Weed Science 2007 55:193–203

Characterization of an EST Database for the Perennial Weed Leafy Spurge: An Important Resource for Weed Biology Research James V. Anderson, David P. Horvath, Wun S. Chao, Michael E. Foley, Alvaro G. Hernandez, Jyothi Thimmapuram, Lie Liu, George L. Gong, Mark Band, Ryan Kim, and Mark A. Mikel* Genomics programs in the weed science community have not developed as rapidly as that of other crop, horticultural, forestry, and model plant systems. Development of genomic resources for selected model weeds are expected to enhance our understanding of weed biology, just as they have in other plant systems. In this report, we describe the development, characteristics, and information gained from an expressed sequence tag (EST) database for the perennial weed leafy spurge. ESTs were obtained using a normalized cDNA library prepared from a comprehensive collection of tissues. During the EST characterization process, redundancy was minimized by periodic subtractions of the normalized cDNA library. A sequencing success rate of 88% yielded 45,314 ESTs with an average read length of 671 nucleotides. Using bioinformatic analysis, the leafy spurge EST database was assembled into 23,472 unique sequences representing 19,015 unigenes (10,293 clusters and 8,722 singletons). Blast similarity searches to the GenBank nonredundant protein database identified 18,186 total matches, of which 14,205 were nonredundant. These data indicate that 77.4% of the 23,472 unique sequences and 74.7% of the 19,015 unigenes are similar to other known proteins. Further bioinformatics analysis indicated that 2,950, or 15.5%, of the unigenes have previously not been identified suggesting that some may be novel to leafy spurge. Functional classifications assigned to leafy spurge unique sequences using Munich Information Center for Protein or Gene Ontology were proportional to functional classifications for genes of arabidopsis, with the exception of unclassified or unknowns and transposable elements which were significantly reduced in leafy spurge. Although these EST resources have been developed for the purpose of constructing high-density leafy spurge microarrays, they are already providing valuable information related to sugar metabolism, cell cycle regulation, dormancy, terpenoid secondary metabolism, and flowering. Nomenclature: Leafy spurge, Euphorbia esula L. EPHES; arabidopsis, Arabidopsis thaliana (L.) Heynh. Key words: Expressed sequence tag, genomics, leafy spurge, perennial weeds.

The era of genomics and bioinformatics has increased our knowledge of plant genome structure and organization, gene function, marker development, plant physiology, and genetics. The recent explosion of genetic and genomic data for a wide range of plant and animal species has led to a proliferation of publicly available information databases throughout the internet (http://www.ncbi.nlm.nih.gov/dbEST, http://arabidopsis. org, http://rgp.dna.affrc.go.jp). In addition, complete plant genomes are available for arabidopsis (The Arabidopsis Genome Initiative 2000) and rice (Oryza sativa L.; International Rice Genome Sequencing Project 2005). EST databases, which are composed of partial sequences (200 to 800 base pairs [bp]) of expressed genes, compose a large portion of these publicly available DNA sequence databases (Ohlrogge and Benning 2000). To date, at least 34 gene indices for plants are publicly available (Quackenbush et al. 2001; http://compbio.dfci.harvard.edu/tgi/plant/html) and other transcript assembles are available for over 200 species (http:// plantta.tigr.org) that integrate data from international EST sequencing, genome sequencing, and gene research projects. Historically, the weed science community has not received the attention or funding needed to sustain genomics programs similar to those of crops or other model plants (e.g., arabidopsis). As a consequence, weed genomics initiatives are currently at the grassroots stage of development, and consensus within the weed science community suggests that DOI: 10.1614/WS-06-138.1 * First through fourth authors: USDA-ARS, Biosciences Research Laboratory, 1605 Albrecht Boulevard, Fargo, ND 58105; fifth through tenth authors: University of Illinois, W. M. Keck Center for Comparative and Functional Genomics, 1201 West Gregory Drive, Edward R. Madigan Laboratory, Urbana, IL 61801; University of Illinois, Department of Crop Sciences and Roy J. Carver Biotechnology Center, 1206 West Gregory Drive, 2610 Institute for Genomic Biology, Urbana, IL 61801. Corresponding author’s E-mail: [email protected]

some weed species representing perennial and annual grassy and broad-leafed classes should be promoted as models (Basu et al. 2004; Chao et al. 2005). Although extensive studies have been carried out on a few model species, little is still known about basic physiological processes and signal transduction pathways that control growth, development, dormancy, and biotic and abiotic stress resistance in weedy species. Development of genomic resources (i.e., EST databases, microarrays, and genome sequences) for selected model weed species would enhance our ability to answer fundamental questions about how weeds survive and adapt to natural and manmade stresses and about what evolutionary or environmental changes cause them to become invasive. Many of these mechanisms could be exploited to enhance adaptability of domestic crops to environmental stress. Leafy spurge is currently being used as a model for the study of perennial broadleaf weeds (Chao et al. 2005). Leafy spurge is an invasive perennial weed causing economic losses to range, recreational, and right-of-way lands in North American plains and prairies (Bangsund et al. 1999; Leitch et al. 1996). The perennial nature of leafy spurge is attributed to vegetative propagation from an abundance of underground adventitious buds (more commonly referred to as crown and root buds). Dormancy-imposed inhibition of new shoot growth from crown and root buds is one of the key characteristics leading to the persistence of perennial weeds like leafy spurge (Coupland et al. 1955). Although biocontrol programs have been successful at reducing the spread of leafy spurge in some ecosystems, natural enemies are not successful in all ecosystems. In addition, seemingly eradicated patches of leafy spurge are able to regenerate through seeds which can remain dormant in the soil bank for up to 8 yr (Chao and Anderson 2004). The development of genomic resources for leafy spurge has been an ongoing project for the last 5 to 10 yr. In addition to Anderson et al.: An EST database for leafy spurge

