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Enhanced epigenetic profiling of classical human monocytes reveals a specific signature of healthy aging in the DNA methylome

Nature Aging volume 1, pages 124–141 (2021)Cite this article

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Abstract

The impact of healthy aging on molecular programming of immune cells is poorly understood. Here we report comprehensive characterization of healthy aging in human classical monocytes, with a focus on epigenomic, transcriptomic and proteomic alterations, as well as the corresponding proteomic and metabolomic data for plasma, using healthy cohorts of 20 young and 20 older males (~27 and ~64 years old on average). For each individual, we performed enhanced reduced representation bisulfite sequencing-based DNA methylation profiling, which allowed us to identify a set of age-associated differentially methylated regions (DMRs)—a novel, cell-type-specific signature of aging in the DNA methylome. Hypermethylation events were associated with H3K27me3 in the CpG islands near promoters of lowly expressed genes, while hypomethylated DMRs were enriched in H3K4me1-marked regions and associated with age-related increase of expression of the corresponding genes, providing a link between DNA methylation and age-associated transcriptional changes in primary human cells.

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Fig. 1: Integrated multiomics profiling of healthy aging.
Fig. 2: Low-magnitude transcriptional changes and distinct proteome alterations in monocytes during healthy aging.
Fig. 3: RRBS sequencing reveals robust continuous regions of age-associated DNA hyper- and hypomethylation (up and down DMRs).
Fig. 4: Distinct chromatin signatures of up and down age-associated DMRs.
Fig. 5: Distinct influence of up and down DMRs on gene expression.
Fig. 6: Up and down DMRs behave differently in various pathological contexts.
Fig. 7: Down DMRs are enriched in genetic susceptibilities loci for asthma, MS, glycated Hb and total protein.

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Data availability

Raw human sequencing data (Figs. 2–4) are deposited in the Synapse repository (https://www.synapse.org/#!Synapse:syn22020090/wiki/602603) and available for download upon request to the corresponding author. Please provide information about the principle investigator (name, affiliation, email address and telephone number) and official email from the institutional signing official. The mass spectrometry proteomics data for monocyte lysates have been deposited to the ProteomeXchange Consortium via the PRIDE77 partner repository with the dataset identifier PXD021821. Processed sequencing data, as well as raw and processed metabolic and proteomics data are available at the dedicated online portal at https://artyomovlab.wustl.edu/aging/. The following publicly available datasets have been used in this study: GSE56045 MESA transcriptomic dataset—expression table and annotation are provided; GSE56046 MESA DNA methylation dataset—table of M values and annotation are provided; EGAD00001002523 blueprint datase—bigwig files with methylation signal were downloaded—experiment IDs are EGAX00001097775, EGAX00001097772, EGAX00001086967, EGAX00001086968, EGAX00001086970 and EGAX00001097774; GSE31263 WGBS dataset—BED files with methylation signal were used; ENCODE ChIP–seq samples were used (GSM1102782, GSM1102785, GSM1102788, GSM1102793 and GSM1102797); ENCODE ChromHMM annotation ENCSR907LCD was used (https://www.encodeproject.org/annotations/ENCSR907LCD/); ReMap 2018 database was downloaded for hg19 (http://pedagogix-tagc.univ-mrs.fr/remap/download/remap2018/hg19/MACS/remap2018_TF_archive_nr_macs2_hg19_v1_2.tar.gz); for a list of datasets used in Fig. 6 and Extended Data Fig. 9, see ‘Integration with public data’ in Methods; Neal lab analysis of UK Biobank was downloaded from http://www.nealelab.is/blog/2017/7/19/rapid-gwas-of-thousands-of-phenotypes-for-337000-samples-in-the-uk-biobank.

Code availability

The ULI-ChIP–seq data processing pipeline is available on GitHub: https://github.com/JetBrains-Research/washu. Custom tools (SPAN and JBR genome browser), models and tutorials are available online at https://artyomovlab.wustl.edu/aging/tools.html.

