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Aging-associated HELIOS deficiency in naive CD4+ T cells alters chromatin remodeling and promotes effector cell responses

Nature Immunology volume 24, pages 96–109 (2023)Cite this article

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Abstract

Immune aging combines cellular defects in adaptive immunity with the activation of pathways causing a low-inflammatory state. Here we examined the influence of age on the kinetic changes in the epigenomic and transcriptional landscape induced by T cell receptor (TCR) stimulation in naive CD4+ T cells. Despite attenuated TCR signaling in older adults, TCR activation accelerated remodeling of the epigenome and induced transcription factor networks favoring effector cell differentiation. We identified increased phosphorylation of STAT5, at least in part due to aberrant IL-2 receptor and lower HELIOS expression, as upstream regulators. Human HELIOS-deficient, naive CD4+ T cells, when transferred into human-synovium-mouse chimeras, infiltrated tissues more efficiently. Inhibition of IL-2 or STAT5 activity in T cell responses of older adults restored the epigenetic response pattern to the one seen in young adults. In summary, reduced HELIOS expression in non-regulatory naive CD4+ T cells in older adults directs T cell fate decisions toward inflammatory effector cells that infiltrate tissue.

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Fig. 1: Chromatin accessibility changes induced by TCR stimulation of different signaling strengths.
Fig. 2: Dissociation of TCR-induced signaling and chromatin accessibility changes in naive CD4+ T cells from older adults.
Fig. 3: Accelerated chromatin changes in T cells from older adults are driven by the STAT5 pathway.
Fig. 4: Age-associated expansion of naive CD4+ T cell subsets with increased activation of the STAT5 pathway.
Fig. 5: CD25 upregulation is induced by a loss of HELIOS with age.
Fig. 6: Forced HELIOS degradation in human naive CD4+ T cells induces tissue inflammation in vivo.
Fig. 7: Inhibition of IL-2-STAT5 signaling rewires activated naive CD4+ T cells from older individuals to a younger epigenetic response pattern.

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Data availability

Raw sequencing data have been deposited in SRA with the BioProject accession no. PRJNA757466. Raw experimental data will be provided by the corresponding author upon request. Source data are provided with this paper.

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Acknowledgements

This work was supported by the National Institutes of Health (R01 AR042527, R01 HL117913, R01 AI108906, R01 HL142068 and P01 HL129941 to C.M.W. and R01 AI108891, R01 AG045779, U19 AI057266 and R01 AI129191 to J.J.G.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank E. Fischer from Harvard Medical School for providing the ALV2 compound, P. Li from the National Institutes of Health for providing processed ChIP-seq data files of human CD4+ T cells, C. Gustafson and F. Müller for suggestions on single-cell data analysis, X. Wang and F. Cao for suggestions on ATAC-seq time course analysis and the Stanford Genome Sequencing Service Center and Novogen for providing sequencing services.

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Author notes

  1. These authors contributed equally: Huimin Zhang, Rohit R. Jadhav.

Authors and Affiliations

  1. Department of Immunology, Mayo Clinic, Rochester, MN, USA

    Huimin Zhang, Rohit R. Jadhav, Wenqiang Cao, Jun Jin, Cornelia M. Weyand & Jörg J. Goronzy

  2. Department of Medicine, Stanford University, Stanford, CA, USA

    Huimin Zhang, Rohit R. Jadhav, Wenqiang Cao, Tuantuan V. Zhao, Jun Jin, Shozo Ohtsuki, Zhaolan Hu, Cornelia M. Weyand & Jörg J. Goronzy

  3. Health Sciences Institute, China Medical University, Shenyang, China

    Wenqiang Cao

  4. Biochemistry and Molecular Biophysics, California Institute of Technology, Pasadena, CA, USA

    Isabel N. Goronzy

  5. Department of Medicine, Division of Rheumatology, Mayo Clinic, Rochester, MN, USA

    Tuantuan V. Zhao, Shozo Ohtsuki, Zhaolan Hu, Jose Morales, Cornelia M. Weyand & Jörg J. Goronzy

  6. Department of Genetics, Stanford University, Palo Alto, Stanford, CA, USA

    William J. Greenleaf

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Contributions

H.Z., R.R.J., W.J.G., C.M.W. and J.J.G. designed the research and interpreted data. H.Z., W.C., T.Z., J.J., S.O., J.M. and Z.H. performed experimental work. R.R.J. and I.N.G. analyzed high-throughput data. H.Z., R.R.J. and J.J.G. wrote the manuscript.

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Correspondence to Jörg J. Goronzy.

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W.J.G. has affiliations with 10x Genomics (consultant). The other authors declare no competing interests.

