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Multi-omic rejuvenation and lifespan extension on exposure to youthful circulation

Nature Aging volume 3, pages 948–964 (2023)Cite this article

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Abstract

Heterochronic parabiosis (HPB) is known for its functional rejuvenation effects across several mouse tissues. However, its impact on biological age and long-term health is unknown. Here we performed extended (3-month) HPB, followed by a 2-month detachment period of anastomosed pairs. Old detached mice exhibited improved physiological parameters and lived longer than control isochronic mice. HPB drastically reduced the epigenetic age of blood and liver based on several clock models using two independent platforms. Remarkably, this rejuvenation effect persisted even after 2 months of detachment. Transcriptomic and epigenomic profiles of anastomosed mice showed an intermediate phenotype between old and young, suggesting a global multi-omic rejuvenation effect. In addition, old HPB mice showed gene expression changes opposite to aging but akin to several lifespan-extending interventions. Altogether, we reveal that long-term HPB results in lasting epigenetic and transcriptome remodeling, culminating in the extension of lifespan and healthspan.

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Fig. 1: Prolonged parabiosis followed by detachment leads to extended lifespan and healthspan.
Fig. 2: Persistent epigenetic age reversal in blood and liver on HPB assessed by RRBS-based aging clocks.
Fig. 3: Sustained epigenetic age reversal in liver on HPB assessed by microarray-based aging clocks.
Fig. 4: Transcriptomic analyses of HPB reveal unique pathway enrichment and differential expression patterns.
Fig. 5: Dimensionality reduction highlights rejuvenated molecular profiles after HPB.
Fig. 6: The transcriptomic signatures of HPB align with longevity interventions and oppose aging.
Fig. 7: Gene expression analyses hint at putative rejuvenation mechanisms of long-term HPB.

Data availability

The sequencing data obtained in this study are deposited with the Gene Expression Omnibus under accession no. GSE224447. The data used to generate the graphs can be found in the accompanying source data files. Source data for Figs. 1–7 and Extended Data Figs. 1–10 are provided with the paper.

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Acknowledgements

J.P.W. was supported by National Institutes of Health (NIH) grant nos. K01AG056664 and R21AG065943. V.N.G. was supported by NIH grant nos. R01AG067782, P01AG047200 and R01AG065403. D.E.L. was supported by NIH training grant no. T32HL007057. S.H. acknowledges support from the Milky Way Research Foundation and the Epigenetic Clock Development Foundation, and A. Tyshkovskiy and S.E.D. acknowledge support from the Interdisciplinary Scientific and Educational School of Moscow University ‘Molecular Technologies of the Living Systems and Synthetic Biology’. We thank the Duke Behavioral Core for support on this project. We also thank T. Fox for help with schematic figures. Figure icons were partially created with BioRender.com. The funders had no role in study design, data collection, data analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Alexandre Trapp & Anastasia V. Shindyapina

    Present address: Retro Biosciences, Redwood City, CA, USA

  2. Ake T. Lu & Steve Horvath

    Present address: Altos Labs, San Diego, CA, USA

  3. Csaba Kerepesi

    Present address: Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network, Budapest, Hungary

  4. Lauren K. McKay

    Present address: Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

  5. These authors contributed equally: Bohan Zhang, David E. Lee, Alexandre Trapp.

Authors and Affiliations

  1. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Bohan Zhang, Alexandre Trapp, Alexander Tyshkovskiy, Csaba Kerepesi, Anastasia V. Shindyapina & Vadim N. Gladyshev

