The distribution of ATP within tomato (Lycopersicon esculentum Mill.) embryos correlates with germination whee as total ATP concentration does not

The distribution of ATP in tomato seeds was visualized by monitoring the luminescence of frozen sections on top of a gel containing all the components of the luciferase reaction, but excluding ATP. ATP was imaged in germinating tomato seeds at intervals of 3, 6, 17, 24 and 48 h and in seeds with primary or secondary dormancy. ATP was present mainly in the embryo and concentrated in the radicle tip towards the completion of germination. In contrast to germinating seeds, ATP was distributed more evenly in dormant seeds. For germination, the ratio of ATP concentration in the radicle tip versus cotyledons was decisive, rather than the absolute concentration

Imaging genetics of seed performance

The Netherlands has a long history of plant breeding which has resulted in a leading position in the world with respect to the sales of vegetable seeds. Nowadays high-tech methods are used for crop-production which demands high standards for the quality of the starting materials. While breeding has mainly focused on crop yield and disease resistance in the past, it now becomes equally important to create seeds that rapidly and uniformly germinate under a wide range of production environments. A better understanding of the molecular processes that are underlying seed quality is a crucial first step to enable targeted breeding. In this thesis we describe the results of new methods that were used to map the genetics of seed germination. For this research we have used the leading plant science model species Arabidopsis thaliana which has a short generation time and a fully sequenced genome. Further, the large scientific community working on this model species is providing a wealth of resources ranging from large collections of worldwide accessions, genetic mapping populations, mutants and knowledge about gene, protein and metabolite action. A disadvantage of using Arabidopsis is the small size of the seeds, which requires evaluation of the germination of individual seeds with the use of magnifying glasses. This problem has been solved by using image analysis to create an automated procedure to obtain detailed information for parameters such as rate, uniformity and maximum germination. This procedure, called ‘the Germinator’, is described in Chapter 2 and has been enthusiastically adopted by the seed community. Plants cannot walk away from the environment at which the seed is dispersed. To survive and to enable reproduction, plants adapt to the prevailing environment which results in considerable genetic variation. This ‘natural variation’ is a great resource to study the mechanisms of adaptation. In Chapter 3 we have used two distinct Arabidopsis accessions, one originating from Germany (Bayreuth) and the other from high altitude in the Pamiro-Alay Mountains in Tadjikistan (Shahdara). In contrast to the Bayreuth accession, the Shahdara accession is well adapted to survive harsh conditions and is known to be stress tolerant to a range of environments. A genetic mapping (recombinant inbred line; RIL) population, consisting of 165 lines, that was derived from these two accessions is therefore particularly suitable to locate the genomic regions with genetic differences that influence seed germination. Such genomic regions are commonly referred to as quantitative trait loci (QTL). With help of the Germinator system we were able to evaluate germination of this RIL population under many different conditions. This resulted in a description of the ‘genetic landscape of seed performance’ in which we identified many QTLs for Arabidopsis seed germination. QTL regions are often large and identification of the causal gene requires intensive follow up research. We therefore aimed for a high throughput analysis using modern ‘omics’ techniques to analyze differences in metabolite levels and gene expression between the lines. A method to classify and visualize the vast amount of data derived from such an approach is described in Chapter 4. The so called genetical ‘omics’ experiments are expensive and therefore often force researchers to limit their study to a single developmental stage or environment only. A novel generalized setup overcomes this limitation and was tested for metabolite level changes in Chapter 5. This setup offers a unique reduction of experimental load with minimal effect on statistical power and is of great potential in the field of system genetics. Four different developmental stages of seed germination were tested in the RIL population. This approach resulted in a large dataset for which efficient analytical procedures were lacking. Thus, Chapter 5 also includes a description of a newly developed statistical procedure to analyze this type of data. The same approach and material were used in Chapter 6 to evaluate the genetics of genome wide gene expression. Another approach to zoom in on the molecular mechanisms underlying seed performance is described in Chapter 7. Here, the genetic diversity was maximized by using 360 different Arabidopsis accessions which had been subjected to ultra-high density genotyping. In potential, such a genome wide association (GWA) study can provide high resolution mapping of genetic variation resulting in only a few candidate genes per association for the phenotype under study. Although we were able to replicate experiments over two years with a high level of heritability, no significant associations were found. This emphasizes the need to critically review the power of such an approach for traits that are expected to be determined by many small effect loci. Finally, closing in on the molecular mechanisms underlying the seed traits that we studied might be possible by a full integration of the datasets that were described in the different chapters. Two examples that show the potential and the complexity of such integration are described in the General Discussion (Chapter 8). Research focused on seed quality does not end here but has gained an impulse by the described new methods and hypotheses to continue on both the fundamental and applied level in the coming years.</p