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Acknowledgements

The study was supported by funding from the Aging Biology Foundation to the Artyomov laboratory. The Bagaitkar lab is partially supported by GM125504 and DE28296. The Dixit lab is supported in part by NIH grants P01AG051459, AI105097, AG051459 and AR070811, the Glenn Foundation on Aging Research and Cure Alzheimer’s Fund. This publication is solely the responsibility of the authors and does not necessarily represent the official view of the National Centre for Research Resources (NCRR) or the National Institutes of Health (NIH). We thank the Genome Technology Access Centre in the Department of Genetics at Washington University School of Medicine for help with genomic analysis. The centre is partially supported by NCI Cancer Centre Support grant number P30 CA91842 to the Siteman Cancer Centre and by ICTS/CTSA grant number UL1TR000448 from the NCRR, a component of the NIH, and the NIH Roadmap for Medical Research. We also thank the Epigenomic Core of Weill Cornell Medicine for the initial analysis of the methylation data (eRRBS and raw data pre-processing). We acknowledge the ENCODE consortium and the ENCODE production laboratories that generated the datasets used in the manuscript. We thank I. Miralda for the Fig. 1 schematic.

Author information

Author notes

  1. Konstantin Zaitsev

    Present address: Computer Technologies Department, ITMO University, St. Petersburg, Russia

  2. These authors contributed equally: Irina Shchukina, Juhi Bagaitkar, Oleg Shpynov.

Authors and Affiliations

  1. Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA

    Irina Shchukina, Oleg Shpynov, Ekaterina Loginicheva, Sofia Porter, Denis A. Mogilenko, Patrick Collins, German Demidov, Konstantin Zaitsev, Monika Bambouskova, Laura Arthur, Amanda Swain, Alexandra Panteleeva, Petr Tsurinov, Roman Chernyatchik, Eugene M. Oltz & Maxim N. Artyomov

  2. Department of Oral Immunology and Infectious Diseases, University of Louisville, Louisville, KY, USA

    Juhi Bagaitkar

  3. JetBrains Research, St. Petersburg, Russia

    Oleg Shpynov, Aleksei Dievskii, Evgeny Kurbatsky, Petr Tsurinov, Roman Chernyatchik & Sergey Dmitriev

  4. Department of Biological Sciences, Columbia University, New York, NY, USA

    Erica Wolin & Marko Jovanovic

  5. Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain

    German Demidov

  6. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    German Demidov

  7. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Mykyta Artomov & Mark J. Daly

  8. Broad Institute, Cambridge, MA, USA

    Mykyta Artomov & Mark J. Daly

  9. Yale Center for Research on Aging, Yale School of Medicine, New Haven, CT, USA

    Sviatoslav Sidorov, Christina Camell & Vishwa Deep Dixit

  10. Department of Cell Biology and Physiology, Washington University School of Medicine, Saint Louis, MO, USA

    Sheila A. Stewart

  11. Institute for Molecular Medicine, Helsinki, Finland

    Mark J. Daly

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  1. Irina Shchukina
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Contributions

I.S., J.B. and O.S. contributed equally. I.S., J.B., O.S., S.D. and M.N.A. designed the project. J.B., D.A.M., E.L., S.P., L.A., A.S. and A.P. performed wet laboratory experiments. I.S., J.B, O.S., E.L., S.P., D.A.M., E.W., P.C., G.D., M.A., K.Z., S.S., C.C., M.B., L.A., A.S., A.P., A.D. E.K., P.T., R.C. V.D.D., M.J., S.D., S.A.S., M.J.D., E.M.O. and M.N.A. participated in data collection and data analysis. I.S., J.B., O.S., G.D, M.A., E.K., P.T. and R.C. made the figures and tables. Writing and editing were done by I.S., J.B., O.S. and M.N.A.

Corresponding author

Correspondence to Maxim N. Artyomov.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Cell counts and cytokine profiles.

a, Blood cytokine levels measured by bioplex assay and b, blood differentials obtained using Hemavet in young (n = 20) and old (n = 20) cohorts. Normal ranges for humans are shown below the boxplots. P-values for all comparisons were calculated using two-sided Mann-Whitney U test. In both panels, the lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles.

