Project Outline

Living creatures depend on signals from their surroundings to navigate the various stages of their lives. Yet, the ever-changing nature of these environmental cues poses a significant challenge in precisely timing critical life decisions. What's fascinating is that many organisms, including plants, have developed remarkable resilience in responding to these environmental signals, even though they lack centralized information-processing systems, like our brains.

Compared to animals, somatic cells of plants can be much more easily reprogrammed to regenerate whole new organs and, sometimes, entire individuals. This unique biological property has enabled plants to reproduce either sexually or asexually according to their habitat. The ability of plant cells to readily acquire a totipotent state has been attributed to their flexible epigenetic states. As a result, plant totipotency has been traditionally exploited by humans for the clonal propagation and genetic manipulation of economically important species. We have recently found that clonal plants have the capacity to remember previous exposures to environmental insults, which we termed “environmental memory”. This memory is linked to the accumulation of epigenetic modifications that allow clonal plants to become more resistant to environmental stress. The aim of this project is to define the impact that stress-mediated epigenetic changes have on chromatin 3D conformation and the role of these changes stress adaptation in plants.

References

Wibowo, A., Becker, C., Durr, J., Price, J., Papareddy, R., Saintain, Q., Spaepen, S., Hilton, S., Bending.,G., Schulze-Lefert, P., Weigel., D. and Gutierrez-Marcos J.F. (2018) Incomplete reprogramming of cell-specific epigenetic marks during asexual reproduction leads to heritable phenotypic variation in plants PNAS 116:8037-8042

Techniques

The student will investigate the molecular changes associated with stress memory in Arabidopsis using a multidisciplinary approach (microscopy, next-generation-sequencing data analysis and epi/genome editing). The groups of JGM and LF have developed all the material and methodology required for their analysis. The student will also use customized computational models (machine learning) to enable the high-throughput analysis of chromatin dynamics.

Lab-Techniques: Confocal Microscopy, cell culture, genome sequencing.

Computational techniques: computational tools for image data and genomic analysis.