eligibility of a proposed CDMproject activity which started before registration1.1. If project participants wish to have the crediting period starting prior to the registration of theirproject activity, they shall:(a) Provide evidence that the starting date of the CDM project activity falls between1 January 2000 and the date of the registration of a first CDM project activity, bearing inmind that only CDM project activities submitted for registration before 31 December2005 may claim for a crediting period starting before the date of registration; and(b) Provide evidence that the incentive from the CDM was seriously considered in thedecision to proceed with the project activity. This evidence shall be based on (preferablyofficial, legal and/or other corporate) documentation that was available to third parties at,or prior to, the start of the project activity.1 For more information see decisions 17/CP.7 and 18/CP.9 (documents FCCC/CP/2001/13/Add.2,FCCC/CP/2003/6/Add.2) and the Glossary of CDM terms contained in the guidelines for completing the projectdesign document (CDM-PDD) available on the UNFCCC CDM web site: unfccc.int/cdm.Report1Annexpage 2Step 1. Identification of alternatives to the project activity consistent withcurrent laws and regulations(Note: In accordance with guidance by the Executive Board, consistency is to be ensured between“baseline scenario” and “baseline emissions”2)Define realistic and credible alternatives3 to the project activity(s) that can be (part of) the baselinescenario through the following sub-steps:Sub-step 1a. Define alternatives to the project activity:1. Identify realistic and credible alternative(s) available to the project participants or similar projectdevelopers4 that provide outputs or services comparable with the proposed CDM project activity5. Thesealternatives are to include:• The proposed project activity not undertaken as a CDM project activity;• All other plausible and credible alternatives to the project activity that deliver outputs and onservices (e.g. electricity, heat or cement) with comparable quality, properties and applicationareas;• If applicable,continuation of the current situation (no project activity or other alternativesundertaken).Sub-step 1b. Enforcement of applicable laws and regulations:2. The alternative(s) shall be in compliance with all applicable legal and regulatory requirements,even if these laws and regulations have objectives other than GHG reductions, e.g. to mitigate local airpollution.6 (This sub-step does not consider national and local policies that do not have legally-bindingstatus.7).3. If an alternative does not comply with all applicable legislation and regulations, then show that,based on an examination of current practice in the country or region in which the law or regulation2 Please refer to paragraph 2 of Annex3 of the report of the Executive Board at its ninth meeting, see:http://cdm.unfccc.int/EB/Meetings/009/eb09repa3.pdf.3 Reference to “alternatives” throughout this document denotes “alternative scenarios”.4 For example, a coal-fired power station or hydropower may not be an alternative for an independent powerproducer investing in wind energy or for a sugar factory owner investing in a co-generation, but may be analternative for a public utility. Alternatives are, therefore, related to technology and circumstances as well as to theinvestor.5 For example, the outputs of a cogeneration project could include heat for on-site use, electricity for on-site use, andexcess electricity for export to the grid. In the case of a proposed landfill gas capture project, the service providedby the projects includes operation of a capped landfill.6 For example, an alternative consisting of an open, uncapped landfill would be non-complying in a country wherethis scenario would imply violations of safety or environmental regulations pertaining to landfills.7 This aspect may be modified based on forthcoming guidance from the Executive Board on national and sectoralpolicies.Report1Annexpage 3applies, those applicable legal or regulatory requirements are systematically not enforced and that non-compliance with those requirements is widespread in the country. If this cannot be shown, then eliminatethe alternative from further consideration;4. If the proposed project activity is the only alternative amongst the ones considered by the projectparticipants that is in compliance with all regulations with which there is general compliance, then theproposed CDM project activity is not additional.8→Proceed to Step 2 (Investment analysis) or Step 3 (Barrier analysis). (Project participants may alsoselect to complete both steps 2 and 3.)Step 2. Investment analysisDetermine whether the proposed project activity is the economically or financially less attractive thanother alternatives without the revenue from the sale of certified emission reductions (CERs). To conductthe investment analysis, use the following sub-steps:Sub-step 2a. Determine appropriate analysis method1. Determine whether to apply simple cost analysis, investment comparison analysis or benchmarkanalysis (sub-step 2b). If the CDM project activity generates no financial or economic benefits otherthan CDM related income, then apply the simple cost analysis (Option I). Otherwise, use the investmentcomparison analysis (Option II) or the benchmark analysis (Option III).Sub-step 2b. – Option I. Apply simple cost analysis2. Document the costs associated with the CDM project activity and demonstrate that the activityproduces no economic benefits other than CDM related income.→ If it is concluded that the proposed CDM project activity is not financially attractive then proceedto Step 4 (Common practice analysis).Sub-step 2b. – Option II. Apply investment comparison analysis3. Identify the financial indicator, such as IRR9, NPV, cost benefit ratio, or unit cost of service (e.g.,levelized cost of electricity production in $/kWh or levelized cost of delivered heat in $/GJ) most suitablefor the project type and decision-making context.8 This provision may be further elaborated depending on deliberation from the Board regarding requirements for therenewal of a crediting period.9 For the investment comparison analysis, IRRs can be calculated either as project IRRs or as equity IRRs. ProjectIRRs calculate a return based on project cash outflows and cash inflows only, irrespective the source of financing.Equity IRRs calculate a return to equity investors and therefore also consider amount and costs of available debtfinancing. The decision to proceed with an investment is based on returns to the investors, so equity IRR will bemore appropriate in many cases. However, there will also be cases where a project IRR may be appropriate.Report1Annexpage 4Sub-step 2b – Option III. Apply benchmark analysis4. Identify the financial indicator, such as IRR10, NPV, cost benefit ratio, or unit cost of service(e.g., levelized cost of electricity production in $/kWh or levelized cost of delivered heat in $/GJ) mostsuitable for the project type and decision context. Identify the relevant benchmark value, such as therequired rate of return (RRR) on equity. The benchmark is to represent standard returns in the market,considering the specific risk of the project type, but not linked to the subjective profitability expectationor risk profile of a particular project developer. Benchmarks can be derived from:• Government bond rates, increased by a suitable risk premium to reflect private investment and/orthe project type, as substantiated by an independent (financial) expert;• Estimates of the cost of financing and required return on capital (e.g. commercial lending ratesand guarantees required for the country and the type of project activity concerned), based onbankers views and private equity investors/funds’ required