Extended Data Fig. 2 Metabolomic profile of plasma.

a, PCA of standardized levels of metabolites in plasma. Each dot represents a donor. b, Dendrogram produced by unsupervised hierarchical clustering of metabolic data. Clustering using average algorithm and Euclidian distance as the distance metric. c, List of significantly different metabolites. d, Selected differentially regulated metabolites from sex steroids synthesis pathway in young (n = 20) and old (n = 20) cohorts. Statistical analyses by two-sided Mann-Whitney U test with Benjamini–Hochberg correction for multiple testing. e, Schema of sex steroids synthesis. f, Pathway analysis of metabolic data. Each boxplot summarizes log2FC of all members of the corresponding pathway. Pathways with mean log2FC significantly different from zero are highlighted (two-sided Mann-Whitney U test and Benjamini-Hochberg correction for multiple testing). Dots represent outliers. N for each pathway is shown in brackets. g, PCA as in Extended Data 2a. Z-scores were calculated for all metabolites. For each sample, color of the dot represents averaged z-scores of all metabolites belonging to the pathway. In panels (D) and (F), the lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles.

Extended Data Fig. 3 Proteomic profile of plasma (Somascan).

a, PCA of standardized proteins levels in plasma (Somascan). Each dot represents a donor. b, Dendrogram produced by unsupervised hierarchical clustering of Somascan data. Clustering using average algorithm and Euclidian distance as the distance metric. c, Differential analysis results for plasma proteomic profile: volcano plot. Each dot represents a protein. P-values and logFC were calculated by two-sided test from Limma package and adjusted by Benjamini–Hochberg method. d, ELISA validation of selected proteins in young (n = 20) and old (n = 20) cohorts. Adjusted p-values for Somascan results (as in (C)) are shown in the top panel and ELISA validation of the same targets are shown below. ELISA p-values were calculated using two-sided Mann-Whitney U test. The lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles. e, Heatmap representation of absolute values of Spearman’s correlation coefficients (rho) between plasma proteins (rows) and plasma metabolites (columns) calculated within old (left panel) and young (right panel) cohorts. Clustering of old cohort rows and columns was done using complete algorithm and Euclidian distance as a metric. Order of rows and columns in young cohort heatmap matches order established for the old cohort. f, Each point represents a donor. Smoothing was done by lm function separately in young and old groups, shaded error bands represent SE.

Extended Data Fig. 4 Monocyte transcriptome and proteome.

(a) Left panel: schematic representation of CD14+CD16− monocytes isolation using magnetic beads. Right panel: flow cytometry validation and estimation of purity. (b) Number of significant genes detected after downsampling MESA dataset (youngest and oldest 25%). Downsampling was repeated n = 50 times for each group size. The lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles. (c) GSEA enrichment curves illustrate pathways that significantly change with age in MESA dataset. P-values are one-sided and corrected by Benjamini-Hochberg method. (d) Monocytes were differentiated into macrophages by one-week incubation with M-CSF. Both cell types were stimulated by LPS for 24 hours. (e) PCA of normalized expression levels estimated by RNA-seq for monocyte differentiation and activation experiment. (f) Each dot represents a monocyte protein significantly different between age groups. LogFC proteomics (x axis) as in Fig. 2f, LogFC transcriptomics as in Fig. 2c.

Extended Data Fig. 5 RRBS quality control and monocyte methylome.

a, Library depth for each sample. b, Hannum and Horvath methylation clocks for old (n = 20) and young (n = 20) groups. Methylation levels of CpGs that were used in the model but were not covered in our eRRBS data were imputed using mean methylation of [−100kb; +100 kb] region around the CpG. CpG methylation was set to zero if imputation was not possible. P-values were calculated using two-sided Mann-Whitney U test. The lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles. c, Enrichment of DMRs in CpG islands. Histogram shows distribution of simulated intersection sizes (n = 100,000 random simulations). d, Comparison to Blueprint dataset. Each dot represents one DMR detected in our dataset. X axis – difference between old and young cohorts in our dataset, Y axis – difference between old donors and cord blood from Blueprint. e, Plot as in right panel of Fig. 3g for a newborn vs centenarian WGBS dataset (GSE31263). f, PCA on MESA data as in Fig. 3i using all cytosines profiled by DNA methylation array. g, Number of methylated (methylation level > 0) cytosines in CpG, CHG and CHH context shared by one to 40 samples. h, PCA of CpGs methylation levels from old and young groups. Each dot represents a sample. i, Dendrogram produced by unsupervised hierarchical clustering of the samples. Each sample described as a vector of CpG methylation levels. Clustering using Ward algorithm and Manhattan distance as the distance metric. Outliers are labelled. j, Distribution of CpG methylation levels. For each segment fractions of CpGs with corresponding methylation level in each sample are shown by dots. Bar shows average fraction of CpG across all samples. Outlying samples are labelled. k, PCA of CpGs methylation levels. Outliers (OD11, OD17, YD9) were excluded.