  2. Department of Medicine, Duke University School of Medicine, Durham, NC, USA

    David E. Lee, Akshay Bareja & James P. White

  3. Duke Molecular Physiology Institute, Duke University, Durham, NC, USA

    David E. Lee, Akshay Bareja, Lauren K. McKay, Gurpreet S. Baht & James P. White

  4. Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia

    Alexander Tyshkovskiy & Sergey E. Dmitriev

  5. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

    Ake T. Lu & Steve Horvath

  6. Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA

    Gurpreet S. Baht

  7. Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, USA

    Gurpreet S. Baht & James P. White

  8. Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA, USA

    Steve Horvath

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Contributions

B.Z., D.E.L., V.N.G. and J.P.W. conceived the project. D.E.L., G.S.B., A.B., L.K.M. and J.P.W. carried out the animal experiments. A. Trapp, D.E.L. and B.Z. designed the figures. A. Trapp, A. Tyshkovskiy and B.Z. performed the gene expression analyses. B.Z., A. Trapp and C.K. conducted the DNA methylation clock analyses based on RRBS. B.Z., A. Trapp, A.T.L. and S.H. conducted the analyses based on the microarrays. A.V.S. contributed to the enrichment analyses. S.E.D. contributed to data interpretation. V.N.G. and J.P.W. supervised the project. B.Z., D.E.L., A. Trapp, V.N.G. and J.P.W. wrote the paper with final approval from all coauthors.

Corresponding authors

Correspondence to Vadim N. Gladyshev or James P. White.

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Extended data

Extended Data Fig. 1 Timeline of molecular and physiological profiling.

a, Schematic timeline of the molecular (transcriptomic & epigenetic) and physiological profiling presented in this study (top) as well as a table (bottom) highlighting mortality rates at various parts in the parabiosis protocol (numbers represent individual mice, not pairs). Samples were taken immediately after the parabiosis period (month 3), or after a 2-month detachment period (month 5). Physiological profiling was performed before the parabiosis experiment (month 0), and then every month from month 4 to month 10. Data in the table (bottom) is separated by experimental purposes and ages of the pairs. Mortality rate across experiments for each group: Young ISO: 11% mortality during PB, 0% during detach; Old ISO: 20% mortality during PB, 13% during detach. Old HET: 19% mortality during PB, 7% during detach. b, Changes in food consumption resulting from parabiosis. Lines depict mean changes with 95% confidence intervals. Individual observations are shown as points. Young isochronic (Young ISO) mice are shown in blue, old heterochronic (Old HET) mice are shown in purple, and old isochronic (Old ISO) mice are shown in red. Dashed line depicts time of detachment. c, Changes in vertical activity resulting from parabiosis. Lines depict mean changes with 95% confidence intervals. Individual observations are shown as points. Legend is the same as (b). d, Schematic of the experimental workflow and associated findings.

Source data

Extended Data Fig. 2 Blood mixture analyses after 3 months of parabiosis.

a, Schematic of experiments to analyze the blood chimerism after attachment and detachment. Green represents GFP (+) mice, white represents wild-type mice. b, Gating scheme applied to all downstream blood analysis between GFP and wild-type mice. Cells were ultimately gated on GFP (+) (x-axis) and CD45-APC (+) (y-axis). c, Percentage of blood crossover during attachment. d, Percentage of blood (left) and bone marrow (BM, right) crossover after 2-month detachment, calculated as the % GFP (+) and (-) cells, n = 3 biological replicates group. Bars represent mean +/− SEM of cell proportions labelled GFP+ and GFP-(WT).

Source data

Extended Data Fig. 3 Relationship of DNAm age across tissues and platforms, delta age analysis, and effect of surgery.