return on comparable projects;• A company internal benchmark (weighted average capital cost of the company) if there is onlyone potential project developer (e.g. when the project activity upgrades an existing process). Theproject developers shall demonstrate that this benchmark has been consistently used in the past,i.e. that project activities under similar conditions developed by the same company used the samebenchmark.Sub-step 2c. Calculation and comparison of financial indicators (only applicable to options II andIII):5. Calculate the suitable financial indicator for the proposed CDM project activity and, in the caseof Option II above, for the other alternatives. Include all relevant costs (including, for example, theinvestment cost, the operations and maintenance costs), and revenues (excluding CER revenues, butincluding subsidies/fiscal incentives11 where applicable), and, as appropriate, non-market cost andbenefits in the case of public investors.6. Present the investment analysis in a transparent manner and provide all the relevant assumptionsin the CDM-PDD, so that a reader can reproduce the analysis and obtain the same results. Clearlypresent critical techno-economic parameters and assumptions (such as capital costs, fuel prices, lifetimes,and discount rate or cost of capital). Justify and/or cite assumptions in a manner that can be validated bythe DOE. In calculating the financial indicator, the project’s risks can be included through the cash flowpattern, subject to project-specific expectations and assumptions (e.g. insurance premiums can be used inthe calculation to reflect specific risk equivalents).7. Assumptions and input data for the investment analysis shall not differ across the project activityand its alternatives, unless differences can be well substantiated.10 For the benchmark analysis, the IRR shall be calculated as project IRR. If there is only one potential projectdeveloper (e.g. when the project activity upgrades an existing process), the IRR shall be calculated as equity IRR.11 This provision may be further elaborated depending on deliberations by the Board on national and sectoralpolicies.Report1Annexpage 58. Present in the CDM-PDD submitted for validation a clear comparison of the financial indicatorfor the proposed CDM activity and:(a) The alternatives, if Option II (investment comparison analysis) is used. If one of theother alternatives has the best indicator (e.g. highest IRR), then the CDM project activitycan not be considered as the most financially attractive;(b) The financial benchmark, if Option III (benchmark analysis) is used. If the CDM projectactivity has a less favourable indicator (e.g. lower IRR) than the benchmark, then theCDM project activity cannot be considered as financially attractive.Sub-step 2d. Sensitivity analysis (only applicable to options II and III):9. Include a sensitivity analysis that shows whether the conclusion regarding the financialattractiveness is robust to reasonable variations in the critical assumptions. The investment analysisprovides a valid argument in favour of additionality only if it consistently supports (for a realistic rangeof assumptions) the conclusion that the project activity is unlikely to be the most financially attractive (asper step 2c para 8a) or is unlikely to be financially attractive (as per step 2c para 8b).→ If after the sensitivity analysis it is concluded that the proposed CDM project activity is unlikely tobe the most financially attractive (as per step 2c para 8a) or is unlikely to be financially attractive (asper step 2c para 8b), then proceed to Step 3 (Barrier analysis) or Step 4 (Common practice analysis).→ Otherwise, unless barrier analysis below is undertaken and indicates that the proposed projectactivity faces barriers that do not prevent the baseline scenario(s) from occurring, the project activityis considered not additional.Step 3. Barrier analysisIf this step is used, determine whether the proposed project activity faces barriers that:(a) Prevent the implementation of this type of proposed project activity; and(b) Do not prevent the implementation of at least one of the alternatives.Use the following sub-steps:Sub-step 3a. Identify barriers that would prevent the implementation of type of the proposed projectactivity:1. Establish that there are barriers that would prevent the implementation of the type of proposedproject activity from being carried out if the project activity was not registered as a CDM activity. Suchbarriers may include, among others:Investment barriers, other than the economic/financial barriers in Step 2 above, inter alia:- Debt funding is not available for this type of innovative project activities.Report1Annexpage 6- No access to international capital markets due to real or perceived risks associated withdomestic or foreign direct investment in the country where the project activity is to beimplemented.Technological barriers, inter alia:- Skilled and/or properly trained labour to operate and maintain the technology is notavailable and no education/training institution in the host country provides the neededskill, leading to equipment disrepair and malfunctioning;- Lack of infrastructure for implementation of the technology.Barriers due to prevailing practice, inter alia:- The project activity is the “first of its kind”: No project activity of this type is currentlyoperational in the host country or region.The identified barriers are only sufficient grounds for demonstration of additionality if they wouldprevent potential project proponents from carrying out the proposed project activity if it was not expectedto be registered as a CDM activity.2. Provide transparent and documented evidence, and offer conservative interpretations of thisdocumented evidence, as to how it demonstrates the existence and significance of the identified barriers.Anecdotal evidence can be included, but alone is not sufficient proof of barriers. The type of evidence tobe provided may include:(a) Relevant legislation, regulatory information or industry norms;(b) Relevant (sectoral) studies or surveys (e.g. market surveys, technology studies, etc)undertaken by universities, research institutions, industry associations, companies,bilateral/multilateral institutions, etc;(c) Relevant statistical data from national or international statistics;(d) Documentation of relevant market data (e.g. market prices, tariffs, rules);(e) Written documentation from the company or institution developing or implementing theCDM project activity or the CDM project developer, such as minutes from Boardmeetings, correspondence, feasibility studies, financial or budgetary information, etc;(f) Documents prepared by the project developer, contractors or project partners in thecontext of the proposed project activity or similar previous project implementations;(g) Written documentation of independent expert judgements from industry, educationalinstitutions (e.g. universities, technical schools, training centres), industry associationsand others.Sub-step 3 b. Show that the identified barriers would not prevent the implementation of at least one ofthe alternatives (except the proposed project activity):3. If the identified barriers also affect other alternatives, explain how they are affected less stronglythan they affect the proposed CDM project activity. In other words, explain how the identified barriersare not preventing the implementation of at least one of the alternatives. Any alternative that would beReport1Annexpage 7prevented by the barriers identified in Sub-step 3a is not a viable alternative, and shall be eliminated fromconsideration. At least one viable alternative shall be identified.