Cotton plants that have been genetically engineered to contain a gene from the soil bacterium Bacillus thuringiensis (Bt) (Bollgard^®, Monsanto Co., St. Louis, MO) have been used as a tool to selectively manage caterpillar pests for over 10 years. This technology is now used worldwide, with increasing Bt acres occurring in China, Brazil, India, and Australia. Although highly effective against certain species of insects such as the tobacco budworm, Heliothis virescens (F.), and the pink bollworm, Pectinophora gossypiella (Saunders), other control strategies, such as supplemental foliar insecticide applications, are needed to control additional caterpillar species that are not controlled with the Bt gene (more accurately called the cry1Ac gene and its Cry1Ac protein). This is especially true for the cotton bollworm, Helicoverpa armigera, a major pest outside of the western hemisphere.

Commercial cultivars of Bollgard^® cotton differ in the amount of expressed Cry1Ac protein. Overall Cry1Ac levels among Bollgard^® cultivars have been correlated to survival levels in various caterpillar pests that are naturally tolerant to this Bt protein^1,2. These differences in Cry1Ac protein levels among commercial cultivars can vary as much as five-fold throughout the season¹. Furthermore, these cultivar differences are heritable and controlled by a small number (1 – 2) of genetic factors^3,4.

The parental background (i.e., the non-Bt cultivar) has a significant influence on the amount of available Cry1Ac protein in Bollgard® cultivars^3,4. By using forward breeding (i.e., Bollgard® cultivars crossed with Bollgard® cultivars), cultivars can be selected for the highest overall amount of Cry1Ac in addition to desired agronomic traits⁵. Although increased levels of Cry1Ac protein can be obtained in a selected cultivar, the plant mechanism by which this occurs has not been fully characterized. The purpose of this study was two-fold: 1) to determine if the overall cry1Ac mRNA levels differ among Bollgard® lines; and 2) to determine if transcription levels correlate with available Cry1Ac levels.

Forward breeding to obtain Cry1Ac protein
Details of the development of cotton lines that contain different levels of Cry1Ac protein derived from the Bollgard® trait are described in previous publications^3,5. Briefly, in 2001 crosses were made from plants derived from distantly related backgrounds that expressed different levels of Cry1Ac. A Stoneville Pedigree Seed Co. cultivar (cv. ST4691B) and a Delta and Pineland Co. cultivar (cv. NuCOTN 33B) were chosen as parental cultivars for the subsequent crosses because NuCOTN 33B expresses significantly more Cry1Ac in all plant structures compared to ST4691B⁶.

Seeds produced from the crosses (F₁) were grown in the greenhouse, allowed to self-pollinate, and the F₂ seed was planted the following spring under field conditions. Once cotton bolls began to open, five randomly chosen plants/plot were marked, and the amount of Cry1Ac protein was quantified using common ELISA techniques⁵. After all bolls were mature, F₃ seed was hand-harvested from each tagged plant and planted the following spring.

The resulting F₃ seeds obtained from each of the five selected F₂ plants were planted in progeny rows. After plants began to flower, healthy, uniform plants (5) were selected from each plot for Cry1Ac quantification⁵. Once Cry1Ac protein levels were obtained for all progeny rows, lines were selected for further study. Four lines that consistently had high and low levels of Cry1Ac protein (High: 51-2 and 53-5, Low: 48-5 and 42-1) were identified using ELISA, and the resulting F₄ seeds were hand-harvested and planted the following spring. These lines were then used for measuring the cry1Ac transcript.

Cry1Ac mRNA quantification
Relative quantification of the cry1Ac transcript was conducted using qPCR by the standard curve based method⁷. An endogenous reference and a treatment calibrator were used to determine the relative quantity of a target transcript. The cry1Ac primer-probe 20X concentrate contained forward primer (5’-CGCGAGGAAATGCGTATTCAAT -3’) and reverse primer (5’-ACAATGGGATAGCTGTGGTCAAG-3’) at a concentration of 18 µM each and the TaqMan MGB probe (5’-FAM-TCAACGACATGAACAGCG-3’) at a 5 µM concentration. The 18S rRNA universal endogenous control primer-probe set (assay # Hs99999901_s1) was purchased from Applied Biosystem’s Assays-on-Demand service. Nucleotide sequences of the PCR primers or the detection probe of this proprietary product were not provided, although provided context sequence (TGGAGGGCAAGTCTGGTGCCAGCAG), around which the detection sequences were designed, matched cotton (G. hirsutum) 18S rRNA sequence (Accession # GHU42827) from nucleotide positions 521 to 545. TaqMan MGB detection probes for both cry1Ac and 18S rRNA sequences were labeled with a 6-FAM (6-carboxyfluorescein) reporter dye molecule at the 5’ end and with a non-fluorescent quencher DABCYL (4-(4’-dimethylamino-phenylazo)-benzoic acid) at the 3’ end.