a, Scatterplots highlighting the association of liver and blood DNAm predictions based on RRBS sequencing across 6 different group: young isochronic (Young ISO), young isochronic detached (Young ISO Det.), old heterochronic (Old HET), old isochronic (Old ISO), old heterochronic detached (Old HET Det.), and old isochronic detached (Old ISO Det.). Each point depicts the mean DNAm prediction for a particular tissue for that group. The clock used for predictions is shown in the top part of each panel. The Pearson correlation (r) is shown in each panel, along with its associated two-tailed P value. Linear regression lines (dark grey) with 95% confidence intervals (light grey) are shown. b, Scatterplots highlighting the association of liver DNAm predictions based on RRBS sequencing and methylation array profiling across 6 different groups (same legend as a). The clock used for RRBS predictions is shown in the top part of each panel. The mouse liver development clock was used for array predictions given that it showed the greatest changes in epigenetic age comparing long-term old heterochronic and isochronic mice. c, Delta age (epigenetic age minus chronological age) of liver samples from old heterochronic mice, old isochronic controls and untreated controls based on universal relative age mammalian, universal log-linear transformed age mammalian, liver, and development liver clocks. Dashed line denotes a delta age of 0 (epigenetic age = chronological age). Points above this line depict age acceleration (epigenetic age > chronological age), while points below this line depict age deceleration (epigenetic age < chronological age). n = 5 per group. d, Epigenetic age of liver samples from 8-month old non-surgical control mice (Young NSC), isochronic detached (Young ISO) and heterochronic detached mice (Young HET), based on the universal relative age mammalian, universal log-linear transformed age mammalian, liver, and liver development clocks. n = 5 per group for parabiosed mice and 6 per group for controls. Box plots represent 25–75 percentile and 1.5x IQR. Two-tailed Welch’s t-tests were used for statistical analyses.

Source data

Extended Data Fig. 4 Mean methylation in array and RRBS methylation profiles.

a, Mean liver methylation assayed by the Mammalian Methylation Array (HorvathMammalMethylChip40), both based on all sites in the array (left) and only sites in CpG islands (right). n = 5 samples per group. b, Mean global methylation (top), promoter methylation (middle), and gene body methylation (bottom) of liver (left) and blood (right) RRBS samples. Mean methylation was assayed across 1,014,243 CpGs (top), 11,842 promoters (middle), and 13,811 gene bodies (bottom). n = 4 samples per group. Box plots represent 25–75 percentile and 1.5x IQR. Two-tailed Welch’s t-test were used for statistical analysis. Schematic tissues in each panel indicate the source of the sample.

Source data

Extended Data Fig. 5 Physiological tests of a mock parabiosis procedure to assess the interaction of parabiosis and exercise.

a, Schematic of the experiment to test blood sharing using glucose levels. Glucose is injected into one mouse and subsequently assessed in both mice in mock parabiosis pairs. b, Blood glucose level changes in pairs starting with a young (left) and old (right) glucose bolus to determine blood sharing; n = 5 biological replicates/group. No statistical test was performed. c, Moving distance in cm (left), and velocity (right) in the old isochronic, young isochronic and heterochronic mock parabiosis pairs. n = 4 biological replicates/ group. Bars represents mean +/− SEM. Pdistance(Young ISO—Old ISO) = 0.034 calculated by two-tailed, Student’s t-test. d, Movement tracking in the old isochronic, young isochronic and heterochronic mock parabiosis pairs.

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Extended Data Fig. 6 Tissue specificity of RRBS epigenomic profiles, promoter methylation changes, and proteomic dynamics in HPB.

a-c, Principal component analysis (PCA) of CpG methylation across 1,014,243 CpG sites (a), 11,842 promoters (b), and 13,811 gene bodies (c) in n = 36 liver samples and n = 32 blood samples, with 6 different groups in both tissues: young isochronic (Young ISO), young isochronic detached (Young ISO Det.), old heterochronic (Old HET), old isochronic (Old ISO), old heterochronic detached (Old HET Det.), and old isochronic detached (Old ISO Det.). In all the PCA plots, tissue of origin is the largest source of variation (44–91%). d, Promoter methylation of Cdc20, Sox30, Mpped1, and Ubl5 across liver RRBS samples. Genes were identified after passing a significance threshold (p < 0.05) when comparing young and old mice, as well as old heterochronic and isochronic mice in both attached and detached groups. n = 6 biological replicates/group. P value’s calculated by two-tailed Welch’s t-tests. Box plots represent Mean, 25–75 quartiles and 1.5x IQR. e, Western blot validation (left) and relative protein expression quantification (right) of Sirt3, Gstt2 and C1qb in mice subjected to parabiosis.