→ If both Sub-steps 3a – 3b are satisfied, proceed to Step 4 (Common practice analysis)→ If one of the Sub-steps 3a – 3b is not satisfied, the project activity is not additional.Step 4. Common practice analysisThe above generic additionality tests shall be complemented with an analysis of the extent to which theproposed project type (e.g. technology or practice) has already diffused in the relevant sector and region.This test is a credibility check to complement the investment analysis (Step 2) or barrier analysis(Step 3). Identify and discuss the existing common practice through the following sub-steps:Sub-step 4a. Analyze other activities similar to the proposed project activity:1. Provide an analysis of any other activities implemented previously or currently underway that aresimilar to the proposed project activity. Projects are considered similar if they are in the samecountry/region and/or rely on a broadly similar technology, are of a similar scale, and take place in acomparable environment with respect to regulatory framework, investment climate, access to technology,access to financing, etc. Other CDM project activities are not to be included in this analysis. Providequantitative information where relevant.Sub-step 4b. Discuss any similar options that are occurring:2. If similar activities are widely observed and commonly carried out, it calls into question theclaim that the proposed project activity is financially unattractive (as contended in Step 2) or facesbarriers (as contended in Step 3). Therefore, if similar activities are identified above, then it is necessaryto demonstrate why the existence of these activities does not contradict the claim that the proposedproject activity is financially unattractive or subject to barriers. This can be done by comparing theproposed project activity to the other similar activities, and pointing out and explaining essentialdistinctions between them that explain why the similar activities enjoyed certain benefits that rendered itfinancially attractive (e.g., subsidies or other financial flows) or did not face the barriers to which theproposed project activity is subject.3. Essential distinctions may include a serious change in circumstances under which the proposedCDM project activity will be implemented when compared to circumstances under which similar projectswhere carried out. For example, new barriers may have arisen, or promotional policies may have ended,leading to a situation in which the proposed CDM project activity would not be implemented without theincentive provided by the CDM. The change must be fundamental and verifiable.→ If Sub-steps 4a and 4b are satisfied, i.e. similar activities cannot be observed or similar activitiesare observed, but essential distinctions between the project activity and similar activities canreasonably be explained, please go to step 5 (Impact of CDM registration).→ If Sub-steps 4a and 4b are not satisfied, i.e. similar activities can be observed and essentialdistinctions between the project activity and similar activities cannot reasonably be explained, theproposed CDM project activity is not additional.Report1Annexpage 8Step 5. Impact of CDM registrationExplain how the approval and registration of the project activity as a CDM activity, and the attendantbenefits and incentives derived from the project activity, will alleviate the economic and financialhurdles (Step 2) or other identified barriers (Step 3) and thus enable the project activity to be undertaken.The benefits and incentives can be of various types, such as:• Anthropogenic greenhouse gas emission reductions;• The financial benefit of the revenue obtained by selling CERs,• Attracting new players who are not exposed to the same barriers, or can accept a lower IRR (forinstance because they have access to cheaper capital),• Attracting new players who bring the capacity to implement a new technology, and• Reducing inflation /exchange rate risk affecting expected revenues and attractiveness forinvestors.→ If Step 5 is satisfied, the proposed CDM project activity is not the baseline scenario.→ If Step 5 is not satisfied, the proposed CDM project activity is not additional.Report1Annexpage 9Flowchart: Additionality scheme。

Face Identification from Manipulated Facial Images using SIFTH. R. Chennamma Lalitha Rangarajan Veerabhadrappa Dept. of Studies in Computer Science Dept. of Studies in Computer Science Dept. of Studies in Computer Science University of Mysore University of Mysore University of MysoreMysore, INDIA Mysore, INDIA Mysore, INDIAanusha_hr@ lali85arun@yahoo.co.in veerabadrappa@ Abstract—Editing on digital images is ubiquitous.Identification of deliberately modified facial images is a newchallenge for face identification system. In this paper, weaddress the problem of identification of a face or person fromheavily altered facial images. In this face identificationproblem, the input to the system is a manipulated ortransformed face image and the system reports back thedetermined identity from a database of known individuals.Such a system can be useful in mugshot identification in whichmugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods.Keywords- Mugshots, Image Tampering, SIFT, PCAI.I NTRODUCTIONPhoto editing software tools are becoming moresophisticated and user friendly day by day. Face is animportant biometric trait for the identification of a person. Forensic investigation and law enforcement is one of themajor applications of face recognition problem. Rigorous research has been carried out so far for recognition of faces by considering different viewpoints, illuminations, facial expressions, occlusions etc. Changing appearance to hide the identity of a person is very common. Some examples are shown in Fig. 1. In which original face images are modified by altering almost all facial features like eyes, ears, nose, hair style, mouth, shape of the face. etc. Such identity modifications are simulated using face transformation software tool. In this paper, we deal with the problem of face identification from altered facial images.(a) (b)(c) (d)Figure 1. (a) & (c) are original images, (b) & (d) are modified imagesModifications to the face image are not a well defined notion and it is always depending on the purpose of usage. For a magazine cover or posters, skin softening and somelocal editing may be required. Change of complete appearance of the face may be necessary to misguide the face identification system or agency. In our face identification problem, the query face image to the system is suspected to be a manipulated face image and the system reports back the identified person from a face database of known individuals. In this research work, we concentrate only on mugshot identification. Mugshots consists of two views (frontal and profile) of each criminal. Mugshots are downloaded from /mugshots. We considered only frontal view in our mugshot identification system. Image editing software tool like Adobe Photoshop is commonly used to perform alterations on digital images. For instance a skilled person can create old age face or childhood face from young adult face. Such a job can also be done by various software tools available on the web and such a process is called as face transformation. The aim of this research is to measure similarity between query (manipulated or transformed) face image with all the face images in database and retrieve the image which has got highest similarity i.e. nearest neighbour. Since query is created from one of the database image (source or original image), the system should assign highest rank to the right source image and retrieve it. Query image is created using the face transformation tool implemented by the University of St. Andrews available in . Two well known face recognition techniques Scale Invariant Feature Transform (SIFT) and Eigenfaces are evaluated and their performances compared.II.R ELATED W ORKAs far as