Suitability of both target and reference genes (cry1Ac and 18S, respectively) for quantifying the respective genes was tested by performing qPCR on cDNA prepared from total RNA of Bt cotton plants. Relative expression level of cry1Ac in each sample was calculated by normalizing the absolute quantities of cry1Ac to that of 18S in each sample⁷. These relative expression values normalized to endogenous controls were divided by the expression value of the leaf sample showing the least expression level (i.e., calibrator) to obtain expression levels relative to the calibrator sample.

Cry1Ac protein in Bollgard^® correlated with cry1Ac mRNA transcript
The four selected lines used for subsequent mRNA studies differ in the amount of expressed Cry1Ac protein. The mean for the two low lines (48-5 and 42-1) is 2.58 μg Cry1Ac g ^-1 fresh weight and for the two high lines (53-5 and 51-2) is 10.32 μg Cry1Ac g ^-1 fresh weight, or a difference of ca. 400%. It is evident that cultivar improvement in Cry1Ac protein content can be greatly improved by selection at the F2 plant and F2:3 progeny row level⁵. Further selection of high levels of Cry1Ac protein is highly effective, increasing the ability to control target caterpillar pests.

The amount of Cry1Ac protein and mRNA is significantly different (P < 0.05) among the examined cotton lines (Protein: F = 171.64; df = 4, 11; P < 0.001) (mRNA: F = 22.47; df = 4, 14; P< 0.001). Both lines 48-5 and 42-1 have significantly lower (P < 0.05) amounts of cry1Ac mRNA transcript and corresponding protein compared to all other cotton lines containing the Bollgard^® trait (Fig. 1).

In addition, both lines 53-5 and 51-2 contain significantly higher (P < 0.05) amounts of cry1Ac mRNA transcript than all other cotton lines, and at least numerically higher amounts of Cry1Ac protein than the remaining cotton lines. The cv. NuCOTN 33B control has significantly higher levels of cry1Ac mRNA transcript and corresponding protein compared to the low lines (42-1 and 48-5). Furthermore, in the same environment and from the same plant structure, there is a significant correlation (p < 0.001) between the amount of cry1Ac mRNA transcript and the amount of corresponding Cry1Ac protein (Fig. 2). It is important to note that a full linear relationship is not observed.

For example, Cry1Ac protein varied ca. 30-fold when corresponding cry1Ac mRNA transcripts are between ca. 2.5 and ca. 3.0 (Cry1Ac/18S).

These differences in Cry1Ac protein levels may be partially explained by plant-to-plant variability caused by environmental factors⁸.

Our data show that the overall level of cry1Ac mRNA transcript differs among Bollgard^® lines and is correlated with corresponding Cry1Ac protein. Previous studies have shown that only a small number of genetic factors were controlling the amount of Cry1Ac protein in these transgenic cultivars³. This current study suggests that these genetic factors are impacting the overall cry1Ac mRNA transcript levels among different Bollgard^® lines. However, post-translational effects or environmental factors that could further impact available Cry1Ac protein levels cannot be excluded^8,9.

Commercial seed companies could select for the highest cry1Ac-expressing Bollgard^® cultivar in addition to desired agronomic traits early in plant development (i.e., seeds), virtually excluding environmental factors that could impact Cry1Ac protein expression⁸. Previously, the differences among Cry1Ac protein levels in Bollgard^® cultivars could be determined at any stage of plant development using ELISA assays⁶. Furthermore, we postulate that qPCR-based methods could be successfully employed for quantifying expression levels of transgenes in plants carrying different Cry genes and could offer an alternative choice for conducting such studies without the expense of acquiring monoclonal antibodies.