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Extended Data Fig. 7 Interactions between different omics modalities.

a, Correlation of changes across experimental groups and readout types. For each comparison in parentheses (that is Young ISO vs. Old ISO), a z-score was computed for either promoter methylation or gene expression. The Pearson correlation of these z-scores was then determined for genes passing a differential expression/methylation threshold of p < 0.05 for the first (left) group listed in each comparison. b, Heat maps highlighting the concordance of readouts and experimental groups. For each heat map, the z-score of genes that are significantly (p < 0.05) differentially expressed or methylated is shown. Genes are ordered by the z-score in the top group. Color bar on the right denotes z-score. The directional concordance, a measure of how directionally aligned the changes between the two readouts/groups are, is shown at the top of each heat map. 50% concordance represents random changes across groups Two-tailed Benchamini-Hochberg corrected FDR was calculated to compare groups.

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Extended Data Fig. 8 Transcriptomic and epigenetic changes resulting from HPB.

a, Boxplots of RLD-transformed, log-normalized counts of Tert, Dnmt3b, Ly6e, Lmna and Pld1 across 6 groups (from left to right: old short-term isochronic (n = 5), old short-term heterochronic (n = 5), old long-term isochronic (n = 3), old long-term heterochronic (n = 3), old long-term isochronic detached (n = 3), and old long-term heterochronic detached (n = 3). b, Mean promoter methylation of the genes mentioned in (a) across liver RRBS samples across 6 groups (from left to right: young long-term isochronic (n = 6), old long-term isochronic (n = 5), old long-term heterochronic (n = 5), young long-term isochronic detached (n = 6), old long-term isochronic detached (n = 7), and old long-term heterochronic detached (n = 6). Color of the box around each gene signifies directionality (green: gene expression increases in Old HET mice compared to ISO, orange: gene expression decreases in Old HET mice compared to ISO). Despite evident changes in expression patterns for these genes, no significant changes in promoter methylation are observed. N represents biological replicates for each group. P values determined by two-tailed Welch’s t-test. Box plots represent median, 25–75 percentile and 1.5x IQR.

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Extended Data Fig. 9 SASP enrichment and differential expression across long-term and short-term HPB.

a-c, Running enrichment score for the senescence-associated secretory phenotype (SASP) gene set, comparing in (a) old isochronic and old heterochronic mice from the long-term HPB, in (b) old detached isochronic and old detached heterochronic mice from long-term HPB, and in (c) old detached isochronic and old detached heterochronic mice from short-term HPB. The P values for the gene set enrichment, along with the adjusted P value are shown in each panel. Positions of individual genes in the gene set are shown as black bars near the bottom of each panel. Long-term attached HPB shows the greatest negative enrichment for heterochronic samples, followed by detached long-term HPB and attached short-term HPB, respectively. d, Boxplots of RLD-transformed, log-normalized count of five SASP genes across 6 groups (from left to right: old short-term isochronic (n = 5), old short-term heterochronic (n = 5), old long-term isochronic (n = 3), old long-term heterochronic (n = 3), old long-term isochronic detached (n = 3), and old long-term heterochronic detached (n = 3). The log2 fold-change (log2FC) and associated P value are shown as calculated by two-tailed Welch’s test. Box plots represent median, 25–75 percentile and 1.5x IQR.

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Extended Data Fig. 10 STRING network representation of significantly down- and upregulated genes.

a, STRING network representation of significantly downregulated genes in livers of old heterochronic mice (n = 421 genes) compared to isochronic mice immediately after detachment or after a 2-month detachment period (protein-protein interaction q value < 1e-16). Genes were filtered based on directionality and absolute value of the log2FC. b, STRING network representation of significantly upregulated genes in livers of old heterochronic mice (n = 337 genes) compared to isochronic mice immediately after detachment or after a 2-month detachment period (protein-protein interaction q value < 1e-16). Genes were filtered based on directionality and absolute value of the log2FC.

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Zhang, B., Lee, D.E., Trapp, A. et al. Multi-omic rejuvenation and lifespan extension on exposure to youthful circulation. Nat Aging 3, 948–964 (2023). https://doi.org/10.1038/s43587-023-00451-9

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