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Nature Immunology thanks Graham Pawelec, Benedict Seddon and Harinder Singh for their contribution to the peer review of this work. Primary Handling Editor: Laurie A. Dempsey, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Related to Fig. 1: Titration of TCR stimulation.

a, Naive CD4+ T cells were stimulated with polystyrene beads coated with indicated amounts of anti-CD3 Ab together with a constant amount of 1 µg mL-1 antiCD28. Bead-cell conjugates were gated and the percent of bound to total cells were determined. b, Histograms of phosphorylated ERK gated on bead-bound cells at 0 and 5 min stimulated with indicated amounts of anti-CD3. c, Mean fluorescence intensity (MFI) of phosphorylated ZAP70, SLP76 and ERK was measured gated on bead-bound cells. d, Histogram of CD69 gated on bead-bound cells at 24 h of stimulation (left panel). Frequency of CD69+ cells as percentage of total bead-bound cells from 0–48 h with indicated anti-CD3 concentrations (right panel). e, Cell Trace Violet dye dilution of naive CD4+ T cells stimulated with 1 µg mL-1 anti-CD3. Experiments shown are representative of three experiments.

Source data

Extended Data Fig. 2 Related to Fig. 1a, b: Longitudinal epigenomic and transcriptomic changes induced by TCR stimulation.

Naive CD4+ T cells from eight individuals were stimulated with 0.01 (low stimulation strength, square) or 1 µg mL-1 (medium strength, triangle) anti-CD3-coated beads and subjected to ATAC-seq and RNA-seq at indicated time points. a, Bar graphs show the number of peaks more or less accessible in activated cells compared to unstimulated cells at each time point. b, Differential peaks comparing unstimulated cells to either low (left) or high (right) TCR stimulation are plotted as average log2 fold-change (logFC) versus log2 mean reads per peak. c, Bar graphs show the number of significantly up- or downregulated gene transcripts for each time point. d, Up- or downregulated gene transcripts in cells activated as indicated compared to unstimulated cells are shown as volcano plots of logFC versus significance levels. To illustrate similarities between stimulation conditions, few transcripts are labeled. Comparisons in b, d were done by two-sided robust empirical Bayes moderated statistical test; a Benjamini–Hochberg adjusted P value of 0.001 was used as cutoff.

Extended Data Fig. 3 Related to Fig. 2: Higher intensity TCR stimulation amplifies age-associated chromatin accessibility differences seen at baseline or with low intensity stimulation.

Peak sets from ATAC-seq analysis of naive CD4+ T cells from young (left) or old (right) adults were compared for each of the three conditions, unstimulated and after TCR stimulation with beads coated with low and medium concentrations of anti-CD3. Comparisons were done by robust empirical Bayes moderated statistical test; a Benjamini–Hochberg adjusted P value of 0.05 was used as cutoff. Relationships between the three sets of differential peaks are shown as Venn diagrams.

Extended Data Fig. 4 Related to Fig. 2c, d: Activation-induced temporal patterns of epigenetic changes in naive CD4+ T cells from young and older adults.

a, Genes assigned to differentially accessible regulatory sites were examined for significant enrichment in biological pathways. Selected GO terms associated with the different temporal patterns in young (upper panel) and older adults (lower panel) are shown. Comparisons were done by two-sided robust empirical Bayes moderated statistical test; a Benjamini–Hochberg adjusted P value of 0.05 was used as cutoff. b, Gene expression of indicated TFs shown as mean + s.e.m. n = 8 biologically independent samples.

Extended Data Fig. 5 Related to Fig. 3c: Relationship between differential accessibility of regulatory regions and transcriptome.

a, Differential peaks between young and older individuals at indicated TCR stimulation conditions and time points are plotted as logFC versus log mean reads per peak. Comparisons were done by robust empirical Bayes moderated statistical test; a Benjamini–Hochberg adjusted P value of 0.05 was used as cutoff. b, Aggregate genome accessibility tracks (top panel) of representative sites of cluster 6–10 shown in the heat plot in Fig. 3c. Cyan-shaded areas indicate peaks that are more open in young compared to older adults, magenta-shaded areas are more open in old. Transcript data from corresponding genes as determined by RNA-seq (lower panel) and shown as mean + s.e.m. n = 8 biologically independent samples. c, LogFC difference of transcripts differentially expressed between young and older adults from 48-h RNA-seq data is plotted against logFC difference of differential peaks from ATAC-seq data annotated to the same gene at the indicated time point. Data are fitted with two-sided simple linear regression. P < 0.0001.

Extended Data Fig. 6 Related to Fig. 3h: Time course of TF expression after STAT5 inhibition.