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the development of cDNA and genomic libraries, a small-scale EST project was initiated that resulted in identification of a limited set of sequences (Anderson and Horvath 2001) and low-density microarrays (Horvath et al. 2005, 2006). EST databases are important for identifying expressed genes that can be used for developing high-density DNA microarrays (Richmond and Sommerville 2000). cDNA microarray technology, first developed by Schena et al. (1995), has resulted in an explosion of scientific papers related to transcriptome expression (Marshall 2004). For example, in plants, this technology has been used to identify specific gene functions (Aharoni et al. 2000; Gutierrez et al. 2002), evaluate transcript profiles during development (Kloosterman et al. 2005), study well-defined phases of dormancy in various tissues (Cadman et al. 2006; Schrader et al. 2004) or responses to various physiological or environmental conditions (Lee et al. 2002; Oztur et al. 2002; Potokina et al. 2002; Reymond et al. 2000; van Hal et al. 2000; Zhu et al. 2003), and evaluate transcript profiles from genetically modified species (van Hal et al. 2000). However, because few large-scale EST projects have been undertaken for non–crop-related weeds, the development of high-density microarrays for model weedy species that could be used for transcriptome analysis similar to those described above has so far been limited. In addition to EST projects, full genome sequencing projects for many plant species are underway and are expected to affect future plant research. For example, the use of coordinately expressed genes identified by microarray analysis in combination with genome sequences that include noncoding portions of these genes would provide a mechanism for detecting regulatory sequences such as short transcription factor binding sites shared among coordinately regulated gene clusters. Such an approach can be a powerful tool for identifying the specific genes and signaling components responsible for sensing environmental or developmental cues involved in regulating specific phases of weed growth and development or responses to stress and other factors. In an attempt to develop more robust microarrays, including genes expressed in multiple tissues at different developmental stages or under different environmental conditions, and to increase the potential of developing leafy spurge as a model for perennial weed studies (Chao et al. 2005), we have greatly increased the number of unigenes within our leafy spurge EST database. In this report, we describe the generation of an EST database and its bioinformatic analysis, and we discuss how this resource might benefit other researchers studying specific physiological responses in leafy spurge and related species. Methods and Materials

Library Development. Plant tissues used to initiate this project originated from three sources that included (1) outdoor garden plants located at the USDA/ARS, Biosciences Research Laboratory in Fargo, ND, (2) wild plants from a population located , 1.6 km (1 mile) north of the Biosciences Research Laboratory, and (3) greenhouse-grown plants. Leafy spurge (biotype 1984-ND001) plants maintained in a greenhouse were propagated as previously described (Anderson and Davis 2004). Outdoor garden plants located at the laboratory originated from a portion of the leafy spurge greenhouse population that was transplanted in 1998. 194

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From the garden plot, leaf, stem, and meristem were collected every 2 wk from August 22, 2003, through October 31, 2003, which represents a period of senescence; seeds were collected August 20, 2003; and crown buds were collected monthly from January 15, 2002 through September 10, 2003. Crown buds collected over this 21-month period experienced environmentally stressful conditions, including cold, freezing, dehydration, and heat. From the wild population, shoots, immature and mature flowers, galls resulting from Spurgia esulae (an introduced biocontrol agent of leafy spurge that lays eggs in the flowers, resulting in a gall that interferes with flowering/seed production), and mature and developing seed pods with seeds were collected in June 2003. From greenhouse-grown plants, crown and root buds of 3- to 4mo-old plants were collected at 0, 1, 2, 3, 4, and 5 d after decapitation of aerial tissue, which breaks paradormancy in greenhouse-grown leafy spurge root and crown buds (Horvath et al. 2005). Greenhouse-grown plants were also used to obtain vernalized leaf, stem, and crown buds from 3- to 4-moold plants that were collected at 0, 1, 2, 4, 5, 7, 14, 15, 21, 35, 55, and 90 d during incubation at , 4 to 7 C. Vernalization was accomplished with the use of an incubator1 with an 8 : 16 h light : dark photoperiod. Light intensity during vernalization was 50 photosynthetically active radiation. Library Construction. Total RNA and mRNA Isolation. Total RNA was extracted separately from each ground tissue sample by the ‘‘pine tree’’ method (Chang et al. 1993). The integrity and purity of total RNA was verified by denaturing agarose gels and by spectrophotometry (ratio A260/280). Total RNA from different samples was pooled in approximately equal amounts before isolation of mRNA. Poly(A)+mRNA was isolated twice from total RNA from each tissue with the Oligotex Direct mRNA kit.2 cDNA Synthesis. Reverse transcription of mRNA into doublestranded cDNA was accomplished by the SuperScript Choice System3 with a NotI/oligo(dT) primer [59-AACTGGAAGAATTCGCGGCCGCACGCA(T)18V-39]. Directional Cloning. Double-stranded cDNAs of $ 450 bp were selected by agarose gel electrophoresis. EcoRI adaptors (59-AATTCCATTGTGTTGGG-39) were ligated to the cDNAs, followed by digestion with Not I. The cDNAs were then directionally ligated into EcoRI–Not I-digested pBluescript II SK(+) phagemid vector.4 Ligated cDNAs were electroporated into DH10BTM cells.5 Titers and Inserts Size. The total number of white colonyforming units (cfu) in the primary library before amplification was 1.0 3 106. More than 99% of the clones were recombinant. The average insert size of random clones on the basis of polymerase chain reaction (PCR) was 1.2 kilobase pairs. Library Normalization. Normalization was performed following the procedure of Bonaldo et al. (1996). Briefly, purified plasmid DNA from the primary library was converted to single-stranded circles by digestion with GeneII and Exonuclease III and used as a template for PCR amplification with the use of the T7 and T3 priming sites