1. Adamczyk JJ et al. (2001) Journal of Economic Entomology 94, 284-290
2. Wan P et al. (2005) Journal of Economic Entomology 98, 195-201
3. Adamczyk JJ and Meredith WR (2004) Journal of Cotton Science 8, 17-23
4. Rochester IJ (2006) Journal of Cotton Science 10, 252-262
5. Adamczyk JJ and Meredith WR (2006) Journal of Economic Entomology 99, 1835-1841
6. Adamczyk JJ and Sumerford DV (2001) Journal of Insect Science 1, 13
7. Livak, K (1997) ABI Prism 7700 Sequence Detection System, User Bulletin 2. PE Applied Biosystems, Foster City, CA
8. Dong HZ and Li WJ (2007) Journal of Agronomy and Crop Science 193, 21-29
9. Xia L et al. (2005) Acta Agron Sin 31,197-202
10. Adamczyk JJ, Perera O and Meredith WR (2009) Transgenic Research 18 (1), 143-149

ALTERED POLYAMINE CATABOLISM: PRODUCING TRANSGENIC PLANTS WITH ENHANCED TOLERANCE TO BACTERIA AND OOMYCETES
K. A. Roubelakis-Angelakis, P. N. Moschou, P. F. Sarris, N. J. Panopoulos

In the last two decades, molecular approaches have been used to unravel the complex mechanisms by which tolerant plants cope with invading pathogens¹. This information has helped scientists develop transgenic plants with enhanced tolerance to specific pathogens. Polyamines (PAs) are aliphatic amines present in all eukaryotic cells, with Putrescine (Put), Spermidine (Spd), and Spermine (Spm) the more prevalent ones. PAs have been linked to stress protection in plants, based mostly on pharmacological studies and monitoring intracellular PA titers, though the mode of action has remained largely unknown. Polyamine oxidase (PAO) oxidizes PAs, generating hydrogen peroxide (H₂O₂). Overexpression of the PAO gene increases apoplastic H₂O₂, which up to a certain maximum value induces expression of antioxidant enzymes; however, above that value, the programmed cell death (PCD) syndrome is initiated². Recently we showed that abiotic stress induces Spd secretion into the apoplast, where it is oxidized by PAO to produce H₂O₂. Depending on its ‘signatures’ and the intracellular PAs homeostasis, the generated H₂O₂ signals the induction of either tolerance to abiotic stress or the PCD syndrome. Thus, over-expression of PAO generated abiotic stress-sensitive tobacco plants.

Because H₂O₂ represents a nodal point in plant pathogen interactions, we attempted to establish the origin and role of H₂O₂ during infection with bacterial, oomycetes, and viral pathogens, using tobacco transgenic plants with either an overexpressed or down-regulated the PAO gene³. More specifically, we used Nicotiana tabacum cv Xanthi transgenic plants overexpressing (S-PAO) or down-regulating (A-PAO) the maize PAO gene (mPAO) and studied their host-pathogen interaction with (i) the biotrophic bacterium Pseudomonas syringae pv tabaci (PS), using one strain with mild pathogenicity aggressiveness, P. syringae pv tabaci BPIC1514 (mPS), and another strain with high pathogenicity aggressiveness, P. syringae pv tabaci SFP-2124 (sPS); (ii) the hemibiotrophic oomycete Phytophthora parasitica var nicotianae (PP); and (iii) the Cucumber Mosaic Virus. Oomycetes are unrelated to true fungi, and they are resistant to most fungicides. Moreover, they quickly overcome plant resistance genes. PS causes wildfire, one of the most destructive diseases affecting field tobacco plants. CMV is a systemic, tripartite, positive-sense RNA virus that infects several plant species and causes light-green or yellow mosaic, leading to stunted growth.

Following infection with PS, the NtPAO gene was induced, and PAO immunoreactive protein accumulated shortly after the onset of biotic stress. Concomitantly, PAs biosynthetic enzymes were also induced, of which Arginine decarboxylase (ADC) appeared to be the most important responsive enzyme required to sustain the increase in PAs homeostasis. The increase in the biosynthetic activities was mirrored by a parallel increase in endogenous PA titers, consisting mostly of Spm [18-fold 12 h post inoculation (p.i.)] in the apoplastic compartment. The growth rate of PS and the endophytic growth of PP in S-PAO plants was significantly lower compared to the corresponding rate in A-PAO and wild type plants (Fig. 1A). In contrast, in A-PAO and WT plants, the growth rate of PS c.f.u. was increased 10-fold compared to S-PAO plants (Fig. 1A), and after 5 d p.i. the PS population started to decline, reaching the initial values, due to tissue collapse (Fig. 1A). During PP infection on tobacco leaves, endophytic growth in A-PAO plant leaves was slightly slower 8 days p.i. compared to control, but eventually the whole leaf was infected similarly to the PP infection of wild type tobacco leaves (Fig. 1A). No phenotypical or multiplication rate differences were observed between the genotypes inoculated with the CMV virus (Fig. 1A). A direct positive relationship between PAO activity and H₂O₂ accumulation in the apoplast of infected plants was documented (Fig. 1B). Thus, S-pao transgenic tobacco exhibited tolerance to virulent bacterial pathogens and to a hemibiotrophic oomycete but not to viruses.