Advances in Genetical Genomics of Plants

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research

BABY BOOM target genes provide diverse entry points into cell proliferation and cell growth pathways

Ectopic expression of the Brassica napus BABY BOOM (BBM) AP2/ERF transcription factor is sufficient to induce spontaneous cell proliferation leading primarily to somatic embryogenesis, but also to organogenesis and callus formation. We used DNA microarray analysis in combination with a post-translationally regulated BBM:GR protein and cycloheximide to identify target genes that are directly activated by BBM expression in Arabidopsis seedlings. We show that BBM activated the expression of a largely uncharacterized set of genes encoding proteins with potential roles in transcription, cellular signaling, cell wall biosynthesis and targeted protein turnover. A number of the target genes have been shown to be expressed in meristems or to be involved in cell wall modifications associated with dividing/growing cells. One of the BBM target genes encodes an ADF/cofilin protein, ACTIN DEPOLYMERIZING FACTOR9 (ADF9). The consequences of BBM:GR activation on the actin cytoskeleton were followed using the GFP:FIMBRIN ACTIN BINDING DOMAIN2 (GFP:FABD) actin marker. Dexamethasone-mediated BBM:GR activation induced dramatic changes in actin organization resulting in the formation of dense actin networks with high turnover rates, a phenotype that is consistent with cells that are rapidly undergoing cytoplasmic reorganization. Together the data suggest that the BBM transcription factor activates a complex network of developmental pathways associated with cell proliferation and growth

Unravelling the complex trait of seed quality: using natural variation through a combination of physiology, genetics and -omics technologies

Seed quality is a complex trait that is the result of a large variety of developmental processes. The molecular-genetic dissection of these seed processes and their relationship with seed and seedling phenotypes will allow the identification of the regulatory genes and signalling pathways involved and, thus, provide the means to predict and enhance seed quality. Natural variation for seed-quality aspects found in recombinant inbred line (RIL) populations is a great resource to help unravel the complex networks involved in the acquisition of seed quality. Besides extensive phenotyping, RILs can also be profiled by -omics technologies, such as transcriptomics, proteomics and metabolomics in a sophisticated so-called generalized genetical genomics approach. This combined use of physiology, genetics and several -omics technologies, followed by advanced data analysis, allows the construction of regulatory networks involved in the various attributes of seed and seedling quality. This type of analysis of the genetic variation in RIL populations in combination with genome-wide association (GWA) studies will allow a relatively rapid identification of genes that are responsible for quality-related traits of seeds and seedlings. New developments in several -omics technologies, especially the fast-evolving next-generation sequencing techniques, will make a similar system-wide approach more applicable to non-model species in the near future and this will be a huge boost for the potential to breed for seed quality

Visualization of molecular processes associated with seed dormancy and germination using MapMan

Seed dormancy and germination involve the concerted operation of molecular and biochemical programmes. It has become feasible to study these processes in great detail, using the current methods for transcriptome, proteome and metabolome analysis. Yet, the large amounts of data generated by these methods are often dazzling and demand efficient tools for data visualization. We have used the freely available PageMan/MapMan package (http://MapMan.gabipd.org) to visualize transcriptome and metabolome changes in Arabidopsis thaliana seeds during dormancy and germination. Using this package we developed two seed-specific MapMan pathways, which efficiently capture the most important molecular processes in seeds. The results demonstrated the usefulness of the PageMan/MapMan package for seed research

Genetical Genomics of Plants: From Genotype to Phenotype

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e., genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotypephenotype relationships for both fundamental and applied researc

The Germinator automated germination scoring system

Fundamental and applied seed biology research relies heavily on accurate quantification of seed germination. Nowadays, large-scale experiments using large genetic populations or mutant collections are popular tools to unravel the molecular aspects of seed development, germination, dormancy and seed performance. The scientific community has embraced Arabidopsis thaliana as the ultimate model species for plant science, and it has also become a very useful model plant to study seed biology. However, so far, germination of the very small A. thaliana seeds can only be evaluated by binocular microscope, making this a very laborious task. This often hampers the collection of cumulative germination data in large-scale experiments