Cathy Xiaoyan Zhong

Phosphoserines on Maize CENTROMERIC HISTONE H3 and Histone H3 Demarcate the Centromere and Pericentromere during Chromosome Segregation

We have identified and characterized a 17- to 18-kD Ser50-phosphorylated form of maize (Zea mays) CENTROMERIC HISTONE H3 (phCENH3-Ser50). Immunostaining in both mitosis and meiosis indicates that CENH3-Ser50 phosphorylation begins in prophase/diplotene, increases to a maximum at prometaphase-metaphase, and drops during anaphase. Dephosphorylation is precipitous (approximately sixfold) at the metaphase–anaphase transition, suggesting a role in the spindle checkpoint. Although phCENH3-Ser50 lies within a region that lacks homology to any other known histone, its closest counterpart is the phospho-Ser28 residue of histone H3 (phH3-Ser28). CENH3-Ser50 and H3-Ser28 are phosphorylated with nearly identical kinetics, but the former is restricted to centromeres and the latter to pericentromeres. Opposing centromeres separate in prometaphase, whereas the phH3-Ser28–marked pericentromeres remain attached and coalesce into a well-defined tether that binds the centromeres together. We propose that a centromere-initiated wave of histone phosphorylation is an early step in defining the two major structural domains required for chromosome segregation: centromere (alignment, motility) and pericentromere (cohesion).

Transformation of rice with long DNA-segments consisting of random genomic DNA or centromere-specific DNA

Rice was transformed with either long DNA-segments of random genomic DNA from rice, or centromere-specific DNA sequences from either maize or rice. Despite the repetitive nature of the transgenic DNA sequences, the centromere-specific sequences were inserted largely intact and behave as simple Mendelian units. Between 4 and 5% of bombarded callus clusters were transformed when bombarded with just pCAMBIA 1305.2. Frequency of recovery dropped to 2–3% when BACs with random genomic inserts were co-bombarded with pCAMBIA, and fell to less than 1% when BACs with centromeric DNA inserts and pCAMBIA were co-bombarded. A similar effect was noted on regeneration frequency. Differences in transformation ability, regeneration and behavior of plants transgenic for BACs with random genomic DNA inserts, as compared to those with centromeric DNA inserts, suggests functional differences between these two types of DNA.

Effects of Genetics and Environment on the Metabolome of Commercial Maize Hybrids: A Multisite Study

This study was designed to elucidate the biological variation in expression of many metabolites due to environment, genotype, or both, and to investigate the potential utility of metabolomics to supplement compositional analysis for substantial equivalence assessments of genetically modified (GM) crops. A total of 654 grain and 695 forage samples from 50 genetically diverse non-GM DuPont Pioneer maize hybrids grown at six locations in the U.S. and Canada were analyzed by coupled gas chromatography time-of-flight-mass spectrometry (GC/TOF-MS). A total of 156 and 185 metabolites were measured in grain and forage samples, respectively. Univariate and multivariate statistical analyses were employed extensively to compare and correlate the metabolite profiles. We show that the environment had far more impact on the forage metabolome compared to the grain metabolome, and the environment affected up to 50% of the metabolites compared to less than 2% by the genetic background. The findings from this study demonstrate that the combination of GC/TOF-MS metabolomics and comprehensive multivariate statistical analysis is a powerful approach to identify the sources of natural variation contributed by the environment and genotype.

Effect of environment and genotype on commercial maize hybrids using LC/MS-based metabolomics.

We recently applied gas chromatography coupled to time-of-flight mass spectrometry (GC/TOF-MS) and multivariate statistical analysis to measure biological variation of many metabolites due to environment and genotype in forage and grain samples collected from 50 genetically diverse nongenetically modified (non-GM) DuPont Pioneer commercial maize hybrids grown at six North American locations. In the present study, the metabolome coverage was extended using a core subset of these grain and forage samples employing ultra high pressure liquid chromatography (uHPLC) mass spectrometry (LC/MS). A total of 286 and 857 metabolites were detected in grain and forage samples, respectively, using LC/MS. Multivariate statistical analysis was utilized to compare and correlate the metabolite profiles. Environment had a greater effect on the metabolome than genetic background. The results of this study support and extend previously published insights into the environmental and genetic associated perturbations to the metabolome that are not associated with transgenic modification.

