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Measuring the effects of regulatory variants in an endogenous context

Michael Montgomery @michaeltmont.bsky.social · Jun 11

Had a lot of fun writing this “tools of the trade” highlight for our Variant-EFFECTS technology. Check it out! 🛠️

Nature Reviews Genetics @natrevgenet.nature.com · Jun 11

New online! Measuring the effects of regulatory variants in an endogenous context

Nature Reviews Genetics, Published online: 11 June 2025; doi:10.1038/s41576-025-00863-wIn this Tools of the Trade article, Michael Montgomery presents Variant-EFFECTS, a high-throughput and generalizable approach that combines prime editing, flow cytometry, sequencing, and computational analysis to quantify the effects of regulatory variants.

www.nature.com

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Michael Montgomery @michaeltmont.bsky.social · Apr 24

Very cool work! I'm particularly excited about the integration of multiple models/prediction heads in this approach, given how many aspects of gene regulation likely need to be considered to achieve a specific quantitative design goal.

Jacob Schreiber @jmschreiber91.bsky.social · Apr 24

Our preprint on designing and editing cis-regulatory elements using Ledidi is out! Ledidi turns *any* ML model (or set of models) into a designer of edits to DNA sequences that induce desired characteristics.

Preprint: www.biorxiv.org/content/10.1...
GitHub: github.com/jmschrei/led...

Programmatic design and editing of cis-regulatory elements

The development of modern genome editing tools has enabled researchers to make such edits with high precision but has left unsolved the problem of designing these edits. As a solution, we propose Ledi...

www.biorxiv.org

Michael Montgomery @michaeltmont.bsky.social · Apr 19

🫰🫰

Michael Montgomery @michaeltmont.bsky.social · Apr 19

Thank you!!!

Michael Montgomery @michaeltmont.bsky.social · Apr 18

Thanks Josh!!! Trying to extend it to a bunch of other promoters now 🫡

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Michael Montgomery @michaeltmont.bsky.social · Apr 18

Thank you, Joseph!! That is so great to hear. Time to think about what to try to convince you of next 🤔

Michael Montgomery @michaeltmont.bsky.social · Apr 18

Thank you! And thanks again for the guidance re MPRA analysis!

Michael Montgomery @michaeltmont.bsky.social · Apr 18

Thank you!! 😊

Michael Montgomery @michaeltmont.bsky.social · Apr 18

Thank you so much! 😊

Michael Montgomery @michaeltmont.bsky.social · Apr 17

Thank you for highlighting our work!

Michael Montgomery @michaeltmont.bsky.social · Apr 17

thank you for promoting our work! :)

Michael Montgomery @michaeltmont.bsky.social · Apr 17

This project was a true team effort. Big thanks to Gabriella Martyn for co-leading, our advisor @jengreitz.bsky.social for mentorship and support, and the whole Variant-EFFECTS team for their essential contributions. Anything is possible when collaborations happen between friends.

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Michael Montgomery @michaeltmont.bsky.social · Apr 17

In summary, Variant-EFFECTS can dissect and reprogram endogenous regulatory elements to tune gene expression in a cell type-specific manner. This approach is broadly applicable for studying gene regulation and characterizing effects of both designed and disease-associated variants.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

(iii) A lentiMPRA experiment measuring the effects of the same PPIF promoter variants assayed with Variant-EFFECTS. This revealed systematic differences between effects measured in an endogenous versus reporter context, highlighting the strengths of Variant-EFFECTS.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

(ii) A mutagenesis screen targeting the IL2RA promoter & measuring effects using an alternative assay, which demonstrated how Variant-EFFECTS can be generalized to study the quantitative function of regulatory sequences that control various cellular phenotypes, including RNA & protein expression.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

(i) A validation screen of 10 significant variants from the original PPIF enhancer screen, which demonstrated the reproducibility and robustness of Variant-EFFECTS measurements across screens performed with varied parameters (MOI, coverage, guide, representation).

Michael Montgomery @michaeltmont.bsky.social · Apr 17

In revision, we added several new experiments and analyses. While these are detailed in the supplement of our manuscript, I would like to highlight a few that were key to demonstrating the capabilities of our technology:

Michael Montgomery @michaeltmont.bsky.social · Apr 17

The experiments we present here—mapping key sites within endogenous elements, identifying efficient prime editing sites, then iteratively applying computational modeling and Variant-EFFECTS—provide a generalizable strategy to develop genome editing reagents that create desired changes in expression.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

In total, 78% of edits designed to increase or decrease expression in either cell type significantly affected PPIF expression in the intended direction, although to varying degrees.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

The range of effect sizes induced by these ML-designed edits was striking. We found that small changes (4-10bp) to the PPIF promoter sequence, delivered via CRISPR prime editing, can turn PPIF gene expression up by more than 2-fold (+140%) or down by more than 7-fold (-86%)!

Michael Montgomery @michaeltmont.bsky.social · Apr 17

CRISPR edits to endogenous DNA that tune expression to a desired level (e.g., up 2-fold) could enable new therapeutic applications for many diseases. To test if we could achieve such changes with CRISPR prime editing, we used an ML-guided approach to rationally design edits to the PPIF promoter.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

Additionally, none of the models appeared to correctly interpret the effects of edits to the distal enhancer—either due to limited context in local models or failure of long-range models to learn its importance. However, our results suggest an alternative way to capture long-range variant effects.

Michael Montgomery @michaeltmont.bsky.social · Apr 17

Through this analysis, we found that chromatin accessibility models outperformed the corresponding expression models for predicting effects on gene expression, similar to previous analysis of data from plasmid reporter assays and natural genetic variation.

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Michael Montgomery @michaeltmont.bsky.social · Apr 17

To illustrate how gold-standard experimental measurements of variant effects on expression in endogenous context can be leveraged for quantitative evaluation of predictive models, we benchmarked the accuracy of recent deep learning models of gene regulation against our new Variant-EFFECTS dataset.