flanking the cloned cDNA inserts. The purified PCR products, representing the entire cloned cDNA population, were used as a driver for normalization. Hybridization between the single-stranded library and the PCR products were carried out for 44 h at 30 C. Nonhybridized singlestranded DNA circles were separated from hybridized DNA by hydroxyapatite, rendered partially double-stranded by primer extension with the use of standard M13 reverse primer, and electroporated into DH10B cells to generate the normalized library. The total number of clones with insert was 6 3 106 cfu and . 99% of the clones were recombinant. Library Subtraction. Subtraction was carried out as previously described (Bonaldo et al. 1996). Briefly, purified DNAs from previously sequenced clones were pooled and used as template for PCR amplification with standard T7 and T3 primers. The purified PCR products were used as a driver for subtraction. Purified plasmid DNA from the normalized library was converted to single-stranded circles by digestion with GeneII and Exonuclease III and used as a tracer for the subtraction reaction. Hybridization between the singlestranded tracer library and the PCR products was carried out for 88 h at 30 C. Nonhybridized, single-stranded DNA circles were separated from hybridized DNA by hydroxyapatite, rendered partially double-stranded by primer extension with standard M13 reverse primer, and electroporated into DH10B cells to generate the subtracted library. Subtractions were carried out when overall redundancy approached 40%. In all, three subtractions were performed: subtracted library 1, with the use of , 12,000 clones sequenced from the normalized library as driver; subtracted library 2, with , 25,000 clones sequenced from the first subtracted library as driver; and subtracted library 3, from which , 39,000 previously sequenced clones were used as driver. The total number of clones with inserts from the first subtraction was 3.5 3 106 cfu ml21 and . 98% of the clones were recombinant. In the second subtraction, the titer was 5 3 106 cfu ml21, and empty clones made up 4% of the library. In the third subtraction, the titer was 107 total cfu, and the background of empty clones was , 4%. DNA Sequencing. The libraries were plated on agar and individual colonies were picked by a Genetix Q-Pix robot6 and racked as glycerol stocks in 384-well plates. After overnight growth of the glycerol stocks, bacteria were inoculated into 96well deep cultures with Luria Bertani medium and carbenicillin (100 mg ml21). Plasmid DNA was purified from the bacterial cultures after 24 h of growth at 37 C by Qiagen 8000 and Qiagen 9600 robots.7 Sequencing reactions were performed with standard T7 primer (providing sequence for the 59 end of cloned inserts only) and ABI BigDye terminator chemistry on ABI 3730XL capillary systems.8 Sequence Processing Protocol. EST sequences were processed in batch by the bioinformatics pipeline developed in the Bioinformatics Unit at the W. M. Keck Center, University of Illinois, Urbana-Champaign. The steps in the pipeline are described as follows. After base-calling by the PHRED program (Ewing et al., 1998; Ewing and Green, 1998), the Cross-match program (http://bozeman.mbt.washington.edu/ phrap.docs/phrap.html), and Qualtrim script (a quality control script developed by the Bioinformatics Unit) were

used to trim off vector sequences, ambiguous regions, and user-defined sequence patterns (see Table 1 for definitions and links to genomics terms used). Sequences of . 200 bp in length after processing were considered useful for further analysis. A report summarizing success rates and quality scores were generated for each plate. Subsequently, sequences were screened for contaminated sequences, such as bacterial chromosomal DNA, viral DNA or RNA, rRNA, mitochondria, and chloroplast DNA with the BLASTN program (Altschul et al., 1990). Contaminated sequences were excluded from the final set of ‘‘cleaned’’ sequences. Repeats and low-complexity sequences were screened by RepeatMasker program. (The cleaned sequences, their associated features, and their original raw sequences were stored in SeqDB, an inhouse Oracle database developed at the Bioinformatics Unit, and could be accessed through a web-based system, the Genome Project Management System (Liu et al. 2000), also developed by the Bioinformatics Unit.) Protocol for Clustering, Assembling, and Assessing Redundancy. The final clean sequences were used for clustering and assembly by Paracel TranscriptAssembler (PTA). EST sequences were clustered on the basis of local similarity of pairwise comparison with 88% identity over 100 nucleotides. Clusters containing only one sequence are called singlets. The clusters were then assembled into contigs (contiguous sequence) by the CAP4 program, which is an improved version of CAP3 (Huang and Madan 1999) with the criteria of 95% identity and a minimum overlap of 30 nucleotides. A consensus sequence for each contig was generated. The sequences in a cluster that could not be assembled into contigs are called cluster_singlets. The redundancy is calculated as {1 2 [(no. of contigs + no. of cluster_singlets + no. of singlets)/total no. of sequences]} 3 100. Results and Discussion

EST Database Characterization. The use of a normalized whole-plant leafy spurge cDNA library in combination with three library subtractions and a sequencing success rate of 88% yielded 45,314 clean ESTs with an average read length of 671 nucleotides (Table 2). Overall, our data indicate that the leafy spurge EST database assembled into 23,472 unique sequences (10,436 contigs, 4,314 cluster_singlets, and 8,722 singlets) that represent 19,015 unigenes (10,293 clusters and 8,722 singlets). It has been estimated that arabidopsis contains , 26,000 genes (The Arabidopsis Genome Initiative 2000), and rice has as many as 37,000 genes (International Rice Genome Sequencing Project 2005). Thus, our EST database could potentially contain between 60 and 90% of the genes present in leafy spurge. The high number of coding sequences demonstrates the efficiency and cost effectiveness of ESTbased studies for initial genome analysis. The increased number of unique sequences compared with unigenes can be explained by several facts: During assembly of clusters, some will not form any contigs and include only cluster_singlets because these singlets do not overlap, and some clusters have multiple contigs, with or without cluster_singlets. Thus, the number of contigs + cluster_singlets will not add up to the number of clusters. Multiple contigs within a single cluster can be due to a lack of sequence Anderson et al.: An EST database for leafy spurge

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Table 1. A list of terms, definitions, and website resources commonly used in genomics research. Term

Definition

Website

References

BLAST

Basic Local Alignment Search Tool. Finds regions of local similarity between sequences.

http://www.ncbi.nlm.nih.gov/BLAST/

Clustering and assembly Cluster

Paracel Transcript Assembler using CAP4 program generates putative transcripts. A set of sequences grouped together on the basis of pairwise comparison, clone name similarity, or both. Sequences in a cluster that could not be assembled into contigs are called cluster_singlets. A contiguous sequence based on overlapping regions of ESTs in a given cluster. A program for comparing any two sets of DNA sequences.

No web site available

Altschul et al. 1990; McGinnis and Madden 2004 Huang and Madan 1999

No web site available

Huang and Madan 1999

No web site available

Huang and Madan 1999

No web site available

Huang and Madan 1999

http://bozeman.mbt.washington.edu/ phrap.docs/phrap.html http://www.ncbi.nlm.nih.gov/dbEST/

