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. 2017 Feb;2(8):eaag0192.
doi: 10.1126/sciimmunol.aag0192. Epub 2017 Feb 17.

Epigenomics of human CD8 T cell differentiation and aging

David M Moskowitz  1   2 , David W Zhang  1   3 , Bin Hu  3 , Sabine Le Saux  3 , Rolando E Yanes  3 , Zhongde Ye  3 , Jason D Buenrostro  1   4 , Cornelia M Weyand  3 , William J Greenleaf  1 , Jörg J Goronzy  3
Affiliations

Epigenomics of human CD8 T cell differentiation and aging

David M Moskowitz et al. Sci Immunol. 2017 Feb.

Abstract

The efficacy of the adaptive immune response declines dramatically with age, but the cell-intrinsic mechanisms driving immune aging in humans remain poorly understood. Immune aging is characterized by a loss of self-renewing naïve cells and the accumulation of differentiated but dysfunctional cells within the CD8 T cell compartment. Using ATAC-seq, we inferred the transcription factor binding activities correlated with naive and central and effector memory CD8 T cell states in young adults. Integrating our results with RNA-seq, we identified transcription networks associated with CD8 T cell differentiation, with prominent roles implicated for BATF, ETS1, Eomes, and Sp1. Extending our analysis to aged humans, we found that the differences between the memory and naive subsets were largely preserved across age, but that naive and central memory cells from older individuals exhibited a shift toward more differentiated patterns of chromatin openness. Additionally, aged naive cells displayed a loss in chromatin accessibility at gene promoters, largely associated with a decrease in NRF1 binding. This shift was implicated in a marked drop-off in the ability of the aged naive cells to transcribe respiratory chain genes, which may explain the reduced capacity of oxidative phosphorylation in older naïve cells. Our findings identify BATF- and NRF1-driven gene regulation as potential targets for delaying CD8 T cell aging and restoring function.