our knowledge, this is the first attempt for mugshot identification from modified face images. Here we review related work on face recognition problem that deal with different form of face representatives and we also review prior work that evaluated the robustness of SIFT features for face recognition.Robert et. al [1] have presented a theory and practical computations for automatically matching a police artist sketch to a set of true photographs. This method locates facial features in both the sketch as well as the set of photograph images. Then, the sketch is photometrically standardized to facilitate comparison with a photo and then both the sketch and the photos are geometrically standardized. Finally, for matching, eigenanalysis is employed.Xiaogang Wang et. al [2] have proposed a novel face photo-sketch synthesis and recognition method using a multi scale Markov Random Fields (MRF) model. To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learnt at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition.Wolfgang Konen [3] has compared facial line drawings with gray-level images using a software tool called PHANTOMAS. Yongsheng et. al [4] have presented a methodology for facial expression recognition from a single static using line-based caricature. The proposed approach uses structural and geometrical features of a user sketched expression model to match the Line Edge Map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined.Rich Singh et. al [5] have presented a novel age transformation algorithm to handle the challenge of facial aging in face recognition. The proposed algorithm registers the gallery and probe face images in polar coordinate domain and minimizes the variation in facial features caused due to aging. The efficiency of the proposed age transformation algorithm is validated using 2D log polar Gabor based face recognition algorithm on a face database that comprises of face images with large age progression.Mohamed Aly [6] used SIFT features for general face recognition problem. He compared SIFT with Eigen faces and Fisher faces then reported the superiority of SIFT features for face recognition. Han Yanbin et. al [7] have extracted face features by using SIFT. Then, face recognition is conducted by comparing real extracted features with training sets. They experimented with ORL face database and reported recognition rate for SIFT, PCA, ICA and FLD as 96.3%, 92.5%, 91.6% and 92.8% respectively.III.B ACKGROUNDA.SIFTLowe [8] invented robust image features called ScaleInvariant Feature Transform which are invariant to scale, rotation, affine transformations, noise, occlusions and arehighly distinctive. Detection and representation of SIFT features consist of four major stages: (1) scale-space peak selection; (2) keypoint localization; (3) orientation assignment; (4) keypoint descriptor. In the first stage, potential interest points are identified by scanning the image over location and scale. This is implemented efficiently by constructing a Gaussian pyramid and searching for local peaks (termed keypoints) in a series of Difference-of-Gaussian (DoG) images. In the second stage, candidate keypoints are localized to sub-pixel accuracy and eliminated if found to be unstable. Stage 3 identifies the dominant orientations for each keypoint based on its location. The final stage builds a local image descriptor for each keypoint, based upon the image gradients in its local neighbourhood. Every feature is a vector of dimension 128 distinctively identifying the neighbourhood around the key point.B.PCAEigenfaces are based on the dimensionality reductionapproach of Principal Component Analysis (PCA) [9]. The basic idea is to treat each image as a vector in a high dimensional space. Then PCA is applied to the set of images to produce a new reduced subspace that captures most of the variability between the input images. The Principal Component Vector (eigenvectors of the sample covariance matrix) is called the Eigenface. Every input image can be represented as a linear combination of these eigenfaces by projecting the image onto the new eigenfaces space. Then we can perform the identification process by matching in this reduced space. An input image is transformed into the eigenspace and the nearest face is identified using a nearest neighbor approach. Euclidean distance is used to match the input image against all images in the database.IV.A PPROACHMatlab is used to implement eigenfaces. Eigenfaces are computed for each face in the database and the eigenface of the query face is compared with all faces in the database. Comparison is done by computing Euclidean distance between two eigenfaces. Nearest neighbour of the query is retrieved which has got minimum distance.The code for extracting SIFT features is available in Lowe’s [8] website. The SIFT features are extracted from all faces in the database. Then given a new face image, the features extracted from that face are compared against the features from each face in the database. A feature is considered as matched with another feature when the distance to that feature is less than a specific fraction of the distance to the next nearest feature. Further spatial topology is verified by Angle-Line Ratio (ALR) statistics [10] among the matched feature distributions. This ensures that we reduce the number of false matches. The face in the databasewith the largest number of matching points that agrees with the spatial distributions of the keypoints is considered as nearest face and is used for the classification of the new face.V.E XPERIMENTSA.DatasetThe frontal views of the mugshots are usually with neutral expression. Our mugshot dataset consists of 100 face images downloaded from /mugshots. Some examples are shownin Fig. 2.Figure 2. Frontal view of mugshotsImages are of different resolutions varies from 321x442to 700x875. Only the face portion is cropped and used forevaluation. We have created 100 query face images from the100 database images by performing various transformationsto the database images. This is done by using the facetransformation tool implemented by the University of St.Andrews available online in http://morph.cs.st-. Original face image and its transformedversions are shown in Fig. 3. Further, images are convertedto gray scale and resized to 300x300 pixels to assess theefficiency of the algorithms considered for comparison.B.ResultsThe aim of this face identification system is to measuresimilarity between query face image with all face images ofdatabase and retrieve the image which has got highestsimilarity i.e. nearest neighbour. Since query is created fromone of the database images (source or original image), thesystem should retrieve that specific original face image. Wehave 100 manipulated faces as queries and 100 original faceimages of the criminals in the database.Figure 3. Original face images and its transformed versionsIn order to evaluate performance of the system we inputeach query at a time. The identification rate is computed asfollows;100RexQueriesofNumbertrievedPositivesCorrecttimesofNumberRatetionIdentificaSome resultant face images from both SIFT and PCA areshown in Fig. 4. Figure 4 shows two false positives and onecorrect positive retrieved using PCA and correct positivesretrieved in all three cases using SIFT approach. The faceidentification rate is shown in Table 1. It is evident fromTable 1 that SIFT performs better in face identification evenunder deliberate modifications.PCA SIFTFigure 4. Retrieved faces from PCA and SIFT approachesTABLE I. F ACE I DENTIFICATION R ATESIFT PCAIdentification Rate 92% 58%VI.D ISCUSSIONThis paper presents a new approach for faceidentification from manipulated facial images based on SIFTfeatures. The proposed approach is compared witheigenfaces and proved its superiority through experiments. Inthis paper, we concentrated only on mugshot identification. As an extension, we are investigating the use of SIFTfeatures for retrieval of correct face with other forms of face representatives.R EFERENCES[1]Robert G. Uhl, Jr., Niels da Vitoria Lobo, A Framework forRecognizing a Facial Image from a Police Sketch, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.586, 1996.