Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in Mouse Embryonic Stem Cells

  1. Dana M King
  2. Clarice Kit Yee Hong
  3. James L Shepherdson
  4. David M Granas
  5. Brett B Maricque
  6. Barak Cohen  Is a corresponding author
  1. Washington University in St Louis School of Medicine, United States

Abstract

In embryonic stem cells (ESCs), a core transcription factor (TF) network establishes the gene expression program necessary for pluripotency. To address how interactions between four key TFs contribute to cis-regulation in mouse ESCs, we assayed two massively parallel reporter assay (MPRA) libraries composed of binding sites for SOX2, POU5F1 (OCT4), KLF4, and ESRRB. Comparisons between synthetic cis-regulatory elements and genomic sequences with comparable binding site configurations revealed some aspects of a regulatory grammar. The expression of synthetic elements is influenced by both the number and arrangement of binding sites. This grammar plays only a small role for genomic sequences, as the relative activities of genomic sequences are best explained by the predicted affinity of binding sites, regardless of binding site identity and positioning. Our results suggest that the effects of transcription factor binding sites (TFBS) are influenced by the order and orientation of sites, but that in the genome the overall occupancy of TFs is the primary determinant of activity.

Data availability

Sequencing data has been deposited in GEO under accession code GSE120240.Any additional data generated during this study are included in the manuscript and supporting files.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Dana M King

    Edison Center for Genome Sciences and Systems Biology, Washington University in St Louis School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4635-5272
  2. Clarice Kit Yee Hong

    Edison Center for Genome Sciences and Systems Biology, Washington University in St Louis School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. James L Shepherdson

    Edison Center for Genome Sciences and Systems Biology, Washington University in St Louis School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. David M Granas

    Edison Center for Genome Sciences and Systems Biology, Washington University in St Louis School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Brett B Maricque

    Edison Center for Genome Sciences and Systems Biology, Washington University in St Louis School of Medicine, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Barak Cohen

    Edison Center for Genome Sciences and Systems Biology, Washington University in St Louis School of Medicine, St Louis, United States
    For correspondence
    cohen@wustl.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3350-2715

Funding

National Institutes of Health (R01 GM092910)

  • Barak Cohen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Patricia J Wittkopp, University of Michigan, United States

Version history

  1. Received: August 20, 2018
  2. Accepted: February 7, 2020
  3. Accepted Manuscript published: February 11, 2020 (version 1)
  4. Version of Record published: March 17, 2020 (version 2)

Copyright

© 2020, King et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,003
    views
  • 563
    downloads
  • 41
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Dana M King
  2. Clarice Kit Yee Hong
  3. James L Shepherdson
  4. David M Granas
  5. Brett B Maricque
  6. Barak Cohen
(2020)
Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in Mouse Embryonic Stem Cells
eLife 9:e41279.
https://doi.org/10.7554/eLife.41279

Share this article

https://doi.org/10.7554/eLife.41279

Further reading

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

    1. Cell Biology
    2. Computational and Systems Biology
    Thomas Grandits, Christoph M Augustin ... Alexander Jung
    Research Article

    Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.