NA. Website has documentation Boguski et al. 1993

http://www.geneontology.org/

Gene Ontology Consortium 2004

http://www.ncbi.nlm.nih.gov/Genbank/

Benson et al. 2005

http://mips.gsf.de/

Mewes et al. 2002

http://www.ncbi.nlm.nih.gov/

Wheeler et al. 2004

http://www.phrap.org/phredphrap/phred.html NA

Ewing et al. 1998; Ewing and Green 1998 Kumar et al. 2004

http://www.repeatmasker.org/

Smit et al. 1996–2004

No web site available http://godot.ncgr.org/

Huang and Madan 1999 Garcia-Hernandez et al. 2002 NA

Cluster_singlet Contig Cross_Match dbEST

GO

GenBank MIPs

NCBI

Phred QualTrim RepeatMasker Singlet TAIR TIGR

Expressed Sequence Tag database. A division of GenBank that contains sequence data and other information on ‘‘single-pass’’ cDNA sequences from a number of organisms. Gene Ontology. Provides a controlled vocabulary to describe gene and gene products in terms of their associated biological processes, cellular components, and molecular functions in any organism. National Institutes of Health genetic sequence database, an annotated collection of all publicly available DNA sequences. Munich Information Center for Protein. Automatically generated and manually annotated genome-specific databases; develops systematic classification schemes for functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. National Center for Biotechnology Information. A national resource for molecular biology information to aid in the understanding of fundamental molecular and genetic processes. A base-calling program that examines DNA sequence traces and assigns a quality score to each base call. A script used to trim off vector sequences, ambiguous regions, and user-defined sequence patterns. A program that screens DNA sequences for interspersed repeats and low-complexity DNA sequences. Clusters containing only one sequence are called singlets. The Arabidopsis Information Resource. A database of genetic and molecular biology data for Arabidopsis thaliana. The Institute for Genomic Research. A not-for-profit center dedicated to deciphering and analyzing genomes.

Table 2. Summary report for leafy spurge expressed sequence tags (ESTs). Feature

Statistic

Clean ESTs Average EST length (nucleotides) Unique sequences 5 (contigs + cluster_singlets + singlets) Contigs Cluster_singlets Singlets Unigenes 5 (clusters + singlets) Clusters Singlets Clusters with . 1 contig Contigs with . 1 hit to nonredundant and arabidopsis protein database Contig ranges 201–500 nucleotides 501–1,000 nucleotides 1,001–1,500 nucleotides 1,501–2,692 nucleotides Clusters with . 50 ESTs Highest number of ESTs in a cluster

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45,314 671 10,436 4,314 8,722 total 5 23,472 10,293 8,722 total 5 19,015 723 58 870 6,357 2,739 470 5 79

http://www.tigr.org/

information to form an overlap, alternative splicing, gene families, or the presence of chimera in the clusters. For example, Figure 1 shows an example of two contigs (CV03_9.sd.6.C1.Contig10398 and CV03_9.sd.6.C2.Contig10399) from a single cluster that both translate into fulllength cyclophilins (cyclophilins belong to a gene family in arabidopsis). The two contigs have 66% identity at the nucleotide level, but 90% similarity at the amino acid level. Because assembly was not stringent (95% over 30 nucleotides) and the parameters used for clustering (88% over 100 nucleotides) and assembly were different, it is unlikely that chimera within a cluster caused ESTs to break away during assembly. In a BLASTX search of the GenBank nonredundant protein database (National Center for Biotechnology Information [NCBI]) a total of 18,186 out of 23,472 leafy spurge unique transcripts had at least one match, of which 14,205 were nonredundant at an expectation value (E-value) cutoff of 1025 (Figure 2). Only 28 of the unique leafy spurge sequences had matches to insect transcripts that likely occurred from including gall tissue resulting from Spurgia esulae during the development of the normalized cDNA library. Further BLAST searches, including arabidopsis pro-

Figure 1. Consensus nucleotide (nt) and predicted protein sequence (aa) for leafy spurge expressed sequence tag contigs CV03_9.sd.6.C1.Contig10398 and CV03_9.sd.6.C2.Contig10399. Translation start (ATG) and stop (TGA) codons are in bold text in each nucleotide consensus sequence. For protein sequences, methionine residues (M) are referenced in bold lettering.

tein (TAIR, The Arabidopsis Information Resource), Rice protein (NCBI), soybean UniGene (NCBI), Medicago UniGene (NCBI), Poplar UniGene (NCBI), Medicago_TCs (TIGR, The Institute for Genomic Research), and Poplar_TCs (TIGR) databases, identified 4,463 leafy spurge transcripts with no matches to other species (potential leafy spurge–specific transcripts). A BLASTN search of the 4,463 putative leafy spurge– specific transcripts against the plant dbEST database (NCBI) identified an additional 117 matches not found in other leafy spurge databases (Anderson and Horvath 2001; Chao et al. 2006). After adjusting for matches identified in dbEST and for 26 no-matches caused by low-complexity regions within the sequences, an estimated total of 4,320 putative leafy spurge–specific transcripts were identified that represent 3,733 unigenes (1,476 clusters and 2,257 singlets). In some cases, one or more of the sequences within these clusters had

BLAST matches to various species. As a result, only 801 clusters and 2,149 singlets have no matches to other plant species, leading to a conclusion that there are 2,950 sequences (15 to 16% of the total 19,015 unigenes) that, to our knowledge, are reported for the first time in leafy spurge. Although further verification is needed to support this conclusion, the 2,950 leafy spurge unigenes identified for the first time in this report are similar to the 2,859 sequences that were found to be novel to rice (International Rice Genome Sequence Project 2005). On the basis of the identification of 23,472 unique transcripts from a total of 45,314 clean sequences (ESTs), our results suggest an overall redundancy rate of 48%. Ideally, ESTs generated from a total cDNA library should represent all the expressed genes in the tissue from which the library was constructed. However, because the expression level of each gene in a given tissue is different, it is difficult to capture rare Anderson et al.: An EST database for leafy spurge

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develop our leafy spurge EST database not only reduced the redundancy rate but also increased the overall cost-effectiveness of the project. These conclusions are supported by the data shown in Table 2, in which only 5 clusters contained . 50 ESTs and the highest number of ESTs found in any one cluster was 79.

