Crop improvement often relies on random changes to cis-regulatory DNA. With CRISPR we can target genes directly, but finding desirable edits is hard. We screen CRISPR-like mutations in PsbS, Raf1, and SBPase to find edits that achieve the same thing as transgenic overexpression.
Nature Biotechnology
Savage Lab
A consistent ~500 bp core promoter near the TSS drives most effects, but genes differ in which mutation classes (substitutions, deletions, or insertions) boost expression. We tested thousands of insertions in this core region, uncovering motifs that strongly enhance expression.
Engineered TnpB genome editors for plants and human cells identified by ribonucleoprotein mutational scanning - @savagecatsonly.bsky.social go.nature.com/4loaOGn
TnpB endonucleases are engineered for improved genome editing.
Can CRISPR edits enable precise tuning of plant gene expression? We think: yes.
In our newest manuscript, we measured the effects of >30,000 CRISPR-like promoter mutations in sorghum protoplasts.
Savage Lab
Congrats to co-first authors @evangroover.bsky.social and
David Ding, and our talented collaborators, and many thanks to Christine Queitsch, @rnanerd.bsky.social, and Jen Sheen for protoplast wisdom. We look forward to translating what we learned to stable plants!
www.nature.com/articles/s41...
We show that genomic language models trained on plant genomes capture motif syntax and modestly predict the effects of hypomorphic mutations, but not hypermorphic ones. This suggests to us that problem-specific datasets like this will be essential to realize their promise.
Screening thousands of deletions per promoter, we map critical regulatory regions. Integrating thousands of single-base mutations pinpoints core motifs at base-pair resolution and reveals where and how promoters can be edited to change expression.
New preprint from Dave Savage (Savage Lab) and Yun S. Song: Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins. Read here: https://ow.ly/iJk950YCF8q
#AI #AIresearch
The size and location of mutations are highly consequential, and shape both RNA and protein output. For example, a 57-bp deletion in the PsbS promoter induces more protein production than a 585-bp viral enhancer, showing precise edits can rival or exceed transgenic engineering!