[2]Xiaogang Wang, Xiaoou, Face Photo-Sketch Synthesis andRecognition, IEEE Trans. On Pattern Analysis and Machine Intelligence, 31(11), pp. 1955-1967, Nov. 2009.[3]Wolfgang Konen, Comparing Facial Line Drawings with Gray-LevelImages: A Case Study on PHANTOMAS, Lecture Notes In Computer Science, Vol. 1112,Pages: 727 – 734, 1996.[4]Yongsheng Gao, M.K.H. Leung, Siu Cheung Hui, and M.W.Tananda. Facial expression recognition from line-based caricatures,IEEE Transactions on Systems, Man, and Cybernetics, 33(3), pp.407-412, 2003.[5]Richa Singh, Mayank Vatsa, Afzel Noore, Sanjay K. Singh, AgeTransformation for Improving Face Recognition Performance, Lecture Notes In Computer Science, pp. 576-583, 2007.[6]Mohamed Aly, Face recognition using SIFT features/maala/research.php[7]Han Yanbin, Yin Jianqin, Li Jinping, Human Face Feature Extractionand Recognition Base on SIFT, International Symposium on Computer Science and Computational Technology, vol. 1, pp.719-722, 2008.[8]Lowe D.G., Distinctive Image Features from Scale-InvariantKeypoints International Journal of Computer Vision, 1(60), pp. 63-86, 2004.[9]Matthew A. Turk, Alex P. Pentland, Face recognition usingeigenfaces, In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991.[10]H. R. Chennamma, Lalitha Rangarajan, Robust Near-Duplicate ImageMatching for Digital Image Forensics, International Journal of Digital Crime and Forensics, 1(3). July 2009.。

么亚妹，陈奕彤，田永涛，等. 百香果皮分级萃取物中多酚轮廓分析及其抗氧化活性物质筛选[J]. 食品工业科技，2024，45（1）：18−27. doi: 10.13386/j.issn1002-0306.2023070099YAO Yamei, CHEN Yitong, TIAN Yongtao, et al. Analysis of Polyphenol Profiles in Fractional Extracts of Passion Fruit Peels and Screening of Their Antioxidant Active Substances[J]. Science and Technology of Food Industry, 2024, 45(1): 18−27. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2023070099· 特邀主编专栏—食品中天然产物提取分离、结构表征和生物活性（客座主编：杨栩、彭鑫） ·百香果皮分级萃取物中多酚轮廓分析及其抗氧化活性物质筛选么亚妹1,2，陈奕彤2, +，田永涛2，刘天悦2，王文蜀1,2,*（1.质谱成像与代谢组学国家民委重点实验室（中央民族大学），北京 100081；2.中央民族大学生命与环境科学学院，北京 100081）摘　要：为探究不同溶剂对百香果皮多酚的萃取效率，寻找其中抗氧化活性贡献大的特征多酚。

选用石油醚、乙酸乙酯和正丁醇分级萃取百香果皮乙醇粗提物，采用分光光度法分别测定其总酚总黄酮含量，借助超高效液相色谱串联质谱解析各分级萃取物中酚类化合物，并结合非靶向代谢组学技术筛选差异代谢物并定量。

选用DPPH 自由基清除、ABTS +自由基清除和Fe 2+还原法分析各分级萃取物体外抗氧化活性差异，通过皮尔逊相关性分析探寻百香果皮中抗氧化活性酚类标志物。

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.。

ORIGINAL ARTICLESoybean 14-3-3gene family:identification and molecular characterizationXuyan Li •Sangeeta DhaubhadelReceived:31August 2010/Accepted:3November 2010/Published online:26November 2010ÓHer Majesty the Queen in Rights of Canada 2010Abstract The 14-3-3s are a group of proteins that are ubiquitously found in eukaryotes.Plant 14-3-3proteins are encoded by a large multigene family and are involved in signaling pathways to regulate plant development and protection from stress.Recent studies in Arabidopsis and rice have demonstrated the isoform specificity in 14-3-3s and their client protein interactions.However,detailed characterization of 14-3-3gene family in legumes has not been reported.In this study,soybean 14-3-3proteins were identified and their molecular characterization performed.Data mining of soybean genome and expressed sequence tag databases identified 1814-3-3genes,of them 16are transcribed.All 16SGF14s have higher expression in embryo tissues suggesting their potential role in seed development.Subcellular localization of all transcribed SGF14s demonstrated that 14-3-3proteins in soybean have isoform specificity,however,some overlaps were also observed between closely related isoforms.A comparative analysis of SGF14s with Arabidopsis and rice 14-3-3s indicated that SGF14s also group into epsilon and non-epsilon classes.However,unlike Arabidopsis and rice 14-3-3s,SGF14s contained only one kind of gene structure belonging to each class.Overall,soybean consists of the largest family of 14-3-3proteins characterized to date.Our results provide a solid framework for further investigationsinto the role of SGF14s and their involvement in legume-specific functions.Keywords 14-3-3proteins ÁGene regulation ÁMultigene family ÁProtein–protein interaction ÁSoybean ÁPhylogeny Abbreviations DAP Days after pollination EST Expressed sequence tag TC Tentative contig BiFC Bimolecular fluorescence complementationassayBLAST Basic local alignment search tool YFP Yellow fluorescent protein YN N-terminal half of yellow fluorescent protein YC C-terminal half of yellow fluorescent protein MYA Million years agoIntroductionThe 14-3-3s are a group of proteins that are ubiquitously found in eukaryotes.Originally isolated from brain tissue,14-3-3s are abundant,soluble acidic proteins (reviewed in Moore and Perez 1967)that are thought to be brain specific for a long time.They regulate activities of a wide array of target proteins via protein–protein interactions which involves binding with phosphoserine/phosphothreonine residues in the target proteins (Muslin et al.1996;Yaffe et al.1997).The targets for 14-3-3s range from proteins involved in signal transduction to gene regulation with which they interact as a dimer with native dimeric size of *60kDa.Each monomer in the dimer is capable ofElectronic supplementary material The online version of this article (doi:10.1007/s00425-010-1315-6)contains supplementary material,which is available to authorized users.X.Li ÁS.Dhaubhadel (&)Southern Crop Protection and Food Research Center,Agriculture and Agri-Food Canada,1391Sandford Street,London,ON N5V 4T3,Canadae-mail:sangeeta.dhaubhadel@agr.gc.caPlanta (2011)233:569–582DOI 10.1007/s00425-010-1315-6interacting with a separate target protein.The dimeric property of14-3-3s allows them to serve as scaffolds by bringing two different regions of the same protein into proximity within a single complex or two different proteins together.Two consensus14-3-3binding phosphopeptide motifs,RSXpSXP(mode I)and RXY/FXpSXP(mode II), where X is any amino acid and pS is the phosphoserine, have been reported(Yaffe et al.1997).However,many 14-3-3-binding sites do not conform to these consensus motifs(Aitken2006).In addition,some targets do not require phosphorylation status to bind with14-3-3s(Fu et al.2000;Sumioka et al.2005).Although it is possible that the unphosphorylated target protein interacts with an intermediary protein that is phosphorylated and binds to 14-3-3s(Johnson et al.2010).Plant14-3-3proteins were identified about two decades after theirfirst discovery in animals.They werefirst reported from four different plant species,Hordeum vulgare(Brandt et al.1992),Arabidopsis thaliana(Lu et al.1992),Spinacia oleracea and Oenothera hookeri(Hirsch et al.1992).Sub-sequently,more14-3-3s have been isolated and character-ized from several plants(reviewed in Chevalier et al.2009). As in animal system,plant14-3-3proteins have been found to regulate a variety of biological processes such as meta-bolic,growth and developmental or signaling pathways via interactions with their target proteins(reviewed in Sehnke et al.2002).They have been involved in regulation of gene expression,protein synthesis,protein folding,primary metabolism including plasma membrane-localized proton pump,organellar/nucleocytoplasmic shuttling,hormone metabolism,and chromatin remodeling(Huber et al.2002; Paul et al.2008).Some examples of the target proteins for plant14-3-3s include RSG(for repression of shoot growth), that controls gibberellin biosynthesis(Igarashi et al.2001), ABA-responsive element binding factors ABF(Schoonheim et