Undulating changes in human plasma proteome profiles across the lifespan

Abstract

Aging is a predominant risk factor for several chronic diseases that limit healthspan1. Mechanisms of aging are thus increasingly recognized as potential therapeutic targets. Blood from young mice reverses aspects of aging and disease across multiple tissues2,3,4,5,6,7,8,9,10, which supports a hypothesis that age-related molecular changes in blood could provide new insights into age-related disease biology. We measured 2,925 plasma proteins from 4,263 young adults to nonagenarians (18–95 years old) and developed a new bioinformatics approach that uncovered marked non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh and eighth decades of life reflected distinct biological pathways and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits. This new approach to the study of aging led to the identification of unexpected signatures and pathways that might offer potential targets for age-related diseases.

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Fig. 1
Fig. 2: Clustering of protein trajectories identifies linear and non-linear changes during aging.
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Data availability

We created a searchable web interface to mine the human INTERVAL and LonGenity datasets: https://twc-stanford.shinyapps.io/aging_plasma_proteome/.

The independent human cohorts and mouse protein data are available in Supplementary Tables 16 and 17. The INTERVAL data are available through the European Genome–Phenome Archive under accession EGAS00001002555.

Code availability

An R package for DE-SWAN is available in GitHub: http://lehallib.github.io/DEswan/.

References

  1. 1.

    Harman, D. The aging process: major risk factor for disease and death. Proc. Natl Acad. Sci. USA 88, 5360–5363 (1991).

  2. 2.

    Baht, G. S. et al. Exposure to a youthful circulation rejuvenates bone repair through modulation of β-catenin. Nat. Commun. 6, 7131 (2015).

  3. 3.

    Conboy, I. M. et al. Rejuvenation of aged progenitor cells by exposure to a young systemic environment. Nature 433, 760–764 (2005).

  4. 4.

    Huang, Q. et al. A young blood environment decreases aging of senile mice kidneys. J. Gerontol. A Biol. Sci. Med. Sci. 73, 421–428 (2018).

  5. 5.

    Katsimpardi, L. et al. Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors. Science 344, 630–634 (2014).

  6. 6.

    Loffredo, F. S. et al. Growth differentiation factor 11 is a circulating factor that reverses age-related cardiac hypertrophy. Cell 153, 828–839 (2013).

  7. 7.

    Salpeter, S. J. et al. Systemic regulation of the age-related decline of pancreatic beta-cell replication. Diabetes 62, 2843–2848 (2013).

  8. 8.

    Sinha, M. et al. Restoring systemic GDF11 levels reverses age-related dysfunction in mouse skeletal muscle. Science 344, 649–652 (2014).

  9. 9.

    Villeda, S. A. et al. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature 477, 90–94 (2011).

  10. 10.

    Villeda, S. A. et al. Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice. Nat. Med. 20, 659–663 (2014).

  11. 11.

    Valdes, A. M., Glass, D. & Spector, T. D. Omics technologies and the study of human ageing. Nat. Rev. Genet. 14, 601–607 (2013).

  12. 12.

    Stegeman, R. & Weake, V. M. Transcriptional signatures of aging. J. Mol. Biol. 429, 2427–2437 (2017).

  13. 13.

    Aramillo Irizar, P. et al. Transcriptomic alterations during ageing reflect the shift from cancer to degenerative diseases in the elderly. Nat. Commun. 9, 327 (2018).

  14. 14.

    Castellano, J. M. et al. Human umbilical cord plasma proteins revitalize hippocampal function in aged mice. Nature 544, 488–492 (2017).

  15. 15.

    Di Angelantonio, E. et al. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet 390, 2360–2371 (2017).

  16. 16.

    Gubbi, S. et al. Effect of exceptional parental longevity and lifestyle factors on prevalence of cardiovascular disease in offspring. Am. J. Cardiol. 120, 2170–2175 (2017).

  17. 17.

    Zhou, J. & Rossi, J. Aptamers as targeted therapeutics: current potential and challenges. Nat. Rev. Drug Discov. 16, 440 (2017).

  18. 18.

    Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).

  19. 19.

    Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010).

  20. 20.

    Austad, S. N. & Fischer, K. E. Sex differences in lifespan. Cell Metab. 23, 1022–1033 (2016).

  21. 21.

    Ostan, R. et al. Gender, aging and longevity in humans: an update of an intriguing/neglected scenario paving the way to a gender-specific medicine. Clin. Sci. 130, 1711–1725 (2016).

  22. 22.

    Tanaka, T. et al. Plasma proteomic signature of age in healthy humans. Aging Cell 17, e12799 (2018).

  23. 23.

    Cohen, A. A. Aging across the tree of life: the importance of a comparative perspective for the use of animal models in aging. Biochim. Biophys. Acta. Mol. Basis Dis. 1864, 2680–2689 (2018).

  24. 24.

