On the Cooperation between Epigenetics and Transcription Factor Networks in the Specification of Tissue Stem Cells

It is generally accepted that epigenetic modifications, such as DNA and histone methylations, affect transcription and that a gene’s transcription feeds back on its epigenetic profile. Depending on the epigenetic modification, positive and negative feedback loops have been described. Here, we study whether such interrelation are mandatory and how transcription factor networks affect it. We apply self-organizing map machine learning to a published data set on the specification and differentiation of murine intestinal stem cells in order to provide an integrative view of gene transcription and DNA, as well as histone methylation during this process. We show that, although gain/loss of H3K4me3 at a gene promoter is generally considered to be associated with its increased/decreased transcriptional activity, such an interrelation is not mandatory, i.e., changes of the modification level do not necessarily affect transcription. Similar considerations hold for H3K27me3. In addition, even strong changes in the transcription of a gene do not necessarily affect its H3K4me3 and H3K27me3 modification profile. We provide a mechanistic explanation of these phenomena that is based on a model of epigenetic regulation of transcription. Thereby, the analyzed data suggest a broad variance in gene specific regulation of histone methylation and support the assumption of an independent regulation of transcription by histone methylation and transcription factor networks. The results provide insights into basic principles of the specification of tissue stem cells and highlight open questions about a mechanistic modeling of this process.

[1]  A. Valencia,et al.  Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity , 2018, BioEssays : news and reviews in molecular, cellular and developmental biology.

[2]  Robert S. Illingworth,et al.  CpG islands influence chromatin structure via the CpG-binding protein Cfp1 , 2010, Nature.

[3]  Kristian Helin,et al.  Characterization of an antagonistic switch between histone H3 lysine 27 methylation and acetylation in the transcriptional regulation of Polycomb group target genes , 2010, Nucleic acids research.

[4]  Matthew T. Maurano,et al.  Role of DNA Methylation in Modulating Transcription Factor Occupancy. , 2015, Cell reports.

[5]  H. Binder,et al.  Bistable Epigenetic States Explain Age‐Dependent Decline in Mesenchymal Stem Cell Heterogeneity , 2016, Stem cells.

[6]  C. Allis,et al.  DNMT3L connects unmethylated lysine 4 of histone H3 to de novo methylation of DNA , 2007, Nature.

[7]  Hans Binder,et al.  oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor , 2015, Bioinform..

[8]  S. Sayols,et al.  Dynamic changes in chromatin states during specification and differentiation of adult intestinal stem cells , 2017, Nucleic acids research.

[9]  G. Felsenfeld,et al.  Silencing of transgene transcription precedes methylation of promoter DNA and histone H3 lysine 9 , 2004, The EMBO journal.

[10]  M. Loeffler,et al.  The Regulatory Capacity of Bivalent Genes—A Theoretical Approach , 2017, International journal of molecular sciences.

[11]  M. Loeffler,et al.  Targeting DNA hypermethylation: Computational modeling of DNA demethylation treatment of acute myeloid leukemia , 2017, Epigenetics.

[12]  Anushya Muruganujan,et al.  PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements , 2016, Nucleic Acids Res..

[13]  N. Brockdorff,et al.  The interplay of histone modifications – writers that read , 2015, EMBO reports.

[14]  Matthias Mann,et al.  Selective Anchoring of TFIID to Nucleosomes by Trimethylation of Histone H3 Lysine 4 , 2007, Cell.

[15]  Thimo Rohlf,et al.  Understanding epigenetic changes in aging stem cells – a computational model approach , 2014, Aging cell.

[16]  Navin Elango,et al.  DNA methylation and structural and functional bimodality of vertebrate promoters. , 2008, Molecular biology and evolution.

[17]  Dustin E. Schones,et al.  Genome-wide analysis of histone methylation reveals chromatin state-based regulation of gene transcription and function of memory CD8+ T cells. , 2009, Immunity.

[18]  Michael J. Ziller,et al.  Transcription factor binding dynamics during human ESC differentiation , 2015, Nature.

[19]  Hendrik G. Stunnenberg,et al.  The interplay of epigenetic marks during stem cell differentiation and development , 2017, Nature Reviews Genetics.

[20]  Sonja J. Prohaska,et al.  A Global Genome Segmentation Method for Exploration of Epigenetic Patterns , 2012, PloS one.

[21]  Sunil Kumar,et al.  Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features , 2016, BMC Bioinformatics.

[22]  Hans Binder,et al.  Gene expression density profiles characterize modes of genomic regulation: theory and experiment. , 2010, Journal of biotechnology.

[23]  Dustin E. Schones,et al.  Chromatin signatures in multipotent human hematopoietic stem cells indicate the fate of bivalent genes during differentiation. , 2009, Cell stem cell.

[24]  Yaron E. Antebi,et al.  Dynamics of epigenetic regulation at the single-cell level , 2016, Science.

[25]  B. O’Malley,et al.  Histone Marks in the 'Driver's Seat': Functional Roles in Steering the Transcription Cycle. , 2017, Trends in biochemical sciences.

[26]  Lukas Burger,et al.  Short sequences can efficiently recruit histone H3 lysine 27 trimethylation in the absence of enhancer activity and DNA methylation , 2014, Proceedings of the National Academy of Sciences.

[27]  P. Goyal,et al.  Targeting of EZH2 to a defined genomic site is sufficient for recruitment of Dnmt3a but not de novo DNA methylation , 2009, Epigenetics.

[28]  Peter A. Jones Functions of DNA methylation: islands, start sites, gene bodies and beyond , 2012, Nature Reviews Genetics.

[29]  S. Buratowski,et al.  The role of cotranscriptional histone methylations. , 2010, Cold Spring Harbor symposia on quantitative biology.

[30]  Andrew E. Teschendorff,et al.  Statistical and integrative system-level analysis of DNA methylation data , 2017, Nature Reviews Genetics.

[31]  Melissa J. Davis,et al.  Predicting expression: the complementary power of histone modification and transcription factor binding data , 2014, Epigenetics & Chromatin.

[32]  Tao Huang,et al.  TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information , 2010, BMC Bioinformatics.

[33]  T. Mikkelsen,et al.  Genome-wide maps of chromatin state in pluripotent and lineage-committed cells , 2007, Nature.

[34]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[35]  Sonja J. Prohaska,et al.  Transcriptional regulation by histone modifications: towards a theory of chromatin re-organization during stem cell differentiation , 2013, Physical biology.