Figure 2. BLASTX total and nonredundant matches obtained using 23,472 unique leafy spurge sequences and known genes from arabidopsis (Arabidopsis-TAIR.aa), rice (rice.aa), and all known genes accessions in the GenBank nonredundant amino acid database (nr.aa). All matches represent a stringency of E 25.

mRNAs from cDNA libraries. This problem also leads to redundant sequencing of some abundant clones, thereby affecting the efficiency and cost effectiveness of the EST approach (Bonaldo et al. 1996). To overcome the redundancy problem in large-scale cDNA sequencing projects, a cDNA library normalization approach is used to generate uniform abundances of cDNA classes within a library (Soares et al. 1994). This system has been adopted successfully in other systems. For example, normalized cDNA libraries developed from different human tissues and organs have proven effective in representing rare and lowabundance mRNAs (Bonaldo et al. 1996). Recently, ESTs generated from normalized cDNA libraries have provided comprehensive analyses of genes expressed in the model dicotyledonous plant arabidopsis (Asamizu et al. 2000b), trefoil (Lotus japonicus, Asamizu et al. 2000a), and wheat (Triticun aestivum, Ali et al. 2000). The strategy used to

Functional Classification and Gene Ontology. On the basis of the two fully sequenced genomes of plants (arabidopsis and rice), BLASTX results indicated that 23,472 unique leafy spurge sequences had greater homology to arabidopsis (17,711 total matches and 11,509 nonredundant matches) than to rice (16,523 total matches and 10,166 nonredundant matches) (Figure 2). This is not surprising because leafy spurge and arabidopsis are both dicots and thus are more closely related to each other than to rice, which is a monocot. However, , 23% of the unique transcripts identified in leafy spurge produced no matches with arabidopsis. Of the 17,711 unique leafy spurge transcripts with BLASTX matches to arabidopsis (Figure 2), 56.6% are categorized as unclassified proteins and 4.27% as classification not yet clear (Figure 3) on the basis of the Munich Information Center for Protein Sequences (MIPs) functional categorization (Mewes et al. 2002). For those genes that have been classified in arabidopsis (Schoof et al. 2002), our unique leafy spurge transcripts had the greatest matches with genes classified in categories of metabolism (6.92%) and localization (5.03%). Eight other classifications with matches ranging from , 2 to 3% included transcription, protein synthesis, protein fate, protein binding function, cellular transport, cellular communication/signal transduction, cell rescue/defense and virulence (including stress responses), and biogenesis of cellular components. Four categories had hits ranging between 1 and 2% and included energy, cell cycle and DNA processing, cell fate, and development. The remaining seven categories shown in Figure 3 had hits with , 1%, with the lowest number of matches categorized as transposable elements.

Figure 3. Classification of leafy spurge sequences on the basis of Munich Information Center for Protein Sequences functional classification of arabidopsis. Each piece of the pie chart represents percentage of leafy spurge genes that had matches at a stringency of E 25.

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Figure 4. Comparison of Munich Information Center for Protein Sequences functional classifications between leafy spurge unique sequences and genes of arabidopsis.

The results described above are reasonably proportional to the functional categorization of genes from arabidopsis with several exceptions (Figure 4). For example, transcripts categorized as ‘‘unclassified proteins’’ in leafy spurge were significantly reduced compared with arabidopsis (11,862 vs. 19,650, respectively). Leafy spurge transcripts categorized as ‘‘classification not yet clear-cut’’ and ‘‘transposable elements, viral and plasmid proteins’’ were also reduced compared with arabidopsis (Figure 4). Combined, these results suggest that although the distribution of gene functions in our library is fairly similar to arabidopsis, it is likely that some gaps in gene representation exist because of the choice of tissues used for library construction. Also, the comparison of transcripts (ESTs) vs. genes identified by whole-genome sequencing projects could explain the disproportional number of arabidopsis genes categorized as unclassified and transposable elements. This result could also reflect that very little genomics work has been done on perennials, and many genes perhaps have not been studied in other model organisms. It will be interesting to see what proportion of genes from the poplar genome project, for example, will be placed in these categories. Gene Ontology (GO) describes gene products in terms of their associated biological processes, cellular components, and molecular functions in a species-independent manner. A gene product might be associated with or located in one or more

cellular components, active in one or more biological processes, during which it performs one or more molecular functions (see www.geneontology.org for detailed documentation). We used BLAST results of our 23,472 unique leafy spurge sequences against the arabidopsis protein database to infer gene ontology terms for leafy spurge sequences (see supplemental data 1, http://www.ars.usda.gov/SP2UserFiles/Place/54420510/supp_ data_1.xls). Because not all leafy spurge sequences had homology to arabidopsis proteins and not all arabidopsis gene products are associated with a GO term yet, we do not have GO terms or annotation for all leafy spurge unique sequences. Each of these GO annotated sequences can be associated with more than one category or more than one GO term. For example, Contig7285 (CV03_9.6868.C2.Contig7285) can be described (annotated) by the molecular function term ATP binding, the biological process terms apoptosis and defense response, and the cellular component term endomembrane system. Thus, a given sequence might be present on one or more of the GO categories. Because GO annotation, curation, or both of the reference databases change with time, the GO annotation of leafy spurge is also expected to change as new terms and genes are added. Of the 17,711 leafy spurge sequences with BLAST hits to the arabidopsis protein database (Figure 2), 11,371 have GO terms associated with them. But almost half of them (5,427) Anderson et al.: An EST database for leafy spurge

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fell into the unknown category. Different GO categories of the leafy spurge transcripts classified are given in Figure 5. Similar to MIPs functional categories, leafy spurge was proportional to the GO of arabidopsis with the exception of ‘‘unknowns.’’ For example, , 27% of leafy spurge transcripts were listed under the GO term ‘‘biological process unknown’’ compared with the 48% listed under the same GO term for arabidopsis (data available at http://www.tigr.org/tigr-scripts/tgi/GO_browser. pl?species+arab&gi_dir5agi). Similarly, 26 and 2% of leafy spurge transcripts were listed under GO terms ‘‘cellular component unknown’’ and ‘‘molecular function unknown,’’ respectively (Figure 5), compared with the , 62 and 52% listed under the same GO terms for arabidopsis. Again, as previously mentioned, these results likely reflect differences associated with EST vs. whole-genome sequencing projects. Analysis of Repetitive Elements. Excessive representation of repetitive elements in a library can bias data mining and is indicative of a poor EST database. The leafy spurge unique sequences were analyzed for known interspersed repeats with RepeatMasker (Smit et al. 1996–2004) and TIGR’s plant repeat database as the custom library. Various repeat elements identified and the lengths they occupy are given in the Table 3 (a complete description of the repeat elements is provided in supplemental data 2, http://www.ars.usda.gov/ SP2UserFiles/Place/54420510/supp_data_2.xls). Only 1.7% of the total length of the unique sequences is represented in these repetitive elements, suggesting that transposons and other repetitive elements are not overrepresented in our database. Because we used an existing repeat database and the entire genome sequencing is not complete, the repeat content of the leafy spurge genome might be a low estimation. Current Benefits of the Leafy Spurge EST Database. As indicated above, our leafy spurge EST database contains a multitude of transcripts spread across a range of functional classifications. Some of these ESTs have already provided benefits to our understanding of growth and development in leafy spurge. For example, because of our interest in the role that sugar sensing plays in bud dormancy (Anderson et al. 2005; Chao et al. 2006) and growth inhibition (Chao et al. 2006; Horvath et al. 2002), ESTs representing transcripts coding for sugar-metabolizing enzymes have enhanced our understanding of pathways regulating sugar flux and signaling (Anderson et al. 2005), whereas other ESTs have provided further insights into cell cycle regulation (Sonju and Horvath 2005). Additionally important to the invasive nature of leafy spurge is seed production and dormancy. Because seemingly eradicated patches of leafy spurge can re-establish from dormant seeds located within the soil bank for up to 8 yr (Chao and Anderson 2004), an understanding of seed dormancy could contribute to the control of leafy spurge infestations. Numerous genes in the leafy spurge EST database with homologues to arabidopsis genes that are involved in germination and dormancy can be used as probes to enhance our understanding of seed dormancy. The leafy spurge EST database also contains a wealth of clones related to flower regulation, including FLOWERING LOCUS T-like (FT) and CONSTANS (CO), which interestingly have also been linked to regulation of growth cessation, bud set, and bud dormancy in trees (Bo¨hlenius et al. 2006). 200