A modified data normalization method for GC-MS-based metabolomics to minimize batch variation

The goal of metabolomics data pre-processing is to eliminate systematic variation, such that biologically-related metabolite signatures are detected by statistical pattern recognition. Although several methods have been developed to tackle the issue of batch-to-batch variation, each method has its advantages and disadvantages. In this study, we used a reference sample as a normalization standard for test samples within the same batch, and each metabolite value is expressed as a ratio relative to its counterpart in the reference sample. We then applied this approach to a large multi-batch data set to facilitate intra- and inter-batch data integration. Our results demonstrate that normalization to a single reference standard has the potential to minimize batch-to-batch data variation across a large, multi-batch data set.

Analytical method evaluation and discovery of variation within maize varieties in the context of food safety: transcript profiling and metabolomics.

Profiling techniques such as microarrays, proteomics, and metabolomics are used widely to assess the overall effects of genetic background, environmental stimuli, growth stage, or transgene expression in plants. To assess the potential regulatory use of these techniques in agricultural biotechnology, we carried out microarray and metabolomic studies of 3 different tissues from 11 conventional maize varieties. We measured technical variations for both microarrays and metabolomics, compared results from individual plants and corresponding pooled samples, and documented variations detected among different varieties with individual plants or pooled samples. Both microarray and metabolomic technologies are reproducible and can be used to detect plant-to-plant and variety-to-variety differences. A pooling strategy lowered sample variations for both microarray and metabolomics while capturing variety-to-variety variation. However, unknown genomic sequences differing between maize varieties might hinder the application of microarrays. High-throughput metabolomics could be useful as a tool for the characterization of transgenic crops. However, researchers will have to take into consideration the impact on the detection and quantitation of a wide range of metabolites on experimental design as well as validation and interpretation of results.

Genotypic and Environmental Impact on Natural Variation of Nutrient Composition in 50 Non Genetically Modified Commercial Maize Hybrids in North America.

This study was designed to assess natural variation in composition and metabolites in 50 genetically diverse non genetically modified maize hybrids grown at six locations in North America. Results showed that levels of compositional components in maize forage were affected by environment more than genotype. Crude protein, all amino acids except lysine, manganese, and β-carotene in maize grain were affected by environment more than genotype; however, most proximates and fibers, all fatty acids, lysine, most minerals, vitamins, and secondary metabolites in maize grain were affected by genotype more than environment. A strong interaction between genotype and environment was seen for some analytes. The results could be used as reference values for future nutrient composition studies of genetically modified crops and to expand conventional compositional data sets. These results may be further used as a genetic basis for improvement of the nutritional value of maize grain by molecular breeding and biotechnology approaches.

Effect of Genetics, Environment, and Phenotype on the Metabolome of Maize Hybrids Using GC/MS and LC/MS

We evaluated the variability of metabolites in various maize hybrids due to the effect of environment, genotype, phenotype as well as the interaction of the first two factors. We analyzed 480 forage and the same number of grain samples from 21 genetically diverse non-GM Pioneer brand maize hybrids, including some with drought tolerance and viral resistance phenotypes, grown at eight North American locations. As complementary platforms, both GC/MS and LC/MS were utilized to detect a wide diversity of metabolites. GC/MS revealed 166 and 137 metabolites in forage and grain samples, respectively, while LC/MS captured 1341 and 635 metabolites in forage and grain samples, respectively. Univariate and multivariate analyses were utilized to investigate the response of the maize metabolome to the environment, genotype, phenotype, and their interaction. Based on combined percentages from GC/MS and LC/MS datasets, the environment affected 36% to 84% of forage metabolites, while less than 7% were affected by genotype. The environment affected 12% to 90% of grain metabolites, whereas less than 27% were affected by genotype. Less than 10% and 11% of the metabolites were affected by phenotype in forage and grain, respectively. Unsupervised PCA and HCA analyses revealed similar trends, i.e., environmental effect was much stronger than genotype or phenotype effects. On the basis of comparisons of disease tolerant and disease susceptible hybrids, neither forage nor grain samples originating from different locations showed obvious phenotype effects. Our findings demonstrate that the combination of GC/MS and LC/MS based metabolite profiling followed by broad statistical analysis is an effective approach to identify the relative impact of environmental, genetic and phenotypic effects on the forage and grain composition of maize hybrids.