In addition, by modulating the PA-derived apoplastic H₂O₂, primary and secondary biotic stress responses were induced independently of salicylic acid (SA). Plant pathogen tolerance mechanisms include the reinforcement of cell walls and expression of pathogenesis-related genes. The resistance induced by overexpression of PAO was accompanied by such responses: S-PAO plants had enhanced pectin content under normal growth and biotic stress conditions, and continued to further accumulate pectin p.i., while the control S-PAO plants contained an amount of pectin similar to wild type plants p.i. challenged with PS (Fig. 2A). Furthermore, lignin was slightly higher in S-PAO plants when compared to wild type plants, whereas the opposite was true for A-PAO plants. In addition, callose (1,3-β-glucan) deposition in S-PAO plants was significantly higher compared to wild type and A-PAO plants.

Increased apoplastic PA oxidation regulates the expression of biotic stress activated genes. In S-PAO plants, some important genes contributing to tolerance were induced prior to inoculation. These genes included PR-1a, PR-5b, PrxN1, and two MAPKs, namely SIPK and WIPK. In contrast, PrxC1 was not induced (Fig. 2B). Pathogenesis-related (PR) proteins are likely involved in the secondary defense against biotic-stress challenge, while genes such as PrxC1 and PrxN1 are targets of the downstream Spm-signaling pathway during TMV infection of tobacco plants⁴, which depends on Spm oxidation in the apoplast of tobacco plants. Interestingly, these genes were not further induced in S-PAO plants p.i.; on the contrary, these genes were further induced in wild type and A-PAO plants.

In summary, this work provides evidence that apoplastic PAO constitutes an important defense in compatible plant-pathogen interactions but not in viral infections. The generated H₂O₂ in the apoplast signals the induction of plant tolerance mechanisms independently of SA, which can lead to tolerance against two of the most devastating plant diseases. These results add new insight into the participation of the PAO gene in the generation of apoplastic H₂O_2, which signals plant-pathogens interactions. Overexpression of this gene confers tolerance to specific pathogens that use the apoplast for their colonization, and presents a novel means for generating plants tolerant to pathogenic invasion. Thus, genetically engineering PAO can be an efficient method for enhancing tolerance of plants to bacteria, such as PS, and to fungi, such as PP.

In the creation of a transgenic plant, one or more transgenes are inserted into the plant genome. While this is typically done to introduce some novel trait, there is also the possibility for other unintended effects. It is important to understand the types of unintended effects that can occur in transgenic plant systems and to employ a unified terminology for their description, as has recently been reviewed by Miki et al.¹.

There are two main types of unintended effects that can occur in transgenic plants. First, position effects are locus-specific unintended effects that result from the interruption or alteration of processes occurring at the site of transgene insertion. Second, phenotypic traits are directly related to the presence of the transgene and constitute the pleiotropic effects. While one or more of these may be the intended trait, others may result from unexpected interactions of the transgene with plant processes and are unintended pleiotropic effects. These unintended effects in transgenic plants represent potential safety concerns. It is important to understand the nature of these effects and their potential for occurring in order to identify any potential risks that could affect the safe use of transgenic plants.

Global profiling technologies assess unintended effects
Risk assessment of transgenic crops is guided by the concepts of familiarity and substantial equivalence². Generally, a transgenic plant is compared to a suitable non-transgenic counterpart that has a history of safe use, and if they are substantially equivalent, the transgenic plant can be considered safe as well. Identified differences between the plants would be a starting point for more focused risk assessment. Typically, a transgenic plant is assessed for a number of specific parameters that may include specific phenotypic traits and any changes in agronomic performance, levels of important nutrients, and endogenous levels of harmful allergens and toxins or the production of new ones.

Global profiling technologies provide an additional tool for assessing the safety of transgenic plants. These technologies contribute a comprehensive, global view of the levels of transcripts, proteins, and metabolites. They can be an important addition to the analysis of transgenic plants because they are non-targeted and therefore are not limited to those unintended effects that can be predicted³. Several studies have applied these technologies to different transgenic plant systems, demonstrating the effectiveness of such an approach and providing information about the potential for unintended effects to occur in transgenic plant systems.