Naive CD4+ T cells from older adults were activated for 0–24 h with polystyrene beads coated with 1 µg mL-1 anti-CD3 and anti-CD28 in the presence of DMSO solvent control or a STAT5 inhibitor. Representative western blots (top panel). Summary data from 3 experiments showing band intensities of IRF4 and BATF relative to β-actin (bottom panel) and expressed as mean + s.e.m. n = 3 biologically independent samples. Data were analyzed with two-sided paired t-test. *P < 0.05 (p = 0.02), **P < 0.01 (p = 0.0094).

Source data

Extended Data Fig. 7 Related to Fig. 4: Single cell epigenetic and transcriptional analysis of naive CD4+ T cell responses.

a, CD31 and PTK7 surface expression on naive CD4+ T cells. b, Nuclei pooled from unstimulated and activated naive CD4+ T cells were subjected to scATAC- and scRNA-seq. Imputed CD69 gene expression is projected on the UMAP of scATAC-seq. Clusters of resting and activated cells were distinguished based on containing CD69-expressing cells (left panel). UMAP maps of scATAC-seq and scRNA-seq data are shown for activated cells (right panels). c, B3GAT1 and KLRG1 gene expression were projected onto scMultiome UMAPs (top panel). Genome tracks of bulk ATAC-seq and gene expression in bulk RNA-seq expressed as mean + s.e.m. (bottom). n = 8 biologically independent samples. d, IL4 and IL17A gene expression were projected onto scMultiome UMAPs (top panel). Genome tracks of bulk ATAC-seq at 24 h of TCR stimulation (bottom).

Extended Data Fig. 8 Related to Fig. 5b: TFs binding to IL2RA enhancers.

a, Gating strategy for naive CD4+ T cells from PBMC of young and older individuals. b, ATAC-seq genome tracks of unstimulated naive CD4+ T cells from young and older adults were aligned with ENCODE ChIP-seq genome tracks of indicated TFs at IL2RA enhancer locus (see Fig. 5c). The location of enhancer A and enhancer B is indicated by red lines. c, IKZF2 and IL2RA transcripts from sorted CD25lo and CD25- naive CD4+ T cells from 7 donors. Data were fitted with two-sided simple linear regression. P = 0.0076.

Source data

Extended Data Fig. 9 Related to Fig. 6: Naive CD4+ T cell tissue infiltration after HELIOS degradation.

Naive CD4+ T cells were treated with DMSO or 1 µM ALV2 for 5 days. a, Histograms of CD25 gated on cells conjugated to beads after 4 h stimulation with 1 µg mL-1 anti-CD3 (left). Summary of CD25 MFI from four experiments (right). n = 4 biologically independent samples. Data were analyzed with two-sided paired t-test. *P < 0.05 (P = 0.01). b, IL-21 and IFNγ were stained after five days of activation with 5 µg mL-1 plate-coated anti-CD3 and 1 µg mL-1 anti-CD28. Data shown are representative of 4 (IFNγ) and 5 (IL21) experiments. c, Human CD4+ T cells recovered from mouse spleen after 7-day engraftment. n = 7 biologically independent samples. Data were analyzed with two-sided paired t-test.

Source data

Extended Data Fig. 10 Related to Fig. 7: Genome accessibility changes induced by IL-2 receptor blocking and effector TF expression induced by STAT5 inhibition.

a, LogFC of differential peaks between IL-2 receptor (IL-2R) blocking and PBS treatment are plotted against average peak size. Peaks that are also differentially accessible between young and older individuals are indicated in dark red. Comparisons were done by two-sided robust empirical Bayes moderated statistical test; a Benjamini–Hochberg adjusted P value of 0.05 was used as cutoff. b, Genome tracks of representative genes. Magenta-shaded areas indicate peaks that are more open in older adults and with PBS treatment; cyan indicates peaks that are more open in young adults or with IL-2R blocking treatment of older adults. c, IRF4 protein expression were measured by Immuno blot in 48-h activated naive CD4+ T cells from four young adults with and without STAT5 inhibition. n = 4 biologically independent samples. d, BLIMP1 and TCF1 expression was measured by flow cytometry in 5-day activated naive CD4+ T cells from four young adults. The MFI ratio of BLIMP1 to TCF1 was plotted. n = 4 biologically independent samples. Data in c,d were analyzed with two-sided paired t-test.

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Zhang, H., Jadhav, R.R., Cao, W. et al. Aging-associated HELIOS deficiency in naive CD4+ T cells alters chromatin remodeling and promotes effector cell responses. Nat Immunol 24, 96–109 (2023). https://doi.org/10.1038/s41590-022-01369-x

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