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Functional conservation of sequence determinants at rapidly evolving regulatory regions across mammals

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  • Iksoo Huh
  • Isabel Mendizabal
  • Taesung Park
  • Soojin V Yi

Abstract

Recent advances in epigenomics have made it possible to map genome-wide regulatory regions using empirical methods. Subsequent comparative epigenomic studies have revealed that regulatory regions diverge rapidly between genome of different species, and that the divergence is more pronounced in enhancers than in promoters. To understand genomic changes underlying these patterns, we investigated if we can identify specific sequence fragments that are over-enriched in regulatory regions, thus potentially contributing to regulatory functions of such regions. Here we report numerous sequence fragments that are statistically over-enriched in enhancers and promoters of different mammals (which we refer to as ‘sequence determinants’). Interestingly, the degree of statistical enrichment, which presumably is associated with the degree of regulatory impacts of the specific sequence determinant, was significantly higher for promoter sequence determinants than enhancer sequence determinants. We further used a machine learning method to construct prediction models using sequence determinants. Remarkably, prediction models constructed from one species could be used to predict regulatory regions of other species with high accuracy. This observation indicates that even though the precise locations of regulatory regions diverge rapidly during evolution, the functional potential of sequence determinants underlying regulatory sequences may be conserved between species.Author summary: Regions of the genome that do not encode genes but affect expression of other genes, such as enhancers and promoters, are referred to as regulatory regions. Because of their regulatory functions, it was thought that enhancers and promoters should be evolutionarily conserved. Regulatory regions can be now epigenomically identified because they are marked by specific modifications of histone tails at the chromatin level. Interestingly, when we compare epigenomically identified regulatory regions from different mammals, the specific positions of regulatory regions are often divergent between species. Enhancers in particular are highly divergent between species. In this study, we show that we can find sequence fragments that are statistically enriched in enhancers and promoters of different species, and that the degree of statistical enrichment can explain different levels of evolutionary sequence conservation between enhancers and promoters. We further constructed predictive models of enhancers and promoters using the enriched sequence fragments, and show that these models can not only accurately predict enhancers and promoters of the same species, but works comparably well when applied to other species. These results indicate that even though the specific positions of regulatory regions have diverged between species, the functions of sequence fragments that comprise those regions may be conserved.

Suggested Citation

  • Iksoo Huh & Isabel Mendizabal & Taesung Park & Soojin V Yi, 2018. "Functional conservation of sequence determinants at rapidly evolving regulatory regions across mammals," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-21, October.
  • Handle: RePEc:plo:pcbi00:1006451
    DOI: 10.1371/journal.pcbi.1006451
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    1. Len A. Pennacchio & Nadav Ahituv & Alan M. Moses & Shyam Prabhakar & Marcelo A. Nobrega & Malak Shoukry & Simon Minovitsky & Inna Dubchak & Amy Holt & Keith D. Lewis & Ingrid Plajzer-Frick & Jennifer , 2006. "In vivo enhancer analysis of human conserved non-coding sequences," Nature, Nature, vol. 444(7118), pages 499-502, November.
    2. Michael Z. Ludwig & Casey Bergman & Nipam H. Patel & Martin Kreitman, 2000. "Evidence for stabilizing selection in a eukaryotic enhancer element," Nature, Nature, vol. 403(6769), pages 564-567, February.
    3. Helena Santos-Rosa & Robert Schneider & Andrew J. Bannister & Julia Sherriff & Bradley E. Bernstein & N. C. Tolga Emre & Stuart L. Schreiber & Jane Mellor & Tony Kouzarides, 2002. "Active genes are tri-methylated at K4 of histone H3," Nature, Nature, vol. 419(6905), pages 407-411, September.
    4. Annie E. Tsong & Brian B. Tuch & Hao Li & Alexander D. Johnson, 2006. "Evolution of alternative transcriptional circuits with identical logic," Nature, Nature, vol. 443(7110), pages 415-420, September.
    5. Guo-Cheng Yuan & Jun S Liu, 2008. "Genomic Sequence Is Highly Predictive of Local Nucleosome Depletion," PLOS Computational Biology, Public Library of Science, vol. 4(1), pages 1-11, January.
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    7. Jacob F. Degner & Athma A. Pai & Roger Pique-Regi & Jean-Baptiste Veyrieras & Daniel J. Gaffney & Joseph K. Pickrell & Sherryl De Leon & Katelyn Michelini & Noah Lewellen & Gregory E. Crawford & Matth, 2012. "DNase I sensitivity QTLs are a major determinant of human expression variation," Nature, Nature, vol. 482(7385), pages 390-394, February.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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