al.2009)as well as the key transcription factors BZR1and BZR2involved in brassinosteroid signal transduction(Bai et al.2007;Gampala et al.2007;Ryu et al.2007).Several studies have also established a role for14-3-3in signaling pathways and environmental stress response(Aksamit et al. 2005;Lapointe et al.2001;Seehaus and Tenhaken1998;Xu and Shi2006).Recently the whole genome sequencing of plants has assisted for a survey of plant14-3-3proteins and possible implications for their role in plant growth and develop-mental processes.Data mining has identified15and8 14-3-3genes in Arabidopsis and rice genome,respectively (DeLille et al.2001;Rosenquist et al.2001;Sehnke et al. 2006;Yao et al.2007b).Of those14-3-3genes identified, only13genes in Arabidopsis and6in rice are transcribed. Presence of large number of isoforms in these plants sug-gests that isoform-specific interactions with specific targets might be an important element in regulation of14-3-3function.These14-3-3isoforms are encoded by different genes with small differences in their sequences but perform similar function by physically interacting with specific client proteins and bringing a modification in clients. Arabidopsis14-3-3isoforms display distinct and differen-tial patterns of subcellular distribution and their localiza-tion is both isoform specific together with their interaction with cellular clients(Alsterfjord et al.2004;Paul et al. 2005).The isoform-specific interaction of14-3-3proteins has also been found in rice where different isoform showed different binding specificity towards ACC synthase(Yao et al.2007a).Therefore,it is important to address the implications of14-3-3family diversity within organisms as their genome becomes available.The involvement of spe-cific14-3-3isoforms in a given physiological condition and their temporal and spatial expression may elucidate their role in plant development and/or resistance to stress.Soybean(Glycine max[L.]Merr.)is an important cash crop that contains world’s major supply of both vegetable oil and protein with variety of uses in human food and animal feed.Despite that much is learnt about14-3-3s in non-legumes,the diversity of this group of proteins in legume plants is not yet known.A proteomic study of seed filling in soybean identified a number of14-3-3proteins from soybean seeds(Hajduch et al.2005)suggesting their role in seed development.The expression of a soybean 14-3-3gene was also upregulated in response to plant–microbe interaction(Seehaus and Tenhaken1998).Avail-ability of full genome sequence of soybean provides an excellent opportunity to explore diversity of legume14-3-3s in general.Furthermore,a comparative analysis of the 14-3-3proteins from different plant species may provide new insights on the phylogeny and functional conservation and diversification of the14-3-3s in plants.In this study, data mining of soybean genome was performed and18 14-3-3genes were identified.By using reverse transcriptase (RT)-PCR,it was confirmed that out of1814-3-3s,only16 genes(SGF14a-SGF14p)are expressed in soybean.This study demonstrates that(1)soybean SGF14isoforms were produced by tandem duplication as well as whole genome duplication events,(2)the expression patterns of SGF14 genes are diverse and vary depending on tissue type and developmental stage,and(3)SGF14s are localized in dif-ferent subcellular compartments and the localization pattern is isoform specific with some overlaps between closely related isoforms.Identification of all the members of SGF14 family in soybean and understanding their diversity as well as specificity towards their client proteins may provide key information for further study of this important gene family in soybean.Our study presents the foundation for an in-depth and detailed analysis of14-3-3gene family in legumes.The results may facilitate cross-utilization of genetic resources in agricultural important legume crops.Materials and methodsPlant materialsSoybean(G.max L.Merr.)cv Harosoy63seeds were planted at Agriculture and Agri-Food Canada experimental station in southern Ontario,London,in2008.Regular agronomic practices and planting dates were followed.The pods were tagged on thefirst day of pollination and har-vested at30,40,and50days after pollination(DAP). Soybean tissues were randomly collected from5–7plants and were frozen in liquid nitrogen,and stored at-80°C.Tobacco(Nicotiana benthamiana)seeds were grown in pots under the condition of a12-h light/dark cycle at25°C and70–80%relative humidity.Identification of soybean14-3-3ESTs and isolationof full length cDNAsThe DFCI Soybean Gene Index database(http://compbio. /tgi/cgi-bin/tgi/gireport.pl?gudb=soybean) was searched by using‘14-3-3’as a key word.Candidate expressed sequence tags(ESTs)or tentative contigs(TCs) were utilized to search against the soybean genome data-base(/search.php?show=blast). PCR amplification was carried out using pooled cDNA from different soybean tissues as template with gene-spe-cific primers designed to amplify complete open reading frame of each soybean14-3-3gene.In each forward pri-mer,CACC was added before ATG to facilitate the target sequence into Gateway pENTR/D-TOPO entry vector (Invitrogen,Carlsbad,CA,USA)in correct orientation. The PCR product was gel purified and recombined into pENTR/D-TOPO entry vector.The resulting recombinant plasmid contained target14-3-3sequenceflanked by attL region.The full length cDNA sequence for each14-3-3 clone was confirmed by sequencing and primer specificity to amplify specific14-3-3gene was verified.The soybean 14-3-3isoforms were named(SGF14a–SGF14r)following existing nomenclature for constancy.The gene-specific primer sequences for isolating soybean14-3-3genes are shown in supplementary Table S1.Gene structure and phylogenetic analysisComparison and analysis of soybean14-3-3sequences were performed using BLAST(Altschul et al.1990)at the National Center for Biotechnology Information(http:// /).Multiple sequence alignments were obtained using ClustalW2(/ Tools/clustalw2/index.html).The phylogenetic tree was built by neighbor-joining method using MEGA4software (Tamura et al.2007).Bootstrap values were calculated from1,000trials.The evolutionary distances were com-puted using the Dayhoff matrix based method(Schwarz and Dayhoff1979).Gene structure was constructed by SIM4(http://pbil.univ-lyon1.fr/members/duret/cours/ inserm210604/exercise4/sim4.html).The accession num-bers of Arabidopsis and rice14-3-3proteins used in phy-logenetic analysis are:AtGF14j(AAD51783),AtGF14k (AAD51781),AtGF14v(AAA96254),AtGF14u(AAB 62224),AtGF14x(AAA96253),AtGF14m(AAD51782), AtGF14t(AAB62225),AtGF14w(AAA96252),AtGF14l (AAD51784),AtGF14i(AAK11271),AtGF14o(AAG 47840),AtGF14e(AAD51785),AtGF14p(NP_565174), OsGF14a(AAO72553),OsGF14b(AAB07456),OsGF14c (AAB07457),OsGF14d(AAB07458),OsGF14e(CAB 77673),OsGF14f(AAX95656),OsGF14g(BAD73105), OsGF14h(ABA94733)and human theta(NP_006817). The cDNA accession numbers of soybean14-3-3proteins are shown in Table1.cDNA and predicted protein sequences for transcribed soybean SGF14s were used for sequence alignment and phylogenetic analysis. Quantitative RT-PCRTotal RNA was isolated from soybean tissues following the procedure of Wang and Vodkin(1994),quantified using a nanodrop1000(Bio-Rad Laboratories,Inc.),and concen-tration and integrity of RNA checked.RNA sample from each tissue was treated with RQ1RNase-Free DNase I (Promega,Madison,WI)at37°C for30min prior to RT-PCR.DNase I-treated RNA samples were extracted with phenol:chloroform,precipitated with ethanol and re-quan-tified using nanodrop1000.Total RNA(4l g)from each sample was used for reverse transcription using Thermo-Script TM RT-PCR system(Invitrogen)according to manu-facturer’s instruction.Quantitative PCR was conducted using QuantiTectÒSYBR Green PCR(Qiagen