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Release of the leafy spurge EST database to the public through NCBI (dbEST) has already generated interest from outside groups. For example, leafy spurge clones coding for casbene synthase are being used for the expression of fulllength gene products in engineered microbial hosts to study the production of diterpenoid precursors such as prostratin (12-deoxyphorbaol 13-acetate) and DPP (12-deoxyphorbol 13-phenylacetate) at the Keasling Lab at the University of California–Berkeley. These studies hold the potential of meeting the demand for a new HIV treatment. Ontology of Cross-Hybridizing Genes and Implications. Leafy spurge is a member of the genetically diverse Euphorbiaceae family that includes important crop, horticultural, weedy, and endangered species (see Anderson et al. 2004). For example, cassava (Manihot esculenta Crantz) is one of the most important human food crops in the world, whereas Castor bean (Ricinus communis L.) is a major source of castor oil and has gained attention because of the escalating threat of bioterrorism resulting from the production of ricin. Other important members of the family include rubber tree (Hevea brasiliensis Mu¨ll. Arg.); poinsettia (Poinsettia pulcherrima L.), which is a weed in some ecosystems; and endangered species such as Akoka (Chamaesyce spp.) and telephus spurge (Euphorbia telephioides Chapm.). The data we present in this report suggests that nearly 15 to 16%, or 2,950 unigenes, identified from this leafy spurge EST database have the potential to be classified as leafy spurge– specific. Future experiments with microarrays that include these leafy spurge unigenes will determine their hybridization efficiency with other Euphorbiaceae family members. For example, it will be interesting to see how many of these genes have the potential to be classified as ‘‘leafy spurge–specific’’ or ‘‘novel to leafy spurge’’ by not hybridizing to expressed genes in related Euphorbiaceae species such as cassava, castor bean, poinsettia, and rubber tree. Additionally, because a significant understanding of the conservation and diversity of genes between members of the Euphorbiaceae family is lacking, it is currently difficult to design treatments or breeding programs to improve genetic stocks of desirable species or to develop methods to control the growth of undesirable species. Many of these problems could be solved by the development of sequence databases and genomic-based research strategies for members of this family. However, until these resources are generated, it should be possible to use the resources developed for leafy spurge. Comparative genomic hybridization of DNA samples from other Euphorbia species will show the utility of the planned microarrays for cross-species experiments and evolutionary studies. An analysis of sequence similarity between . 9,000 unigenes from cassava and the . 19,000 unigenes from leafy spurge indicates that . 50% of the cassava genes have functional orthologues in leafy spurge (data available at: http://titan.biotec.uiuc.edu/cassava/). Additionally, hybridization of low-density leafy spurge microarrays with transcripts of poinsettia (considered a weed in some native environments) suggests that . 60% of the leafy spurge genes hybridize with their counterparts in poinsettia and that the leafy spurge genes could be used as probes or sources of primer sequences for cloning the orthologous genes from poinsettia (data not shown). In comparison, , 47% of the cDNAs present on the Arabidopsis Functional Genomics Consortium arabidopsis

Figure 5. Characterization of leafy spurge expressed sequence tag database on the basis of Gene Ontology matches. Numbers in parentheses indicate the total number of unique sequences within biological process (A), cellular component (B), and molecular function (C). Bars show the number of sequences for each subcategory within A, B, and C.

Anderson et al.: An EST database for leafy spurge

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Table 3. Unique leafy spurge sequences with known interspersed repeats identified by RepeatMasker software and TIGR plant repeat database as the custom library. Various repeat elements identified and the lengths they occupy are shown. Number of sequences Total length

23,472 18,242,199 base pairs (bp)

Classification

No. of elements

Transposable elements Retrotransposons Transposons MITEsa

Length occupied (bp)

178 76 44

Centromere-related Centromere-specific retrotransposons Centromeric satellite repeats Unclassified centromere sequences Telomere-related Telomere sequences Telomere-associated Unclassified

26,384 6,177 8,460

0 46

6

813

2 3

79 516

12

2,797

322

45,272

Ribosomal RNA genes 45S rDNA 5S rDNA

113 66

18,869 10,323

2,752 3,038

10,5400 129,130

a

Abbreviation: MITE, miniature inverted-repeat transposable element.

cDNA microarrays hybridized significantly to total labeled cDNA from leafy spurge (Horvath et al. 2003). Future Uses. The main goal of developing the whole-plant leafy spurge EST database is to provide a resource for the future development of high-density microarrays. Although lowdensity leafy spurge microarrays have provided information about known and novel signaling processes associated with well-defined phases of dormancy in leafy spurge (Horvath et al. 2005, 2006), they often do not provide a clear understanding of physiological changes that are associated with genes that are coordinately regulated (Horvath et al. 2006). Microarrays developed with the use of the 19,015 leafy spurge unigene set described in this paper will represent the first known highdensity microarrays for a perennial weed species. This EST database should serve as a source of clones and sequences for other researchers studying specific physiological responses in leafy spurge and related species. Although the EST database and microarrays will provide greatly needed tools for studying various aspects of weed biology, the development of resources for identifying regulatory sequences is still needed. Thus, the next step toward a functional genomics characterization of leafy spurge should include the development of a high-density Bacterial Artificial Chromosome library from which regulatory sequences from important genes can be readily obtained. However, care should be taken when constructing this library because it could also serve as the backbone of any future genome sequencing project.

