Seifert, F. | Search bioRxiv

bioRxiv2018

Live cell imaging of meiosis in Arabidopsis thaliana - a landmark system

Meiosis is essential for sexual reproduction and key to the generation of genetic diversity. To reveal the robustness of meiocyte differentiation and progression through meiosis, we have here established a live cell imaging setup to follow the dynamics of individual male meiocytes in Arabidopsis. Our method is based on the concomitant visualization of microtubules and a meiotic cohesion subunit that allowed following five cellular parameters: cell shape, nucleus position, nucleolus position, chromatin condensation and microtubule array. We find that the states of these parameters are not randomly associated and identify 11 states, referred to as landmarks, that occur much more frequently than closely related states, indicating that they are convergent points of meiotic progression. With this, the here-presented landmark system represents a novel method to analyze meiosis not only allowing a high-temporal dissection but also providing new criteria to evaluate mutants or environmental effects on meiosis.

cell biology↗

bioRxiv2017

Omics-Based Hybrid Prediction In Maize

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 (WGP) 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.\n\nKey messageComplementing genomic data with other \"omics\" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.\n\nConflict of InterestThe authors declare that they have no conflict of interest.

genetics↗