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Figures

Fig. 1
Fig. 1. ATAC-seq open chromatin maps capture characteristic signatures that correlate with phenotypically defined CD8 T cell subsets
(A) T cells were sorted into naïve (CD28hiCD45RAhiCD62Lhi), central memory (CM; CD45RAloCD62Lhi), and effector memory (EM; CD45RAloCD62Llo) CD8 T cell subsets, as shown in Fig. S1. Contour plots show purity of sorted populations. (B) ATAC-seq signal tracks in naive, CM, and EM subsets at selected functionally relevant genes (CD8, CD4, IFNG and GZMB). Small boxes in gene diagrams (bottom) represent exons. Each subset’s count is aggregated across the ten constituent donors. The y-axes are in units of reads per million reads in peaks. (C) Proportion of chromatin states in naive CD8 T cells, genome-wide (left), and for ATAC-seq peaks (right). Chromatin states were called by the Roadmap Epigenomics Project using ChromHMM, and we aggregated the state calls over functionally related subclasses. Percentages are as fractions of base pairs. (D) Heat map of Spearman correlation coefficients of openness between naïve (Na), CM, and EM samples. Dendrograms indicate results from consensus clustering.
Figure 2
Figure 2. Differentially open chromatin loci form distinct clusters and neighbor immunologically relevant genes
(A) Numbers of peaks significantly changing in openness after differentiation. Directions of bars indicate in which subset the peaks were more open. (B) k-means clustering of peaks differentially open across differentiation. Peaks are in rows and colors represent z-scores of openness. The sidebar colors label the three clusters, with the inset numbers giving the numbers of peaks in each. Adjacent are results from GO analysis of each cluster, with peaks separately analyzed based on whether the relative chromatin openness was positively (+) or negatively (-) correlated with the expression of the nearest expressed gene. (C) Enrichment for gene expression changing coordinately with chromatin openness changes. The top panel shows the changes of each peak’s nearest gene, with genes increasing in expression with differentiation indicated in orange and genes decreasing in expression shown in purple. The bottom panel is a smoothed track of the fold-changes, divided by the global fold-change average. (D) Correlation of chromatin openness changes and gene expression changes of selected canonical differentiation-associated genes. One point is plotted for each peak for which the nearest gene was one of the 12 differentiation-associated genes depicted. Black line indicates the least-squares fit.
Fig. 3
Fig. 3. Chromatin accessibility peaks are associated with distinct TF families in differentiated CD8 T cell subsets
(A) Expression of TFs with binding motifs enriched in loci more accessible following differentiation. The x-axis indicates TF expression fold-change following differentiation from naïve (Na) to EM, and the y-axis indicates the –log(p-value) of TF binding motif enrichment in loci that are more open after differentiation. Dot size indicates median TF expression in the EM, and dot color gives the TF family to which the TF belongs, based on the DNA-binding motif. The statistical significance of the enrichment of all TF binding motifs in differentially accessible peaks is given in Table S1. (B) As in (A), for TFs with binding motifs enriched in loci less accessible following differentiation. Here, dot size indicates TF expression level in the Na. (C) Violin plots and boxplots of the log2(fold-changes) of expression levels of gene targets of BATF, Eomes, ETS1, and Sp1 between the T cell subset comparisons. p-values give results of Wilcoxon rank-sum tests assuming a median log2(fold-change) of 0 (red line). Target sets are drawn from published knockdown experiments in human-derived cell lines (25, 32–35).
Fig. 4
Fig. 4. Age-associated chromatin openness changes mirror differentiation-associated changes
(A) Bar graphs of the numbers of differentially open peaks between age cohorts, within each T cell subset. Directions of bars indicate in which age group the peaks were more open. (B) Boxplot of variation in openness across the six age-segregated T cell subsets, from PCA. The y-axis gives the values for the first PC. Groups are ordered by increasing median. (C) Boxplot of results from PCA, as in (D), for gene expression from RNA-seq. Cohorts are in the same order as (D), with the original data set in the left panel and an independent validation set in the right panel. For each data set, 0 on the y-axis is set to the median value in the young naive cohort. (D) k-means clustering of open chromatin peaks that were differentially open between young and old in at least one T cell subset. Peaks are in rows, and subsets are in columns. Colors represent median z-scores of the chromatin openness at a given peak in each subset. Sidebar colors label the three clusters, with the inset numbers giving the numbers of peaks in each cluster. GO analysis of the peaks in the bottom cluster, comprising peaks opening with differentiation, is given on the right. GO enrichment for other clusters is given in Fig. S2B. (E) k-means clustering, as in (B), of genes differentially expressed between young and old in at least one T cell subset, from RNA-seq data. GO analysis on right is for the dark blue cluster, second from the bottom, which comprised peaks opening with aging.
Figure 5
Figure 5. TF enrichment in peaks of differential openness in aging parallels that in differentiation
(A) Expression of TFs with binding motifs enriched in loci more accessible in old naïve (Na) cells. The x-axis indicates TF expression fold-change with age, and the y-axis indicates the –log(p-value) of TF binding motif enrichment in loci that gain accessibility with age. Dot size indicates median TF expression in the old Na, and dot color gives the TF family. A list of the statistical significance of the enrichment of all TF binding motifs in differentially open sites is given in Table S1. (B) As in (A), for TFs with binding motifs enriched in loci less accessible following aging. Here, dot size indicates TF expression level in the young Na. (C) Expression changes of BATF targets, from published knockdown experiments in human-derived cell lines (25), in young versus old within T cell subsets. p-values give results of Wilcoxon rank-sum tests. Negative values indicate the gene is more expressed in cells from older individuals. (D) Expression changes of BATF targets, as in (C), for young Na versus old Na, in the validation cohort.
Fig. 6
Fig. 6. Aging is correlated with an erosion of accessibility at promoters
(A) Violin plots of the distributions of distances of peaks to the nearest transcription start site (from RefSeq annotations). The x-axis scale is in log10 of base pairs. (B) Fractions of promoters versus enhancers, from ChromHMM annotations in naive CD8 T cells, for sets of peaks with significant openness changes in the age or differentiation comparisons. Fractions are with respect to proportions of peaks. Vertical, red line at 0.53 represents the overall promoter fraction across all peaks.
Fig. 7
Fig. 7. Age-driven loss of NRF1 binding associated with decline in expression of mitochondrial respiration complex (MRC) genes
(A) Fractions of promoters versus enhancers, from ChromHMM annotations in naive CD8 T cells, for TF families, using peaks with binding motifs for the constituent TFs. The NRF family is highlighted in yellow. Vertical, red line at 0.53 represents the overall promoter fraction across all peaks. (B) Comparison of aggregate footprints for NRF1 in young versus old naive (Na). Y-axis values are average normalized reads per motif site, per donor, at sites that close with age. (C) Overlap between NRF1 binding sites from ChIP-seq, ATAC-seq peaks more open in young than old Na, and ATAC-seq peaks not more open in young than old Na. Association between accessibility increases and NRF1 binding: p < 2.2 * 10−16, Fisher’s exact test. (D) MRC gene expression changes with age in naïve cells. Positive fold-changes indicate the gene is more expressed in the young than in the old. p-value gives result of Wilcoxon rank-sum tests assuming a median log2(fold-change) of 0 (red line). (E) As in (D), from RNA-seq in an independent validation cohort. (F) Overlap between NRF1 binding sites from NRF1 ChIP-seq, ATAC-seq peaks, and MRC gene promoters. Association between MRC gene promoters and NRF1 binding: p = 1.5 * 10−12, Fisher’s exact test. (G) NRF1 footprint, as in (B), for NRF1 knockdown in young naive cells, versus control-transfected naive cells from the same donors. Binding sites used are those from the ChIP-seq results. (H) Western blot of NRF1 (top row) and β-actin control (bottom row) in NRF1-knockdown naive cells from young donors (right column), versus control-transfected naive cells from the same donors (left column). (I) As in (D) and (E), for MRC gene expression changes in the NRF1 knockdown versus control. Negative fold-changes indicate the gene decreased in expression following the knockdown. (J) Oxygen consumption rates (OCR; y-axis) in young Na (light purple) versus old Na (dark purple). Basal OCR (0–20 minutes) and maximal OCR (60–80 minutes, after FCCP treatment) were significantly higher in young than old (p = 0.022 and 0.021, respectively, F-tests). Results are from six young and five older individuals. Points represent mean OCR, with error ranges reflecting standard errors. (K) Cytometry of MitoTracker Green staining (MFI) in naive cells from four young and four older adults, giving relative mitochondrial mass.
Fig. 8
Fig. 8. Integrated view of changes in chromatin openness and TF binding in aging and differentiation
General model of chromatin structure and TF binding changes in immune aging and differentiation. The positions of the six age-specific T cell subsets correspond to the groups’ respective centroids in the space of the first two PCs from PCA. Aging occurs along two dimensions: a predisposition toward differentiation (x-axis) and an erosion of accessibility at promoters (y-axis). Differentiation is associated with a drop in accessibility at ETS and Zf binding sites and a commensurate gain at bZIP, T-box, and Runt binding sites. While differentiation is preserved during aging, there is also a loss in binding of promoter-binding TFs such as NRF1, a gain of differentiation-associated bZIP TFs, and a loss of differentiation-associated ETS TFs.
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