How do you increase a crop trait by editing regulatory DNA instead of knocking a gene out?

In crop development the tractable move has long been to switch a gene off through disruption of the coding sequence, but the more valuable one is often to turn the right gene up, in the right tissue or at the right stage. That means changing cis-regulatory DNA rather than the coding sequence. And as genome editing itself has matured, the harder question is no longer whether you can make an edit, but which edit to make: a single promoter region can hold thousands of possible changes, and only a few will move the trait in the direction you want. Regulatory DNA is difficult to read from sequence, so the real constraint is knowing which change actually shifts expression, and by how much.

Computational prediction is a useful filter, and it keeps improving, but a predicted regulatory edit remains a hypothesis until it is measured, and the subtle, tissue-specific effects that many trait gains depend on are exactly where prediction is least reliable. Choosing an edit on prediction or intuition alone risks committing years of downstream work to a change that turns out to be silent. Measuring the functional effect first is what converts a plausible edit into an evidenced one.

SuRE™ is a massively parallel reporter assay that screens thousands of promoter variants and candidate edits in parallel in a plant-relevant system and reads out the transcriptional activity each one produces. Because every variant is barcoded many times, the assay resolves small differences, and because it can contrast tissues and conditions, it reports not just whether an edit changes expression but whether it does so where and when you need it. Run at saturation across a regulatory region, it returns a design map of which positions and changes matter, rather than a single guess.

The output is a ranked set of native regulatory edits, compatible with cisgenic and new genomic technique (NGT) approaches, and we can focus the variants we test on positions accessible to your editing chemistry. Breeders tell us this is what makes the results actionable: not a list of interesting sequences, but a shortlist of edits you can actually make and reasonably expect to work. Where the biology allows a strong, tissue-specific gain we aim to find it, and where it calls for a balance we show you the trade-off rather than assume one.

Every edit advanced into transformation, field testing and regulatory work carries years of cost, so choosing well early is where much of the value sits. Evaluating a broad set of edits at the start, on measured expression rather than intuition, concentrates that investment on the changes most likely to deliver, which lowers development risk and shortens the path to a trait that performs. The aim is not to generate more edits; it is to identify the few that actually move the trait.

Talk to us. If your trait needs more of the right gene in the right tissue, we can measure which regulatory edits deliver it before you commit to the field.