Plant transformation timelines is widely recognised as a key bottleneck and subsequently receives significant attention in the community. What competing approaches to significantly accelerate transformation pipelines are you most excited by, and how do you see these technologies occupying either separate, complementary or competitive niches to your helper-satellite system? What is your subsequent response for positioning the development of your system in the larger ecosystem, based upon its relative strengths and weaknesses?
An exciting technology that is significantly important, and complimentary, is being able to undifferentiate any plant cell to a stem cell, and then edit it and regenerate a plant from it. I’ve talked to Dave Jackson, a PI at Cold Spring Harbor, who is working towards that goal and he’s expressed interest still in having a viral vector editor system, because even if you can place the cell in different states for transformation, you need a way to edit it.
The reason I’m focusing on the editing portion of the problem, is because my conversations with startups and research I’ve looked into for undifferentiating tree cells and my desire to focus on a tool that can be accessible in many contexts. My impression of cellular transitions is that it’s extremely challenging to know how hormones transition the cells to stay competent for editing, and then even requiring tissue scaffoldings for regeneration.
When I look around the community lab where I’ve learned, the only tools I have are pipettes, a thermo-cycler, a gel electrolysis setup, a -20C freezer, and an optical microscope that I can barely see a spore through. This resource-constrained context is important to me, both as a way to empower resource-constrained researchers and practioners around the world, but also pushing the idea to be better by being simpler.
Additionally, stem cell editing doesn’t allow you to on-demand inoculate an entire crop field against an abiotic stress or disease like a agro-delivered viral vector. However, I imagine it could very well suited for opening up a high-throughput, sophisticated plant transformation automation process. But even in that case, as I’ve talked to Dave, you still need a molecular tool for delivering edits, and a viral vector system does that for you.
To what extent are viral databases being developed by others, offering an opportunity for collaboration to expand capacity towards your Year One output? Beyond the mini-conference proposal, is there any formal strategy for combining efforts?
ViralZone, released in 2024 by the Swiss Institute of Bioinformatics, seems to be the best effort at organizing viral vector research and functional details, bridging citations across papers. While it’s not an effort that has created original research, it’s simple abstract illustrations were an essential aid as I tried to understand viral particle geometry and natural commonalities. It’s what prompted me to think beyond capsid size limits, and search for unique viral vector movement systems like helper-satellites.
I think it’s worth calling out this detail. I’ve found many databases and repositories to be good for holding information, but few have had a user-experience that’s helped me build intuition and think about systems engineering. For that reason, I’d absolutely want to push any original research this effort creates to their setup and find ways to partner since it has a long running institution keeping it online.
But while it’s the best available, as a plant bioengineer, I have a wish list of features; knowing which plant species a vector can infect, lists of suppressing viral sequences and proteins, ways of searching or linking commonality on viral protein functions, descriptions that talk about meristem infiltration or vertical transformation, etc. For a plant engineering effort, there could be a more fine tuned interface, that sits between Viralzone and a single species database like the Geminiviridae-Plant-Insect Database (GPIBase) that captures these various relationships between plants, viruses, and regulatory elements. And additionally, one that further focuses on satellite systems design, like a tool for designing primers or your vector.
To put it another way, it’s UX is cataloging viral vectors of every type. But for a plant biologist doing transformations or understanding disease spread, the key answers and things I need to do is more specific, so I think it could have a structured database and UI/UX more focused on that goal. Accessibility and reducing friction for the researcher, both for humans and machines, is as important as the data itself to me.
Thinking beyond the mini-conference, this research effort may be able to create it’s own interface and extension of a subset of this data. I don’t want to boil the ocean, but in a world where there are LLM-driven literature review pipelines, I do wonder if we have a continuously running literature review process that’s compiling and helping link observations in plant virology, and we have easily accessible data references to sequence/atomic structure, how that can fuel our engineering effort.
I can already share one example where that combing of literature is essential, just from this week of writing responses, where this one sentence changed how I understood the relationship between viral capsid and silencing proteins: “In another convincing example, the N-terminal domain of the CMV coat protein dampens the strong VSR activity of 2b (X. P. Zhang et al., 2017b), resulting in early, transient invasion of meristems and subsequent recovery from acute symptoms.” (https://pmc.ncbi.nlm.nih.gov/articles/PMC8408453) — without this detail I would have naively thought I could transfer a suppressor protein from one viral vector to another. These are the sort of details getting missed in a generalized viral vector database.