What global profiling studies reveal
Global profiling studies have significantly contributed to our understanding of the potential for unintended effects in transgenic plant systems. The focus of several of these studies has been on selectable markers, since they are used in most transgenic plant systems. Unintended effects and plant selectable markers have recently been reviewed by Miki et al.¹, and will be further examined here to demonstrate some of what we have learned from global profiling studies.

First, these studies reveal that it is possible to create transgenic plants that are substantially equivalent to their non-transgenic counterparts apart from the intended novel trait. This is illustrated by the insertion of the commonly used selectable marker gene nptII into the model plant Arabidopsis thaliana. The presence of the nptII gene is correlated with only small changes in the transcriptome, and these changes are considered a natural variation inherent to plant systems since they do not reproducibly occur across multiple plant lines⁴. The transgenic plants also respond similarly to their counterparts to various abiotic stresses, further demonstrating their equivalence. This study illustrates that the transformation process itself does not alter the gene expression patterns of the plant and that selectable marker genes can be inserted into the genome without altering the transcriptome.

In contrast, the insertion of the selectable marker gene bar into the genome of Arabidopsis results in the consistent differential regulation of a small number of genes, and these plants also exhibit a unique response to the application of the herbicide glufosinate⁵. This study illustrates that unintended effects can occur in transgenic plant systems that are specific to the transgene. Furthermore, it demonstrates that global profiling technologies are an effective means of identifying unintended effects, and thereby informing any further safety assessment if required.

Importantly, these technologies also show similar results for the traits found in two classic transgenic plants, demonstrating their effectiveness for the analysis of commercial crops. Microarray analysis of glyphosate-resistant soybean has identified changes in the transcriptome in a small number of genes, but greater changes are seen between different soybean cultivars, suggesting that the transgene has a minimal effect on the transcriptome⁶. Maize plantlets expressing the Cry1Ab protein from Bacillus thuringiensis, which renders the plants resistant to the European corn borer, do not show consistent differences in gene expression that could be attributable to the Cry1Ab gene⁷, while analysis of the proteome and metabolome of maize seed reveal small differences^8,9.

Global profiling studies have also emphasized the need to establish the natural range of variation of genetic products in a given plant species. While rigorous experimental design employing controlled growth conditions and multiple plant lines can minimize natural variability, allowing a more precise identification of unintended effects, it may also be important to assess plants grown under more natural conditions. In such cases, differences between the transgenic plant and its non-transgenic counterpart are only considered to be significant if they fall outside the range of natural variability. One means of assessing the natural variability in a given plant species is to compare different cultivars. This approach has demonstrated that differences in global profiles are often greater between different cultivars than they are between transgenic plants and their non-transgenic counterparts, leading to a conclusion of substantial equivalence^6,10. Alternatively, variation in a single cultivar grown at different sites and during different years is another means of assessing natural variability and again this has been successfully employed to demonstrate substantial equivalence¹¹.

Global profiling technologies may become a useful tool for the risk assessment of transgenic crops and have also contributed significantly to our understanding of unintended effects in transgenic plant systems. They have been employed to assess the substantial equivalence of two transgenic plant systems currently grown commercially, as well as a number of other model systems.

Future perspectives
Studies employing global profiling technologies to assess unintended effects in transgenic plants have to date focused primarily on simple monogenic traits. In contrast, the next generation of transgenic crops is predicted to involve manipulation of more complex traits, focusing on enhancing yield as well as maintaining yield in suboptimal growing conditions. Examples include drought-tolerant corn¹² and drought-tolerant canola¹³.

These second generation traits will involve manipulating complex networks that are typically under multipoint regulation. Therefore, alterations to these networks are expected to have a greater potential for producing unintended effects. Significantly, global profiling technologies have been developed to a level where they can be employed to understand potential unintended effects resulting from these traits before they become commercialized, unlike many first generation crops that were commercialized before the advent of these technologies.

Second generation crops will likely be created by manipulating key points of regulation in target pathways, and many of these key points will be transcription factors. Manipulation of the level of expression of transcription factors is a common approach for altering plant characteristics¹². Transcription factors therefore represent a good model for predicting the potential for unintended effects in transgenic plants with complex traits.

