Conservation of trans-acting circuitry during mammalian regulatory evolution


Andrew B. Stergachis, Shane Neph, Richard Sandstrom, Eric Haugen, Alex P. Reynolds, Miaohua Zhang, Rachel Byron, Theresa Canfield, Sandra Stelhing-Sun, Kristen Lee, Robert E. Thurman, Shinny Vong, Daniel Bates, Fidencio Neri, Morgan Diegel, Erika Giste, Douglas Dunn, Jeff Vierstra, R. Scott Hansen, Audra K. Johnson, Peter J. Sabo, Matthew S. Wilken, Thomas A. Reh, Piper M. Treuting, Rajinder Kaul et al.

Nature 515, 365–370 (20 November 2014) doi:10.1038/nature13972
Received 21 February 2014 Accepted 15 October 2014 Published online 19 November 2014


The basic body plan and major physiological axes have been highly conserved during mammalian evolution, yet only a small fraction of the human genome sequence appears to be subject to evolutionary constraint. To quantify cis- versus trans-acting contributions to mammalian regulatory evolution, we performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ~8.6 million transcription factor (TF) occupancy sites at nucleotide resolution. Here we show that mouse TF footprints conjointly encode a regulatory lexicon that is ~95% similar with that derived from human TF footprints. However, only ~20% of mouse TF footprints have human orthologues. Despite substantial turnover of the cis-regulatory landscape, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Furthermore, the higher-level organization of mouse TF-to-TF connections into cellular network architectures is nearly identical with human. Our results indicate that evolutionary selection on mammalian gene regulation is targeted chiefly at the level of trans-regulatory circuitry, enabling and potentiating cis-regulatory plasticity.


Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications

  1. Eduardo G. Gusmao1,*,
  2. Christoph Dieterich2,
  3. Martin Zenke3,4 and
  4. Ivan G. Costa1,5,6,*

+Author Affiliations

  1. 1IZKF Computational Biology Research Group, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074 Aachen, 2Computational RNA Biology Lab and Bioinformatics Core, Max Planck Institute for Biology of Ageing, 50931 Cologne, 3Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University Medical School, 52074, 4Helmholtz Institute for Biomedical Engineering, 52074, 5Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, 52062 Aachen, Germany and 6Center of Informatics, Federal University of Pernambuco, 50740560 Recife-PE, Brazil
  1. *To whom correspondence should be addressed
  • Received October 28, 2013.
  • Revision received June 27, 2014.
  • Accepted July 25, 2014.


Motivation: The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction.

Results: We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such asde novo motif finding.

Availability and implementation: Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm.

Contact: ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de

Supplementary information: Supplementary data are available atBioinformatics online.