Sources of Materials 1

Model 1-68, Percival Scientific Inc., Boone, IA 50036. Oligotex Direct mRNA kit, Qiagen, 28159 Avenue Stanford, Valencia, CA 91355-1106. 2

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SuperScriptTM Choice System, Invitrogen, Carlsbad, CA 92008. 4 pBS II SK(+) phagemid vector, Stratagene, 1834 State Highway 71 West, Cedar Creek, TX 78612. 5 DH10B cells, Invitrogen, Carlsbad, CA 92008. 6 Genetix Q-Pix robot, Genetix, Hampshire, UK. 7 Qiagen 8000 and Qiagen 9600 robots, Qiagen Inc., 28159 Avenue Stanford, Valencia, CA 91355. 8 ABI BigDye terminator chemistry and ABI 3730XL capillary systems, Applied Biosystems, Foster City, CA 94404.

Literature Cited

1

Total interspersed repeats

Simple repeats Low complexity

3

Aharoni, A., L.C.P. Keizer, and H.J. Bouwmeester, et al. 2000. Identification of the SAAT gene involved in strawberry flavor biogenesis by use of DNA microarrays. Plant Cell 12:647–661. Ali, S., B. Holloway, and W.C. Taylor. 2000. Normalization of cereal endosperm EST libraries for structural and functional genomic analysis. Plant Mol. Biol. Rep. 18:123–132. Altschul, S.F., W. Gish, W. Miller, E.W. Myers, and D.J. Lipman. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403–410. Anderson, J.V. and D.G. Davis. 2004. Abiotic stress alters transcript profiles and activity of enzymes involved in glutathione-metabolism in Euphorbia esula. Physiol. Plant. 120:421–433. Anderson, J.V., M. Delseny, and M.A. Fregene, et al. 2004. An EST resource for cassava and other species of Euphorbiaceae. Plant Mol. Biol. 56:527–539. Anderson, J.V., R.W. Gesch, Y. Jia, W.S. Chao, and D.P. Horvath. 2005. Seasonal shifts in dormancy status, carbohydrate metabolism, and related gene expression in crown buds of leafy spurge. Plant Cell Environ. 28:1567–1578. Anderson, J.V. and D.P. Horvath. 2001. Random sequencing of cDNAs and identification of mRNAs. Weed Sci. 49:590–597. Arabidopsis Genome Initiative, The. 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815. Asamizu, E., Y. Nakamura, S. Sato, and S. Tabata. 2000a. Generation of 7137 non-redundant expressed sequence tags from a legume, Lotus japonicus. DNA Res. 7:127–130. Asamizu, E., Y. Nakamura, S. Sato, and S. Tabata. 2000b. Large scale analysis of cDNA in Arabidopsis thaliana: Generation of 12,028 non-redundant expressed sequence tags from normalized and size-selected cDNA libraries. DNA Res. 7(3):175–180. Bangsund, D.A., F.L. Leistritz, and J.A. Leitch. 1999. Assessing economic impacts of biological control of weeds: The case of leafy spurge in northern Great Plains of the United States. J. Environ. Manag. 56:35–43. Basu, C., M.D. Halfhill, T.C. Mueller, and C.N. Stewart. 2004. Weed genomics: New tools to understand weed biology. Trends Plant Sci. 9:391–398. Benson, D.A., I. Karsch-Mizrach, D.J. Lipman, J. Ostell, and D.L. Wheeler. 2005. GenBank: Update. Nucleic Acids Res. 33:D34–D38. Boguski, M.S., T.M.J. Lowe, and C.M. Tolstoshev. 1993. dbEST-database for ‘‘expressed sequence tags,’’ Nat. Genet. 4:332–333. Bo¨hlenius, H., T. Huang, L. Charbonnel-Campaa, A.M. Brunner, S. Jansson, S.H. Strauss, and O. Nilsson. 2006. CO/FT regulatory module controls timing of flowering and seasonal growth cessation in trees. Science 312:1040–1043. Bonaldo, M.F., G. Lennon, and M.B. Soares. 1996. Normalization and subtraction: Two approaches to facilitate gene discovery. Genome Res. 6:791–806. Cadman, C.S.C., P.E. Toorop, H.W.M. Hilhorst, and W.E. Finch-Savage. 2006. Gene expression profiles of arabidopsis Cvi seeds during dormancy cycling indicate a common underlying dormancy control mechanism. Plant J. 46:805–822. Chang, S., J. Puryer, and J. Cairney. 1993. A simple and efficient method for isolating RNA from pine trees. Plant Mol. Biol. Rep. 11:113–116. Chao, W.S. and J.V. Anderson. 2004. Euphorbia esula. In: Crop Protection Compendium, 2004 ed. Wallingford, UK: CAB International. Chao, W.S., D.P. Horvath, J.V. Anderson, and M.P. Foley. 2005. Potential model weeds to study genomics, ecology, and physiology in the 21st century. Weed Science 53:929–937. Chao, W.S., M.D. Serpe, J.V. Anderson, R.W. Gesch, and D.P. Horvath. 2006. Sugars, hormones, and environment affect the dormancy status in underground adventitious buds of leafy spurge (Euphorbia esula L.). Weed Sci. 54:59–68. Coupland, R.T., G.W. Selleck, and J.F. Alex. 1955. Distribution of vegetative buds on the underground parts of leafy spurge (Euphorbia esula L.). Can. J. Agric. Sci. 35:161–167.