In plants, transcription factors typically exist as members of large families that have undergone complex histories of evolution, allowing them to function in a wide range of plant processes from development to stress response. Gene redundancies have repeatedly occurred throughout evolution as a result of genome duplications, and these can be found at various stages of resolution as a result of subfunctionalization and neofunctionalization processes. In some cases, transcription factors can retain ancestral function, and this functional redundancy may not be revealed until normal gene expression is altered. The MADS-box family, for example, is a large transcription factor family in plants that has undergone a complex history of gene evolution¹⁴. In Arabidopsis, the MADS-box gene AGAMOUS (AG) functions in the development of the stamen, carpel, ovule, and floral meristem, while two paralogs of AG, SHATTERPROOF 1 (SHP1) and SHP2, primarily function in fruit development. However, SHP1 and SHP2 also function redundantly with AG in carpel and ovule development and in ectopic expression, since they are not normally expressed in stamens¹⁵. Interestingly, in Antirrhinum, the functional homolog of AG, PLENA, is actually paralogous to AG and orthologous to SHP1 and SHP2, suggesting the evolution of the AG clade followed different patterns of subfunctionalization in the two species¹⁶.

The AGAMOUS subfamily of MADS-box transcription factors provides an excellent example of the complex history of evolution both within and between species that has shaped the function of transcription factors. It illustrates the potential for unpredictable functional redundancies and residual activities that may only be revealed by ectopic expression, leading to unintended pleiotropic effects. It also suggests that transgenic plants with complex traits may be more likely to exhibit such unintended effects. Global profiling technologies will be fundamental in the assessment of these unintended effects at the molecular level, thus providing a useful tool for identifying any potential resulting new risks.

Concluding remarks
Despite the relatively recent development of many global profiling technologies and their application to transgenic plant systems, they have already proven an effective means of analyzing unintended effects resulting from genetic engineering. A number of studies have demonstrated that plant transformation can occur without significantly altering plant processes. Other studies have identified the occurrence of unintended effects in transgenic plants, providing very useful data for safety assessment. To date, identified differences have been relatively minor but it is imperative that these studies be extended to more complex traits.

Second generation transgenic crops are beginning to enter the regulatory system and this knowledge will be useful in developing the appropriate methods for a complete safety assessment of these plants. Manipulating complex traits is more likely to produce unintended effects in plant systems, making non-targeted assessment of unintended effects a crucial part of their analysis, whether at the genotypic or phenotypic level. Global profiling technologies will be a useful tool in guiding the risk assessment process for increasingly complex plants with novel traits.

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4. El Ouakfaoui S, Miki B. (2005) The stability of the Arabidopsis transcriptome in transgenic plants expressing the marker genes nptII and uidA. Plant J 41, 791-800
5. Abdeen A, Miki B. (2009) The pleiotropic effects of the bar gene and glufosinate on the Arabidopsis transcriptome. Plant Biotechnol J 7, 266-282
6. Cheng KC et al. (2008) Effect of transgenes on global gene expression in soybean is within the natural range of variation of conventional cultivars. J Agric Food Chem 56, 3057-3067
7. Coll A et al. (2008) Lack of repeatable differential expression patterns between MON810 and comparable commercial varieties of maize. Plant Mol Biol 68, 105-117
8. Albo AG et al. (2007) Proteomic analysis of a genetically modified maize flour carrying CRY1AB gene and comparison to the corresponding wild-type. Maydica 52, 443-455
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10. Catchpole GS et al. (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci USA 102, 14458-14462
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12. Nelson DE et al. (2007) Plant nuclear factor Y (NF-Y) B subunits confer drought tolerance and lead to improved corn yields on water-limited acres. Proc Natl Acad Sci USA 104, 16450-16455
13. Wang Y et al. (2009) Shoot-specific down-regulation of protein farnesyltransferase (α-subunit) for yield protection against drought in canola. Mol Plant 2, 191-200
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16. Causier B et al. (2005) Evolution in action: Following function in duplicated floral homeotic genes. Curr Biol 15, 1508-1512

9th International Plant Molecular Biology (IPMB) Congress
Leading Biology through Plant Science
October 25 - 30, 2009
St. Louis, Missouri, USA

In order to increase and maintain world food production, for improved diet and human health, plant scientists need to understand the mechanisms that control plant growth, how to manipulate these mechanisms for plant improvement, and to teach the world about these mechanisms. The 9th International Plant Molecular Biology (IPMB) Congress is a venue composed of Plenary Lectures and 54 symposia that will allow scientists worldwide to come to St. Louis and share their love for biology, knowledge and research results.