Massively parallel characterization of transcriptional regulatory elements

Vikram Agarwal (), Fumitaka Inoue, Max Schubach, Dmitry Penzar, Beth K. Martin, Pyaree Mohan Dash, Pia Keukeleire, Zicong Zhang, Ajuni Sohota, Jingjing Zhao, Ilias Georgakopoulos-Soares, William S. Noble, Galip Gürkan Yardımcı, Ivan V. Kulakovskiy, Martin Kircher, Jay Shendure () and Nadav Ahituv ()
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Vikram Agarwal: University of Washington
Fumitaka Inoue: University of California, San Francisco
Max Schubach: Berlin Institute of Health at Charité-Universitätsmedizin Berlin
Dmitry Penzar: Russian Academy of Sciences
Beth K. Martin: University of Washington
Pyaree Mohan Dash: Berlin Institute of Health at Charité-Universitätsmedizin Berlin
Pia Keukeleire: University of Lübeck
Zicong Zhang: Kyoto University
Ajuni Sohota: University of California, San Francisco
Jingjing Zhao: University of California, San Francisco
Ilias Georgakopoulos-Soares: The Pennsylvania State University College of Medicine
William S. Noble: University of Washington
Galip Gürkan Yardımcı: University of Washington
Ivan V. Kulakovskiy: Russian Academy of Sciences
Martin Kircher: Berlin Institute of Health at Charité-Universitätsmedizin Berlin
Jay Shendure: University of Washington
Nadav Ahituv: University of California, San Francisco

Nature, 2025, vol. 639, issue 8054, 411-420

Abstract: Abstract The human genome contains millions of candidate cis-regulatory elements (cCREs) with cell-type-specific activities that shape both health and many disease states1. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these cCREs. Here we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of more than 680,000 sequences, representing an extensive set of annotated cCREs among three cell types (HepG2, K562 and WTC11), and found that 41.7% of these sequences were active. By testing sequences in both orientations, we find promoters to have strand-orientation biases and their 200-nucleotide cores to function as non-cell-type-specific ‘on switches’ that provide similar expression levels to their associated gene. By contrast, enhancers have weaker orientation biases, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict cCRE function and variant effects with high accuracy, delineate regulatory motifs and model their combinatorial effects. Testing a lentiMPRA library encompassing 60,000 cCREs in all three cell types further identified factors that determine cell-type specificity. Collectively, our work provides an extensive catalogue of functional CREs in three widely used cell lines and showcases how large-scale functional measurements can be used to dissect regulatory grammar.

Date: 2025
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DOI: 10.1038/s41586-024-08430-9

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