Inc.,Miss-issauga,ON,Canada)in a standard PCR reaction according to manufacturer’s instructions using CFX96real-time PCR detection system(Bio-Rad Laboratories,Inc.).A negative control without cDNA template was included for each pri-mer combination.Soybean ubiquitin-3(SUBI-3)gene transcript does not vary among different tissues and was used as a reference gene for data normalization and to calculate the relative mRNA levels(Fig.S1,Kim et al. 2005;Trevaskis et al.2002).The primers used to amplify SGF14genes and their amplicon sizes are shown in sup-plementary Table S2.PCR efficiency for each primer set was determined by standard curve method(Table S2).At least two biological replicates and three technical replicates for each biological replicate were used.Data analysis was performed by using Bio-Rad CFX manager(Bio-Rad Laboratories,Inc.).A melt curve analysis was performed where a single peak was detected for each SGF14amplicon.Subcellular localization of soybean14-3-3genesThe subcellular localization study was conducted using Bimolecular Fluorescence Complementation(BiFC)assay where splitfluorescent protein segments are brought together to form a functionalfluorophore as a result of protein–protein interaction.Each SGF14gene cloned into pENTR/D-TOPO vector was recombined with Gateway BiFC vectors pEarly-gate201-YN and pEarlygate202-YC(Lu et al.2010),that contains N-terminal(1–174amino acids)and C-terminal (175–239amino acids)of eYFP,in separate reactions using LR clonase reaction mix(Invitrogen,Carlsbad,CA,USA)and transformed into E.coli strain DH5a.The recombined plas-mids were extracted and sequenced to confirm the sequence integrity.Each plasmid DNA was transformed into Agro-bacterium tumefaciens strain GV3101via electroporation. Cotransformation of A.tumefaciens carrying SGF14gene cloned into each of the pEarlygate201-YN and pEarlygate202-YC vectors and transient expression in N.benthamiana leaves was conducted according to Sparkes et al.(2006).Epidermal cell layers of N.benthamiana leaves were assayed forfluo-rescence2–3days after infiltration.Imaging of YFP was performed by a Leica TCS SP2inverted confocal microscope using a639water immersion objective and Leica Confocal software at an excitation wavelength of514nm,and emis-sions were collected between530and560nm.The experi-ment was repeated at least three to four times for each gene.ResultsIdentification of soybean14-3-3gene family and their phylogenetic relationshipsThe soybean EST database at DFCI gene index containing 1315705ESTs represents72042tentative contig(TC) sequences and62990singleton ESTs.These TC and single-ton sequences were used as an initial source to identify potential14-3-3genes by keyword search.This process identified94sequences including40singletons which were then used as query sequences to perform BLAST search against soybean genome database to obtain14-3-3genes.The search revealed that many of the TCs were highly similar and/ or corresponded to a different region of the same gene.Our exhaustive data mining identified1814-3-3genes in soybean genome which were located in12different chromosomes as shown in Table1.Out of18,1614-3-3s(SGF14a to SGF14p) have ESTs/TCs with high sequence identity ranging from96 to100%at nucleotide level.Even though SGF14q(in chro-mosome7)and SGF14r(in chromosome20)showed85% similarity with TC302191(Table1),the identical residues were dispersed through out the sequence as revealed by their sequence alignment(Fig.1).Table1provides the detail information on all soybean SGF14genes.The SGF14genes in soybean encode proteins with calculated molecular masses ranging from28.2to30.5kDTable1Soybean14-3-3gene informationGene name cDNAaccession#Protein size(amino acids)Chromosome number:gene location aCorresponding TC#(%identity)bSGF14a HM004359257Gm18:61885459–61888618(?strand)TC336378(100) SGF14b AK285530250Gm04:8085999–8088679(?strand)TC296072(97) SGF14c HM004361259Gm05:34754666–34758625(?strand)TC321441(98) SGF14d AK285774/U70536261Gm13:37943326–37946655(?strand)TC325659(96) SGF14e HM004360259Gm01:7614430–7618123(?strand)TC302191(100) SGF14f HM004358259Gm02:11188826–11193147(?strand)TC279559(100) SGF14g BT096871262Gm02:42460325–42462765(?strand)TC289097(99) SGF14h HM004357259Gm04:9057040–9059334(-strand)TC299321(100) SGF14i AK286671259Gm06:8046858–8049172(-strand)TC299321(97) SGF14j AK285891250Gm06:7426323–7428626(?strand)TC298321(100) SGF14k AK286798261Gm14:44375153–44377652(?strand)TC298759(99) SGF14l AK286414259Gm08:8884778–8887873(?strand)TC321441(97) SGF14m AK286318257Gm08:46677649–46680782(-strand)TC331886(100) SGF14n HM004356265Gm12:38944080–38948239(-strand)TC284291(99) SGF14o AK286943/AK244317261Gm12:36987099–36990513(-strand)TC325659(100) SGF14p AF228501263Gm13:36124809–36128694(?strand)TC277945(100) SGF14q–261Gm07:40343251–40346828(-strand)TC302191(85) SGF14r–261Gm20:2852621–2859029(-strand)TC302191(85)a Chromosome location indicates the position of each gene in chromosomeb The tentative contig(TC)number with the highest homology to target SGF14gene,the identity is shown in the bracketand estimated pI ranging from4.56to4.83.An alignment of deduced SGF14isoforms from soybean indicated that the amino acid sequences are highly conserved except at the N-terminal and C-terminal regions(Fig.2).The sequence conservation among SGF14proteins was also supported by the percentage identity at amino acid and nucleotide level that vary from58to99%and58to97%, respectively(Table S3).Fig.1Nucleotide sequence alignment of SGF14q,SGF14r and TC302191.Identical residues are shown in asterisks. Hyphen indicates a gapA phylogenetic analysis was conducted on all tran-scribed14-3-3s from soybean,Arabidopsis and rice using both protein and cDNA matrices.As shown in Fig.3, SGF14b,SGF14j SGF14m,SGF14a,SGF14g,SGF14k, SGF14h and SGF14i grouped together with the Arabid-opsis and rice non-epsilon isoforms while SGF14o, SGF14d,SGF14c,SGF14l,SGF14n,SGF14p,SGF14e and SGF14f formed a branch together with the Arabidopsis and rice epsilon-like isoforms.The analysis also grouped two SGF14s with sequence identity96%or higher into a dis-crete clade for example SGF14p and SGF14n(Fig.3). Further,inclusion of all14-3-3s from Arabidopsis and rice in the phylogenetic tree of soybean14-3-3s,established the evolutionary relationships of14-3-3proteins from these three different species that are consistent with the species evolutionary history.The presence of highly similar14-3-3s in these plant species could have resulted from their whole genome duplication event.The comparativeanalysisof14-3-3s in Arabidopsis,rice and soybean suggested that Arabidopsis14-3-3s experienced independent gene duplication(e.g.,AtGF14m and AtGF14t,AtGF14v and AtGF14u,AtGF14j and AtGF14k)after its separation from soybean(Fig.3).One such14-3-3gene duplication occurred in rice which generated OsGF14b and OsGF14e. Soybean14-3-3gene structure comprised4and6exons in non-epsilon and in epsilon group,respectively(Fig.4).The gene structure analysis revealed that the two SGF14genes that formed a discrete clade in the phyogenetic tree(such as SGF14p and SGF14n)consisted of mostly similar size of exons but differed in the size of their introns.Soybean14-3-3isoforms are regulated differentiallyTo determine if all1814-3-3genes are expressed in soy-bean,we designed gene-specific primers for each SGF14s and performed RT-PCR using pooled cDNAsamplesprepared from different soybean tissues at various stages of development.The results showed that except for SGF14q and SGF14r,all other SGF14genes were transcribed in soybean.Search for ESTs corresponding to SGF14q and SGF14r in the EST database identified TC302191with 85%identity with both SGF14q and SGF14r(Table1). TC302191also displays100%identity with SGF14e. Therefore,we conclude that despite1814-3-3genes being present in soybean