Live cell imaging of meiosis in Arabidopsis thaliana.

To follow the dynamics of meiosis in the model plant Arabidopsis, we have established a live cell imaging setup to observe male meiocytes. Our method is based on the concomitant visualization of microtubules (MTs) and a meiotic cohesin subunit that allows following five cellular parameters: cell shape, MT array, nucleus position, nucleolus position, and chromatin condensation. We find that the states of these parameters are not randomly associated and identify 11 cellular states, referred to as landmarks, which occur much more frequently than closely related ones, indicating that they are convergence points during meiotic progression. As a first application of our system, we revisited a previously identified mutant in the meiotic A-type cyclin TARDY ASYNCHRONOUS MEIOSIS (TAM). Our imaging system enabled us to reveal both qualitatively and quantitatively altered landmarks in tam, foremost the formation of previously not recognized ectopic spindle- or phragmoplast-like structures that arise without attachment to chromosomes.

Small RNA-based prediction of hybrid performance in maize.

BACKGROUND: Small RNA (sRNA) sequences are known to have a broad impact on gene regulation by various mechanisms. Their performance for the prediction of hybrid traits has not yet been analyzed. Our objective was to analyze the relation of parental sRNA expression with the performance of their hybrids, to develop a sRNA-based prediction approach, and to compare it to more common SNP and mRNA transcript based predictions using a factorial mating scheme of a maize hybrid breeding program. RESULTS: Correlation of genomic differences and messenger RNA (mRNA) or sRNA expression differences between parental lines with hybrid performance of their hybrids revealed that sRNAs showed an inverse relationship in contrast to the other two data types. We associated differences for SNPs, mRNA and sRNA expression between parental inbred lines with the performance of their hybrid combinations and developed two prediction approaches using distance measures based on associated markers. Cross-validations revealed parental differences in sRNA expression to be strong predictors for hybrid performance for grain yield in maize, comparable to genomic and mRNA data. The integration of both positively and negatively associated markers in the prediction approaches enhanced the prediction accurary. The associated sRNAs belong predominantly to the canonical size classes of 22- and 24-nt that show specific genomic mapping characteristics. CONCLUSION: Expression profiles of sRNA are a promising alternative to SNPs or mRNA expression profiles for hybrid prediction, especially for plant species without reference genome or transcriptome information. The characteristics of the sRNAs we identified suggest that association studies based on breeding populations facilitate the identification of sRNAs involved in hybrid performance.

Parental Expression Variation of Small RNAs Is Negatively Correlated with Grain Yield Heterosis in a Maize Breeding Population.

Heterosis refers to a quantitative phenomenon in which F1 hybrid trait values exceed the mean of the parental values in a positive direction. Generally, it is dependent on a high degree of heterozygosity, which is maintained in hybrid breeding by developing parental lines in separate, genetically distinct heterotic groups. The mobility of small RNAs (sRNAs) that mediate epigenetic regulation of gene expression renders them promising candidates for modulating the action of combined diverse genomes in trans-and evidence already indicates their contribution to transgressive phenotypes. By sequencing small RNA libraries of a panel of 21 maize parental inbred lines we found a low overlap of 35% between the sRNA populations from both distinct heterotic groups. Surprisingly, in contrast to genetic or gene expression variation, parental sRNA expression variation is negatively correlated with grain yield (GY) heterosis. Among 0.595 million expressed sRNAs, we identified 9,767, predominantly 22- and 24-nt long sRNAs, which showed an association of their differential expression between parental lines and GY heterosis of the respective hybrids. Of these sRNAs, 3,485 or 6,282 showed an association with high or low GY heterosis, respectively, thus the low heterosis associated group prevailing at 64%. The heterosis associated sRNAs map more frequently to genes that show differential expression between parental lines than reference sets. Together these findings suggest that trans-chromosomal actions of sRNAs in hybrids might add up to a negative contribution in heterosis formation, mediated by unfavorable gene expression regulation. We further revealed an exclusive accumulation of 22-nt sRNAs that are associated with low GY heterosis in pericentromeric genomic regions. That recombinational suppression led to this enrichment is indicated by its close correlation with low recombination rates. The existence of this enrichment, which we hypothesize resulted from the separated breeding of inbred lines within heterotic groups, may have implications for hybrid breeding strategies addressing the recombinational constraints characteristic of complex crop genomes.

Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.