    Guiraud, S. et al. Identification of serum protein biomarkers for utrophin based DMD therapy. Sci. Rep. 7, 43697 (2017).

  25. 25.

    Wang, R. N. et al. Bone morphogenetic protein (BMP) signaling in development and human diseases. Genes Dis. 1, 87–105 (2014).

  26. 26.

    Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

  27. 27.

    Sattlecker, M. et al. Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 10, 724–734 (2014).

  28. 28.

    Sullivan, K. D. et al. Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation. Sci. Rep. 7, 14818 (2017).

  29. 29.

    Ganz, P. et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA 315, 2532–2541 (2016).

  30. 30.

    Carayol, J. et al. Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator. Nat. Commun. 8, 2084 (2017).

  31. 31.

    Go, A. S. et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 127, e6–e245 (2013).

  32. 32.

    Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat. Rev. Endocrinol. 14, 576–590 (2018).

  33. 33.

    Franceschi, C. et al. The continuum of aging and age-related diseases: common mechanisms but different rates. Front. Med. 5, 61 (2018).

  34. 34.

    Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

  35. 35.

    Castellano, J. M., Kirby, E. D. & Wyss-Coray, T. Blood-borne revitalization of the aged brain. JAMA Neurol. 72, 1191–1194 (2015).

  36. 36.

    Wiklund, F. E. et al. Macrophage inhibitory cytokine-1 (MIC-1/GDF15): a new marker of all-cause mortality. Aging Cell 9, 1057–1064 (2010).

  37. 37.

    Cohen, E. & Dillin, A. The insulin paradox: aging, proteotoxicity and neurodegeneration. Nat. Rev. Neurosci. 9, 759–767 (2008).

  38. 38.

    Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

  39. 39.

    Sha, S. J., et al. Safety, tolerability, and feasibility of young plasma infusion in the plasma for Alzheimer symptom amelioration study: a randomized clinical trial. JAMA Neurol. 76, 35–40 (2018).

  40. 40.

    Mehan, M. R. et al. Protein signature of lung cancer tissues. PLoS One 7, e35157 (2012).

  41. 41.

    Britschgi, M. et al. Modeling of pathological traits in Alzheimer’s disease based on systemic extracellular signaling proteome. Mol. Cell Proteomics 10, M111 008862 (2011).

  42. 42.

    Franceschi, C. et al. Genetics of healthy aging in Europe: the EU-integrated project GEHA (GEnetics of Healthy Aging). Ann. NY Acad. Sci. 1100, 21–45 (2007).

  43. 43.

    Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, 2011).

  44. 44.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statis. Soc. B 57, 289–300 (1995).

  45. 45.

    Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

  46. 46.

    Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

  47. 47.

    Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

  48. 48.

    Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for Gene Ontology. https://doi.org/10.18129/B9.bioc.topGO (2016).

  49. 49.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

  50. 50.

    Carlson, M. org.Hs.eg.db: genome wide annotation for human. https://doi.org/10.18129/B9.bioc.org.Hs.eg.db (2017).

  51. 51.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  52. 52.

    Castellano, J. M. et al. In vivo assessment of behavioral recovery and circulatory exchange in the peritoneal parabiosis model. Sci. Rep. 6, 29015 (2016).

  53. 53.

    Pagès, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: efficient manipulation of biological strings. https://doi.org/10.18129/B9.bioc.Biostrings (2019).

  54. 54.

    Dray, S. & Dufour, A. B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw 22, 1–20 (2007).

  55. 55.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

  56. 56.

    Lehallier, B. et al. Combined plasma and cerebrospinal fluid signature for the prediction of midterm progression from mild cognitive impairment to Alzheimer disease. JAMA Neurol. 73, 203–212 (2016).

  57. 57.

    Wang, M., Zhao, Y. & Zhang, B. Efficient test and visualization of multi-set intersections. Sci. Rep. 5, 16923 (2015).

  58. 58.

    Epskamp, S., Cramer, A., Waldorp, L., Schmittmann, V. & Borsboom, D. qgraph: network visualizations of relationships in psychometric data. J. Stat. Softw. 48, 1–18 (2012).

