Interface-guided phenotyping of coding variants in the transcription factor RUNX1.

Kivilcim Ozturk, Rebecca Panwala, Jeanna Sheen, Kyle Ford, Nathan Jayne, Andrew Portell, Dong-Er Zhang, Stephan Hutter, Torsten Haferlach, Trey Ideker, Prashant Mali, Hannah Carter

Cell reports 2024 Jul 23

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Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations. Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Citation

Kivilcim Ozturk, Rebecca Panwala, Jeanna Sheen, Kyle Ford, Nathan Jayne, Andrew Portell, Dong-Er Zhang, Stephan Hutter, Torsten Haferlach, Trey Ideker, Prashant Mali, Hannah Carter. Interface-guided phenotyping of coding variants in the transcription factor RUNX1. Cell reports. 2024 Jul 23;43(7):114436