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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
  • , Nadav Ahituv

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)411-420
Number of pages10
JournalNature
Volume639
Issue number8054
DOIs
StatePublished - Mar 13 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • General

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