Submitted by Patrick M Shih on
What determines the endless variation of plant shapes found in nature? This question has long fascinated biologists and laypersons alike. Using synthetic biology to rationally engineering plant development would be a powerful demonstration that we have found answers to this question. Moreover, plant shape has a very important role in agriculture, with domesticated plants differing from their wild relatives largely based on their development. Hence, engineering plant shape would also have a direct technological application.
At its core, plant development depends on the communication between neighboring cells. Since plant cells cannot move, their developmental fate is determined by the signals they receive from their neighbors. These signals are often RNAs and proteins produced in one cell that move to neighboring cells to regulate their gene expression. This movement occurs via cytoplasmic channels between cells called plasmodesmata.
Over the last decade the field of synthetic biology has created a myriad of RNA-based tools to control gene expression in eukaryotes, including plants. Thus, it should in principle be possible to use these tools to engineer synthetic cell-cell communication in plants via movement through plasmodesmata channels. For example, a cell could be engineered to produce a synthetic RNA that then travels to neighboring cells and instructs them to adopt a given gene expression program. However, plasmodesmata transport is highly regulated, RNAs do not normally move from cell to cell. Interestingly, plant RNA viruses can override this regulation to freely move between plant cells using plasmodesmata.
We seek to engineer plant RNA viruses stripping them of all pathogenic and unnecessary components to create synthetic cell-to-cell communication devices. These engineered minimal viruses will carry synthetic RNAs (such as CRISPR gRNAs) to regulate gene expression in plant cells. To follow the movement of these engineered viruses we will tag them with fluorescent proteins. On the other hand, we will measure the effect of the synthetic RNAs carried by the synthetic viruses using fluorescent protein reporters of gene expression. We will use live cell confocal fluorescence microscopy to measure the performance of different synthetic cell-to-cell communication strategies. We will then analyze the results using computational image analysis. Finally, we will contrast the results with simple mathematical models of how the system is supposed to operate.
Previous work in the lab has demonstrated that this strategy is feasible. Thus, the project is ripe for a student to start using already available tools to design synthetic gene expression patterns in plant tissues. The setup in the Shih lab allows us to perform these experiments in just a few days, which promises rapid progress. Note that most of the experimental side of this research will be conducted at JBEI.
The undergraduate role encompasses the following list. The items are ordered by their level of complexity and the undergraduate's role will move up in the list as their research progresses. The student will receive guidance from the mentor at each step.
- Assemble synthetic DNA constructs encoding engineered plant viruses.
- Express these constructs in plant leaves and measure their performance using a fluorescence plate reader and/or confocal fluorescence microscopy.
- Analyze these data using already existing Python scripts or their own new code.
- Optional: Create mathematical models of the experiment (e.g. models of genetic interactions between cells).
- Use experimental data analyzed in Python to generate publication-quality figures. Discuss these figures with the mentor to derive conclusions and inform future experiments. Compare figures with expectations from mathematical models.
- Independently devise new experiments based on 1-5.
Most of this work will be conducted at JBEI.
The only required qualification is a thorough understanding of the central dogma of molecular biology in bacteria and eukaryotes. No other specific qualifications are required but any combination of the following skills would be welcomed. The undergraduate is not necesarily expected to have experience with this list.
- Familiarity with molecular cloning (in particular Gibson assembly).
- Experience with bacterial work.
- Experience coding in Python.
- Familiarity with mathematical modeling of genetic networks.
- Familiarity with fluorescence microscopy.