The ability to predict the agronomic performance of single-crosses with high precision is essential for selecting superior candidates for hybrid breeding. With recent technological advances, thousands of new parent lines, and, consequently, millions of new hybrid combinations are possible in each breeding cycle, yet only a few hundred can be produced and phenotyped in multi-environment yield trials. Well established prediction approaches such as best linear unbiased prediction (BLUP) using pedigree data and whole-genome prediction using genomic data are limited in capturing epistasis and interactions occurring within and among downstream biological strata such as transcriptome and metabolome. Because mRNA and small RNA (sRNA) sequences are involved in transcriptional, translational and post-translational processes, we expect them to provide information influencing several biological strata. However, using sRNA data of parent lines to predict hybrid performance has not yet been addressed. Here, we gathered genomic, transcriptomic (mRNA and sRNA) and metabolomic data of parent lines to evaluate the ability of the data to predict the performance of untested hybrids for important agronomic traits in grain maize. We found a considerable interaction for predictive ability between predictor and trait, with mRNA data being a superior predictor for grain yield and genomic data for grain dry matter content, while sRNA performed relatively poorly for both traits. Combining mRNA and genomic data as predictors resulted in high predictive abilities across both traits and combining other predictors improved prediction over that of the individual predictors alone. We conclude that downstream "omics" can complement genomics for hybrid prediction, and, thereby, contribute to more efficient selection of hybrid candidates.

Omics-based hybrid prediction in maize.

KEY MESSAGE: Complementing genomic data with other "omics" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits. Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream "omics" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of "omics" data. Here, we evaluate several "omics" predictors-genomic, transcriptomic and metabolic data-measured on parent lines at early developmental stages and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream "omics" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.

Analysis of wheat microspore embryogenesis induction by transcriptome and small RNA sequencing using the highly responsive cultivar "Svilena".

BACKGROUND: Microspore embryogenesis describes a stress-induced reprogramming of immature male plant gametophytes to develop into embryo-like structures, which can be regenerated into doubled haploid plants after whole genome reduplication. This mechanism is of high interest for both research as well as plant breeding. The objective of this study was to characterize transcriptional changes and regulatory relationships in early stages of cold stress-induced wheat microspore embryogenesis by transcriptome and small RNA sequencing using a highly responsive cultivar. RESULTS: Transcriptome and small RNA sequencing was performed in a staged time-course to analyze wheat microspore embryogenesis induction. The analyzed stages were freshly harvested, untreated uninucleate microspores and the two following stages from in vitro anther culture: directly after induction by cold-stress treatment and microspores undergoing the first nuclear divisions. A de novo transcriptome assembly resulted in 29,388 contigs distributing to 20,224 putative transcripts of which 9,305 are not covered by public wheat cDNAs. Differentially expressed transcripts and small RNAs were identified for the stage transitions highlighting various processes as well as specific genes to be involved in microspore embryogenesis induction. CONCLUSION: This study establishes a comprehensive functional genomics resource for wheat microspore embryogenesis induction and initial understanding of molecular mechanisms involved. A large set of putative transcripts presumably specific for microspore embryogenesis induction as well as contributing processes and specific genes were identified. The results allow for a first insight in regulatory roles of small RNAs in the reprogramming of microspores towards an embryogenic cell fate.

Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles.

BACKGROUND: Ridge regression models can be used for predicting heterosis and hybrid performance. Their application to mRNA transcription profiles has not yet been investigated. Our objective was to compare the prediction accuracy of models employing mRNA transcription profiles with that of models employing genome-wide markers using a data set of 98 maize hybrids from a breeding program. RESULTS: We predicted hybrid performance and mid-parent heterosis for grain yield and grain dry matter content and employed cross validation to assess the prediction accuracy. Prediction with a ridge regression model using random effects for mRNA transcription profiles resulted in similar prediction accuracies than employing the model to DNA markers. For hybrids, of which none of the parental inbred lines was part of the training set, the ridge regression model did not reach the prediction accuracy that was obtained with a model using transcriptome-based distances. CONCLUSION: We conclude that mRNA transcription profiles are a promising alternative to DNA markers for hybrid prediction, but further studies with larger data sets are required to investigate the superiority of alternative prediction models.

Genome-wide meta-analysis of maize heterosis reveals the potential role of additive gene expression at pericentromeric loci.

BACKGROUND: The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data. RESULTS: For the identification of genes underlying known heterotic QTL, we made use of tight associations between gene expression pattern and the trait of interest, identified by correlation analyses. Using this approach genes strongly associated with heterosis for GY were discovered to be clustered in pericentromeric regions of the complex maize genome. This suggests that expression differences of sequences in recombination-suppressed regions are important in the establishment of heterosis for GY in F1 hybrids and also in the conservation of heterosis for GY across genotypes. Importantly functional analysis of heterosis-associated genes from these genomic regions revealed over-representation of a number of functional classes, identifying key processes contributing to heterosis for GY. Based on the finding that the majority of the analyzed heterosis-associated genes were addtitively expressed, we propose a model referring to the influence of cis-regulatory variation on heterosis for GY by the compensation of fixed detrimental expression levels in parents. CONCLUSIONS: The study highlights the utility of a meta-analysis approach that integrates phenotypic and multi-level molecular data to unravel complex traits in plants. It provides prospects for the identification of genes relevant for QTL, and also suggests a model for the potential role of additive expression in the formation and conservation of heterosis for GY via dominant, multigenic quantitative trait loci. Our findings contribute to a deeper understanding of the multifactorial phenomenon of heterosis, and thus to the breeding of new high yielding varieties.