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      Comparative analysis of immune cells reveals a conserved regulatory lexicon

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          Summary

          Most well-characterized enhancers are deeply conserved. In contrast, genome-wide comparative studies of steady state systems showed that only a small fraction of active enhancers are conserved. To better understand conservation of enhancer activity we used a comparative genomics approach that integrates temporal expression and epigenetic profiles in an innate immune system. We found that gene expression programs diverge among mildly induced genes while being highly conserved for strongly induced genes. The fraction of conserved enhancers varies greatly across gene expression programs, with induced genes and early response genes in particular, being regulated by a higher fraction of conserved enhancers. Clustering of conserved accessible DNA sequence within enhancers resulted in over 80 sequence motifs including motifs for known factors as well as many with unknown function. We further show that the number of instances of these motifs is a strong predictor of the responsiveness of a gene to pathogen detection.

          eTOC blurb

          A comparison of the transcriptome and chromatin landscape between mouse and human innate immune cells reveals higher conservation of regulatory elements that control specific gene expression programs. These conserved elements contain a large set of constrained sequence motifs, which can be used as features to successfully predict gene induction in stimulated mouse and human innate immune cells.

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          Author and article information

          Journal
          101656080
          43733
          Cell Syst
          Cell Syst
          Cell systems
          2405-4712
          2405-4720
          27 January 2018
          14 February 2018
          28 March 2018
          28 March 2019
          : 6
          : 3
          : 381-394.e7
          Affiliations
          [1 ]Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA-01605, USA
          [2 ]Center for Computational Biology, University of California, Berkeley, Berkeley, CA-94720, USA
          [3 ]Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA-01605, USA
          [4 ]Bioinformatics Core, University of Massachusetts Medical School, Worcester, MA-01605, USA
          [5 ]Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA-94720, USA
          Author notes
          [6]

          Lead Contact

          [7]

          These authors contributed equally

          Article
          PMC5876141 PMC5876141 5876141 nihpa937964
          10.1016/j.cels.2018.01.002
          5876141
          29454939
          15752a5e-7c49-4609-adae-969c3b3412bf
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