Extended Data Fig. 6 ULI-ChIP-seq processing and quality control.

a, Snapshot of the H3K4me1 tracks across all donors shows distinct signal for all samples with high variability in the signal-to-noise ratio. b, Number of peaks for each mark in each donor yielded by classical peak calling tools. c, Schematic representation of overlap metric used in panels (E) and (H). d, Number of peaks yielded by SPAN, MACS2 and SICER. e, Overlap between all pairs of samples for peaks generated by SPAN, MACS2 and SICER. SICER was used for wide modifications only. N for each bar is equal to a number of possible pairs between all samples that passed QC. f, Summary for panel (E). Mean and standard deviation (SD) of overlaps between samples are shown. g, Overlap characteristics as in (F) for SPAN runs with various annotation sizes. h, Directional overlap of SPAN peaks between all samples and all histone modifications. i, Two-way overlap with ENCODE CD14+ monocytes data for different peak calling approaches. In panels (B), (D), and (I) N for each bar is equal to a number of ChIP-seq samples that passed QC for each modification. See Fig. 4b for exact numbers. Error bars in all panels represent SD.

Extended Data Fig. 7 SPAN test set errors.

Test set errors for golden standard tools (MACS2, SICER) as well as SPAN trained with various numbers of labels. Each dot represents a sample, n = 40 for H3K4me3, H3K27me3, H3K27ac, n = 32 for H3K4me1, n = 39 for H3K36me3. In all panels, the lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles.

Extended Data Fig. 8 Chromatin context of DMRs.

a, Enrichment of DMRs in chromatin state segments from ENCODE ChromHMM partition. Upper: for each chromatin state dark grey bar represents number of DMRs intersecting at least one segment of the state. Light grey bar shows expected number of intersections estimated by n = 100,000 random simulations. Error bars show SD, their centers represent expected intersection. Results shown for all DMRs, hypermethylated DMRs only and hypomethylated DMRs only. Chromatin states that are significantly over- or under-represented among DMRs are marked by asterix (*). Bottom: heatmap shows general statistics for each chromatin state. Values are normalized within each row. b, PCA of standardized methylation levels in old and young groups for three chromatin states: bivalent Tss (14 TssBiv), bivalent enhancers (15 EnhBiv) and Polycomb-repressed regions (16 ReprPC). Each dot represents a sample. c, Intensity of H3K4me1, H3K27me3, H3K4me3, H3K27ac, and H3K36me3 signals was calculated for each DMR via Diffbind package, normalized with respect to the DMR length and averaged across the cohorts. Normalized signals were compared between hypo- (n = 423) and hypermethylated (n = 737) DMRs using two-sided Mann-Whitney U test. Dots represent outliers. The lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles. d, Volcano plot as in Figs. 4i and 4k, colored in accordance with intersection with H3K4me3, H3K27ac and H3K36me3. e, Density plot protein coding gene expression with transcription factors of interest highlighted.

Extended Data Fig. 9 Up and down DMRs in different physiological conditions.

a, Comparison of age- and sex-adjusted DMR mean methylation between Alzheimer’s patients (n = 15 for glia and neurons, n = 48 for whole blood) and healthy controls (n = 14 for glia and neurons, n = 9 for whole blood). Each dot represents one donor. P-values calculated using two-sided Mann-Whitney U test. b, Plot as in (A) comparing data from lean (n = 9) and obese (n = 8) donors. c, Plot as in Fig. 6g. Mean methylation of smoking DMRs in our cohort: young lean (n = 11), young overweight (n = 8), old lean (n = 8), and old overweight (n = 10). P-values were calculated using two-sided Mann-Whitney U test. In all panels, the lower and upper hinges of all boxplots represent the 25th and 75th percentiles. Horizontal bars show median value. Whiskers extend to the values that are no further than 1.5*IQR from either upper or lower hinge. IQR stands for inter-quartile range, which is the difference between the 75th and 25th percentiles.