genome,only16isoforms are transcribed.To evaluate the tissue-specific expression of14-3-3 genes in soybean tissues during development,we per-formed quantitative RT-PCR using gene-specific primers. As shown in Fig.5,SGF14genes were expressed ubiqui-tously in all the tissues except for SGF14p which was not expressed in root tissue.The transcript levels and expres-sion profiles of SGF14isoforms varied depending on tissue type and developmental stage in soybean.All the SGF14 genes were expressed to higher level in embryo during seed development compared to other tissues with the exception of SGF14p,SGF14n and SGF14j whose expression levels were higher in other tissues.Furthermore,expression level of majority of soybean SGF14genes were higher during early seed development(30DAP)followed by a gradual decrease in the level as the embryos approached maturity excluding SGF14m(Fig.5).Comparison of relative tran-script levels of SGF14genes in different tissues showed that SGF14b,SGF14n,SGF14j,SGF14d,SGF14i and SGF14g were expressed at higher levels in leaf compared to other vegetative tissues with SGF14j level being the highest.Soybean14-3-3transcripts accumulated to rela-tively lower level in roots compared to other tissues.The qPCR analysis detected relatively higher levels of SGF14n and SGF14p transcripts in pod wall and SGF14m,SGF14p and SGF14k transcripts in seed coat tissues.However,no expression was detected for SGF14n and SGF14p tran-scripts in soybean root.Apart from SGF14m and SGF14d, the expression of other SGF14s was relatively lower level in stem compared to other tissues included in the study. The expression patterns for those SGF14s that formed same clade in phylogenetic tree(Fig.3)generally showed sim-ilar tissue-specific expression pattern except for SGF14a and SGF14m.Subcellular localization of soybean14-3-3proteinsThe principle of BiFC is based upon bringing together split fluorescent protein to form a functionalfluorophore.Theassociation of the splitfluorescent protein molecule does not producefluorescence spontaneously and requires interaction between proteins that are fused to each of the fluorophore fragments.The protein–protein interaction of fused proteins results in the association between the split fluorophore fragments leading to formation of afluorescent protein that has the same spectral properties as the unsplit fluorescent protein(Ohad et al.2007).To investigate the subcellular localization of soybean SGF14s and to ensure that the observed localization is a result of their homodi-merization and not due to the interference from tobacco 14-3-3s,we performed BiFC assay using split yellow fluorescent protein(YFP).A translational fusion of each SGF14gene from soybean was created with N-terminal (YN)and C-terminal(YC)half of YFP under the control of CaMV35S promoter.YN and YC constructs for each SGF14were coexpressed transiently in tobacco epidermal cells by Agrobacterium-mediated transformation and pro-tein expression was monitored by confocal microscopy. Control experiments were performed by coexpressing SGF14-YN or YC fusion for each14-3-3protein with non-fusion half of YFP or coexpressing vector only YN and YC.Thefluorescence detection parameters for YFP observation were kept consistent for the entire SGF14 localization experiment.No signals were detected for negative controls(data not shown).Our results indicated that all SGF14s can form homodimer.The14-3-3s with highest sequence identity that form separate clade in the phylogenetic tree(Fig.3)showed similar subcellular localization pattern(Fig.6).The assay confirmed cyto-plasmic localization of all soybean14-3-3s.In tobacco epidermal cell,the cytoplasmicfluorescence typically looks like thin area between the cell wall and the turgescent vacuole(Walter et al.2004).All the SGF14s were also localized in nucleus.However,the pattern and intensity of SGF14expression in the nucleus varied in different iso-forms.SGF14p and SGF14n YFP signals were strong in nucleus compared to other14soybean SGF14s.No SGF14 were found in nucleolus.There was no overlap of YFP signal with chloroplast autofluorescence suggesting that SGF14s may not be present in chloroplast.Thefluorescent protein usually shows network structure when targeted to endoplasmic reticulum(ER)(Dhaubhadel et al.2008). Absence of such network structure of SGF14fused YFP in the cell suggests that SGF14s may not be localized in ER. Soybean14-3-3proteins can form heterodimerIt has been well documented that14-3-3proteins form both homo-and heterodimers(reviewed in MacKintosh2004). The presence of16expressed14-3-3s in soybean could generate a large number of heterodimers.In order to investigate if soybean SGF14proteins form heterodimer and how the heterodimerization affects their subcellular localization,we selected representative SGF14genes that are either closely related such as SGF14m and SGF14a or distantly related such as SGF14m and SGF14n and per-formed BiFC assay using the translational fusion of YN with YC with different SGF14s.The resultsdemonstratedthat SGF14s hereodimerizes with its isoforms,irrespective of the sequence similarity (Fig.7).However,their sub-cellular localization differed depending on each monomer involved in dimerization.No change in subcellular location was observed for the closely related SGF14heterodimers (Fig.7,SGF14c/SGF14l,SGF14n/SGF14p and SGF14m/SGF14a).Although,the YFP signal for the heterodimers was detected in both cytoplasm and nucleus for the SGF14s belonging to two different classes that lie far apart in the phylogenetic tree (SGF14m/SGF14n and SGF14m/SGF14p),the intensity of signal differed from their homodimer status (compare Figs.6and 7).Discussion14-3-3proteins are encoded by a large multigene family in plants.Arabidopsis and rice genome consist of 15and 814-3-3genes,respectively (Rosenquist et al.2001;Yao et al.2007b ).A large number of 14-3-3s have also been identi-fied in tomato and tobacco and additional 14-3-3genes are expected to be identified as full genome sequence of other organisms becomes available.We have identified 1814-3-3genes,SGF14a -SGF14r ,in soybean genome (Schmutz et al.2010),of which 16SGF14s are transcribed.Soybean is an allotetraploid and its genome is much larger (1,115Mb)compared to Arabidopsis (145Mb)and rice (420Mb)(Arumuganathan and Earle 1991).Soybean genome has undergone at least two whole genome dupli-cations events approximately 14and 42million years ago (MYA)(Blanc and Wolfe 2004;Gill et al.2009;Schlueter et al.2004;Shoemaker et al.1996)while the whole gen-ome duplications in Arabidopsis and rice genomes occur-red about 100–200and 60–70MYA,respectively (Blanc et al.2000;Yu et al.2005).The genome duplication event usually results in rearrangement and reshuffling of chro-mosomal regions creating the potential for new diversity and possibility of subfunctionalization (Force et al.1999).Analysis of soybean SGF14gene sequences show very high sequence identity between two subgroups of SGF14s that form a distinct clade in the phylogenetic tree,however,these genes are found to be located in different chromo-somes (Table 1;Fig.3).The observation of two distinct subgroups of closely related SGF14family members such as SGF14n and SGF14p,SGF14o and SGF14d supportstheFig.6Subcellular localization of SGF14using BiFC assay.N.benth-amiana leaves were co-transformed with SGF14gene constructs fused N-and C-terminally to YFP followed by confocal microscopy.The YFP signal in nucleus and/or cytoplasm resulted from homodi-merization of same SGF14isoform.Scale bars are shown in l m.The arrowhead with a ,b and c indicate nucleus,cytoplasmic streaming and cell periphery,respectivelyc。

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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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。