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Multilayer epigenetic analysis reveals novel transcription factor networks in CD8 T cells

Cellular & Molecular Immunology volume 15, pages 199–202 (2018)Cite this article

Currently, multiple technologies are available to study gene expression and epigenetic regulation on a genome-wide level. High-throughput RNA-based sequencing (RNA-seq) allows global transcriptome analysis down to the single cell level10 and includes long non-coding regulatory RNAs and microRNAs.11, 12 Histones constitute the major proteins of chromatin and package the DNA into the nucleosomes. The five major classes of histones are categorized into the core and linker histone superfamilies. Modifications of histones (for example, methylation or acetylation of lysine residues) are involved in the control of transcription. These epigenetic ‘marks’ include the trimethylation of histone H3 at lysine 4 (H3K4me3) and the acetylation of histone H3 at lysine 27 (H3K27ac), which are associated with actively transcribed genes, and the trimethylation of histone H3 at lysine 27 (H3K27me3), which is associated with repressed transcription. Histone modifications can be analyzed by chromatin immunoprecipitation followed by deep sequencing (ChIP-seq).13 Another important level of gene regulation is the accessibility of chromatin to transcription factors (TFs). The assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) introduced by the Greenleaf lab is a powerful method for the simultaneous analysis of chromatic accessibility, nucleosome positioning and TF occupancy.14 Yu et al. combined these methods to study TF networks during the ongoing CD8 T-cell immune response to L. monocytogenes infection. To this end, they transferred CD8 T cells from OT-1 T-cell receptor (TCR) transgenic mice into host mice, which were then infected with L. monocytogenes expressing recombinant ovalbumin (OVA). The transgenic OT-1 TCR recognizes an OVA peptide presented by MHC class I molecules. This elegant system allowed the researchers to monitor clonal CD8 T-cell responses upon L. monocytogenes infection of the host mice. However, the authors reported similar ATAC-seq profiles when they analyzed the polyclonal effector and memory CD8 T cells rather than the clonal OT-1 CD8 T cells. Eight days after infection, they sorted the CD8 T cells into the TE (KLRG1highIL-7Rlow) and MP (KLRG1lowIL-7Rhigh) subsets. Sixty days after infection, persisting memory CD8 T cells (KLRG1lowIL-7Rhigh) were also isolated. Together with the naive CD8 T cells (CD44low), all the CD8 subsets were subjected to RNA-seq, ChIP-seq and ATAC-seq with a focus on how TE and MP could be specifically characterized. The combined ChIP-seq analysis of given histone marks together with the ATAC-seq analysis of accessible regulatory regions allowed the authors to identify subset-specific open regions containing enhancers and promoters, which were probed with 761 unique known TF binding motifs. These studies revealed CD8 subset-specific depletion or enrichment of binding motifs for many TFs. Although in some cases enrichment of binding motifs for TFs correlated with gene expression and function, this correlation was not found in other cases, indicating that in addition to differential expression the putative binding of TFs is a decisive factor. Yu et al. combined the information on TF binding motifs and chromatin accessibility to establish regulatory networks for the prediction of putative TF targets. This approach revealed novel insights into the role of T-bet (usually considered the T helper 1-specific master TF) in CD8 T-cell differentiation. Although approximately 60% of T-bet target genes were shared by the TE and MP subsets, the model predicted regulation of selected other genes in the TE versus MP subsets, one of which was the anti-apoptotic gene Bcl-2. Interestingly, the authors observed reduced accumulation of MP CD8 T cells in mice lacking T-bet, which was possibly related to the reduced Bcl-2 expression in these mice. Given that targets for many TFs have not been identified, a major achievement in the study of Yu et al. was the development of a new bioinformatics method (the PageRank algorithm) that allowed the authors to rank TFs in the regulatory network according to their importance. This analysis revealed more known TFs involved in CD8 T-cell differentiation than the previous motif-enrichment analysis. The authors validated this novel bioinformatics strategy for two selected TFs (YY1 and Nr3c1), both of which had not been connected previously to effector or memory CD8 T-cell development during infection. Importantly, silencing of YY1 by retroviral transduction of a specific short hairpin RNA (shRNA) into OT-1 CD8 T cells before transfer into host mice and subsequent infection with OVA-expressing L. monocytogenes strongly reduced the differentiation of TE cells, which was in line with the PageRank predicted high ranking of YY1 in the TE but not the MP subsets. Conversely, the nuclear glucocorticoid receptor Nr3cl was ranked highly in the MP subset, and shRNA-mediated silencing of Nr3cl in OT-1 T cells reduced the number of MP CD8 T cells upon infection with L. monocytogenes. These results nicely illustrate the power of the multilayer approach used by Yu et al. to dissect the complexity of TF networks.5

In the past few years, several studies have characterized subset specification into effector and memory CD8 T cells during an ongoing immune response at the epigenetic level. These studies have focused on the analysis of repressive and permissive histone marks at the promoter region of genes important for CD8 T-cell differentiation.15, 16 Moreover, TFs associated with CD8 memory T cells have been identified in genome-wide regulatory networks based on data sets present in public repositories.17 Taken together, the results from previous studies showed that gene expression profiling was insufficient to precisely characterize effector and memory CD8 T cells. The new study from Yu et al. advances the field because they demonstrate the power of multilayer epigenetic analysis to dissociate effector and memory T-cell differentiation. Furthermore, this study also illustrates that sophisticated and innovative bioinformatics tools are required to obtain the maximum benefits from huge experimental data sets. In the future, the challenge to be addressed is the integration of long-range regulatory element interactions and associated transcriptional regulation in the context of the overall epigenetic landscape.

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  1. Institute of Immunology, University of Kiel, Kiel, D-24105, Germany

    Jaydeep Bhat & Dieter Kabelitz

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Correspondence to Dieter Kabelitz.

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Bhat, J., Kabelitz, D. Multilayer epigenetic analysis reveals novel transcription factor networks in CD8 T cells. Cell Mol Immunol 15, 199–202 (2018). https://doi.org/10.1038/cmi.2017.46

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