Ewing, B. and P. Green. 1998. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8:186–194. Ewing, B., L. Hillier, M.C. Wendl, and P. Green. 1998. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8:175–185. Garcia-Hernandez, M., T.Z. Berardini, and G. Chen, et al. 2002. TAIR: A resource for integrated arabidopsis data. Funct. Integr. Genomics 2:239–253. Gene Ontology Consortium. 2004. The gene ontology (GO) database and informatics resource. Nucleic Acids Res. 32:D258–D261. Gutierrez, R.A., R.M. Ewing, J.M. Cherry, and P.J. Green. 2002. Identification of unstable transcripts in arabidopsis by cDNA microarray analysis: Rapid decay is associated with a group of touch and specific clock-controlled genes. Proc. Natl. Acad. Sci. USA. 99(17):11,513–11,518. Horvath, D.P., J.V. Anderson, M. Soto, and W.S. Chao. 2006. Transcriptome analysis of leafy spurge (Euphorbia esula L.) crown buds during shifts in welldefined phases of dormancy. Weed Sci. 54:821–827. Horvath, D.P., W.S. Chao, and J.V. Anderson. 2002. Molecular analysis of signals controlling dormancy and growth in underground adventitious buds of leafy spurge (Euphorbia esula L.). Plant Physiol. 128:1439–1446. Horvath, D.P., R. Schaffer, M. West, and E. Wisman. 2003. Arabidopsis microarrays identify conserved and differentially-expressed genes involved in shoot growth and development from distantly related plant species. Plant J. 34:125–134. Horvath, D.P., M. Soto, Y. Jia, W.S. Chao, and J.V. Anderson. 2005. Transcriptome analysis of paradormancy release in root buds of leafy spurge (Euphorbia esula). Weed Sci. 53:795–801. Huang, H. and A. Madan. 1999. CAP3: A DNA sequence assembly program. Genome Res. 9:868–877. International Rice Genome Sequencing Project. 2005. The map-based sequence of the rice genome. Nature 436:793–800. Kloosterman, B., O. Vorst, R.D. Hall, R.G.F. Visser, and C.W. Bachem. 2005. Tuber on a chip: Differential gene expression during potato tuber development. Plant Biotech. J. 3:505–519. Kumar, C.G., R. LeDuc, G. Gong, L. Roinishivili, H.A. Lewin, and L. Liu. 2004. ESTIMA, a tool for EST management in a multi-project environment. BMC Bioinf. 5:176. Lee, J.M., M.E. Williams, S.V. Tingey, and J.A. Rafalski. 2002. DNA array profiling of gene expression changes during maize embryo development. Funct. Integr. Genomics 2:13–27. Leitch, J.A., F.L. Leistritz, and D.A. Bangsund. 1996. Economic effect of leafy spurge in the upper Great Plains: Methods, models, and results. Impact Assess. 14:419–433. Liu, L., L. Roinishvili, X. Pan, Z. Liu, and C. Kumar. 2000. GPMS A Web based Genome Project Management System. Pages 62–67 in Proceeding of the 4th World Multi-conference on Systematics, Cybernectics, and Informatics SCI2000. Marshall, E. 2004. Getting the noise out of gene arrays. Science 306:630–631. McGinnis, S. and T.L. Madden. 2004. BLAST: At the core of a powerful and diverse set of sequence analysis tools. Nucleic Acid Res. 32:W20–W25.

Mewes, H.W., D. Frishman, and U. Gu¨ldener, et al. 2002. MIPS: A database for genomes and protein sequences. Nucleic Acids Res. 30:31–34. Ohlrogge, J. and C. Benning. 2000. Unravelling plant metabolism by EST analysis. Curr. Opin. Plant Biol. 3:224–228. Oztur, Z.N., V. Talame, M. Deyholos, C.B. Michalowski, D.W. Galbraith, N. Gozukirmizi, R. Tuberosa, and H.J. Bohnert. 2002. Monitoring large-scale changes in transcript abundance in drought- and salt-stressed barley. Plant Mol. Biol. 48:551–573. Potokina, E., N. Sreenivasulu, L. Altschmied, W. Michalek, and A. Graner. 2002. Differential gene expression during seed germination in barley (Hordeum vulgare L.). Funct. Integr. Genomics 2:28–39. Quackenbush, R.C., J. Cho, and D. Lee, et al. 2001. The TIGR Gene Indices: Analysis of gene transcript sequences in highly sampled eukaryotic species. Nucleic Acids Res. 29:159–164. Reymond, P., H. Weber, M. Damond, and E.E. Farmer. 2000. Differential gene expression in response to mechanical wounding and insect feeding in arabidopsis. Plant Cell 12:707–719. Richmond, T. and S. Somerville. 2000. Chasing the dream: Plant EST microarrays. Curr. Opin. Plant Biol. 3:108–116. Schena, M., D. Shalon, R.W. Davis, and P.O. Brown. 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470. Schoof, H., P. Zaccaria, H. Gundlach, K. Lemcke, S. Rudd, G. Kolesov, R. Arnold, H.W. Mewes, and K.F.X. Mayer. 2002. MIPS Arabidopsis thaliana database (MAtDB): An integrated biological knowledge resource based on the first complete plant genome. Nucleic Acids Res. 30:91–93. Schrader, J., R. Moyle, R. Bhalerao, M. Hertzberg, J. Lundeberg, P. Nilsson, and R.P. Bhalerao. 2004. Cambial meristem dormancy in trees involves extensive remodeling of the transcriptome. Plant J. 40:173–187. Smit, A.F., R. Hubley, and P. Green. 1996–2004. RepeatMasker Open-3.0. http://www.repeatmasker.org. Soares, M.B., M.F. Bonaldo, P. Jelene, L. Su, L. Lawton, and F. Efstratiadis. 1994. Construction and characterization of a normalized cDNA library. Proc. Natl. Acad. Sci. USA. 91:9228–9232. Sonju, R. and D.P. Horvath. 2005. Cloning and expression of Krp genes from adventitious buds of the perennial weed leafy spurge. Page 31 in 2005 Midwest American Society of Plant Biology Sectional Meeting. Donald Danforth Plant Science Center, St. Louis, MO. July 18–19. [Abstract]. van Hal, N.L.W., O. Vorst, A.M.M.L. van Houwelingen, E.J. Kok, A. Peijnenburg, A. Aharoni, A.J. van Tunen, and J. Keijer. 2000. The application of microarrays in gene expression analysis. J. Biotech. 78:271–280. Wheeler, D.L., D.M. Church, and R. Edgar, et al. 2004. Database resources of the National Center for Biotechnology Information: Update. Nucleic Acids Res. 32:D35–D40. Zhu, T., P. Budworth, and W. Chen, et al. 2003. Transcriptional control of nutrient partitioning during rice grain filling. Plant Biotech. J. 1:59–70.

Received August 16, 2006, and approved December 22, 2006.

Anderson et al.: An EST database for leafy spurge

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our understanding of weed biology, just as they have in other plant systems. In this report, we ...... nutrient partitioning during rice grain filling. Plant Biotech.

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