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Table 1

Basic donor information and blood differential (cell counts, Hb and HCT levels).

Supplementary Table 2

Cytokine bioplex assay data for all donors (IFN-γ, IL-10, IL-12p70, IL-13, IL-1β, IL-2, IL-4, IL-6, IL-8, TNF-α).

Supplementary Table 3

(A) Scaled intensities of the metabolites in the plasma for all donors (734 metabolites). (B) Differential analysis results. Significant differences in metabolites between cohorts were determined using two-sided Mann–Whitney U-test. P values were adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 4

SomaScan proteomics array data for all donors: (A) scaled data, (B) differential comparison statistics. For differential analysis, functions lmFit and eBayes from the Limma package were used (two-sided). P values were adjusted for multiple testing using the Benjamini–Hochberg method.

Supplementary Table 5

ELISA validation of the most differentially present plasma proteins (sCD86, GDF-15, sclerostin, OMD, Notch1).

Supplementary Table 6

Counts for monocyte RNA-seq data. (A) Raw counts, (B) DESeq2 normalized counts and (C) log2-quantile-normalized counts.

Supplementary Table 7

Monocyte RNA-seq data. DESeq2 differential analysis results between old and young cohorts (two-sided Wald test, correction for multiple testing using the Benjamini–Hochberg method).

Supplementary Table 8

Transcriptomic data for MESA cohort. Limma differential analysis results (gene ~ age + chip + race–gender–site design, two-sided test, correction for multiple testing using the Benjamini–Hochberg method).

Supplementary Table 9

Monocyte proteomics data generated and analysed by Biognosys. (A) Sample table, (B) spectral library protein inventory, (C) spectral library peptide inventory, (D) differential analysis results, (E) protein intensities and (F) peptide intensities. In brief, for each protein, the fold change of each peptide ion variant was estimated as average abundance of peptide ion variant across biological replicates in the older group/average abundance of peptide ion variant across biological replicates in the young group. The values then were log-transformed and fold changes of all peptides belonging to the same proteins were compared to zero using two-sided paired t-test. Multiple testing correction was performed as described in Storey et al.63.

Supplementary Table 10

RNA-seq data for monocyte differentiation and stimulation experiment (Extended Data 4D). (A) Raw counts, (B) DESeq2-normalized counts and (C) log2-quantile-normalized counts.

Supplementary Table 11

Basic QC metrics of RRBS libraries (n covered (≥ 10 reads) CpG, mean CpG coverage, percent mapped reads, conversion rate and library depth).

Supplementary Table 12

Differentially methylated regions as obtained from RRBS data: (A) unfiltered DMRs and (B) Confident DMRs filtered based on ncyto ≥ 3 and abs(avdiff) ≥ 0.025.

Supplementary Table 13

Basic characteristics and QC of the ULI-ChIP–seq libraries: (A) FastqC output, (B) QC of bam files based on ENCODE standards, (C) alignment statistics and (D) QC results reported by phantompeaks.

Supplementary Table 14

Transcription factors binding sites overrepresentation analysis: (A) results for up DMRs corrected for enrichment in CpG islands and (B) results for down DMRs corrected for enrichment in H3K4me1-marked regions. See Methods for the details of the one-sided enrichment procedure. Correction for multiple testing was done using the Benjamini–Hochberg method.

Supplementary Table 15

Positive and negative primers used for the ChIP qPCR quality control.

Supplementary Table 16

Peak calling summary for all samples (MACS2, SICER and SPAN).

Supplementary Table 17

Labels used for the semi-supervised peak calling: (A) overall label sets statistics. Specific labels for (B) H3K27ac, (C) H3K27me3, (D) H3K36me3, (E) H3K4me1 and (F) H3K4me3.

Supplementary Table 18

Comparisons of errors produced by SICER, MACS2 and SPAN on the datasets annotated by McGill team from Hocking et al.44.

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Shchukina, I., Bagaitkar, J., Shpynov, O. et al. Enhanced epigenetic profiling of classical human monocytes reveals a specific signature of healthy aging in the DNA methylome. Nat Aging 1, 124–141 (2021). https://doi.org/10.1038/s43587-020-00002-6

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