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Acknowledgements

We thank the members of the Wyss-Coray laboratory for feedback and support. We thank the clinical staff for human blood and plasma collection/coordination. We thank A. Butterworth for his help in getting access to the INTERVAL proteomics data. The AddNeuroMed data are from a public–private partnership supported by EFPIA companies and the European Union Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5. Clinical leads responsible for data collection were I. Kłoszewska (Lodz), S. Lovestone (London), P. Mecocci (Perugia), H. Soininen (Kuopio), M. Tsolaki (Thessaloniki) and B. Vellas (Toulouse); imaging leads were A. Simmons (London), L.O. Wahlund (Stockholm) and C. Spenger (Zurich); and bioinformatics leads were R. Dobson (London) and S. Newhouse (London). This work was supported by National Institutes of Health National Institute on Aging (NIA) F32 1F32AG055255 01A1 (D.G.), Hungarian Brain Research Program Grant No. 2017-1.2.1-NKP-2017-00002 (T.N.), the Fulbright Foreign Student Program (T.N.), the Cure Alzheimer’s Fund (T.W.-C.), Nan Fung Life Sciences (T.W.-C.), the NOMIS Foundation (T.W.-C.), the Stanford Brain Rejuvenation Project (an initiative of the Stanford Wu Tsai Neurosciences Institute), the Paul F. Glenn Center for Aging Research (T.W.-C.), NIA R01 AG04503 and DP1 AG053015 (T.W.-C.) and the NIA-funded Stanford Alzheimer’s Disease Research Center P50AG047366, NIA K23AG051148 (S.M.), R01AG061155 (S.M.), the American Federation for Aging Research (S.M.), R01AG044829 (J.V. and N.B.), NIA R01AG057909 (N.B.), the Nathan Shock Center of Excellence for the Basic Biology of Aging P30AG038072 (N.B.) and the Glenn Center for the Biology of Human Aging (N.B.).

Author information

B.L. and T.W.-C. planned the study. D.B., C.F., S.M., J.V., S.S. and N.B. provided human plasma samples. N.S., S.E.L. and H.Y. performed the mouse experiments. B.L. analyzed the data, with contributions from T.N. and A.K. P.M.L. developed the searchable web interface (shiny app). B.L., D.G. and T.W.-C. wrote the manuscript. A.K., C.F., S.M., J.V., S.S., N.B. and T.W.-C. supervised the study. All authors edited and reviewed the manuscript.

Correspondence to Benoit Lehallier or Tony Wyss-Coray.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Brett Bennedetti and Jennifer Sargent were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Sample demographics.

Age (a, b), cohort (a, b) and sex distributions (c) of the 4,263 subjects from the INTERVAL and LonGenity cohorts. (d) Age and cohort distributions of the 171 subjects from the 4 independent cohorts.

Extended Data Fig. 2 Comparing age and sex effects in independent cohorts.

(a) Age and sex effects in the INTERVAL and LonGenity studies (n = 4,263) were compared to age and sex effects in 4 independent cohorts analyzed together (n = 171) and to age effect from Tanaka et al. (n = 240, 2018). The aging plasma proteome was measured with the SomaScan assay in these cohorts and 888 proteins were measured in all studies (b) Scatter plot representing the signed -log10(q value) of the sex effect in the INTERVAL/LonGenity cohorts (x axis, n = 4,263) vs the 4 independent cohorts (y-axis, n = 171). Similar analysis for the age effect in the 4 independent cohorts (c, n = 171) and in Tanaka et al study (d, n = 240).

Extended Data Fig. 3 Deeper investigation of the aging proteomic clock.

(a) Prediction of age in the 4 independent cohorts (n = 171) using the proteomic clock. Only 141 proteins out of the 373 constituting the clock were measured in these samples. (b) Prediction of age in the discovery cohort (n = 2,817) using the 373 plasma markers. (c) Feature reduction of the aging model in the Discovery and Validation cohorts to estimate whether a subset of the aging signature can provide similar results to the 373 aging proteins. Dashed lines represent a broken stick model and indicate the best compromise between number of variables and prediction accuracy. (d) Heatmap representing the associations between delta age and 334 clinical and functional variables. For quantitative traits, linear models adjusted for delta age, age and sex were used and significance was tested using F-test. For binary outcomes, binomial generalized linear models adjusted for delta age, age and sex were used and significance was tested using likelihood ratio chi-square test. As in (c) the analysis was performed for the top 2 to top 373 variables predicting age. The non-uniformity in the heatmaps suggests that specific subsets of proteins may best predict certain clinical and functional parameters.

Extended Data Fig. 4 Proteins and proteome undulations in independent human cohorts and in mouse.

(a) Trajectories of 5 selected proteins based on the INTERVAL and LonGenity cohorts (n = 4,263, left) and 4 independent human cohorts (n = 171, right). Trajectories were estimated using LOESS regression. Undulation of the 1,305 plasma proteins measured in 4 independent cohorts (b, n = 171) and in mouse (c, n = 81). Plasma proteins levels were z-scored and LOESS regression was fitted for each plasma factor.

Extended Data Fig. 5 Cluster trajectories in independent cohorts.

Protein trajectories for the 8 clusters identified in the INTERVAL and LonGenity cohorts (left column). Thicker lines represent the average trajectory for each cluster. Cluster trajectories for the subset of proteins measured in the 4 independent cohorts (middle column). Corresponding cluster trajectories in 4 independent cohorts (right column).

Extended Data Fig. 6 Pathways in clusters.

Pathway enrichment was tested using GO, Reactome and KEGG databases (n = 4,263). Enrichment was tested using Fisher’s exact test (GO) and hypergeometric test (Reactome and KEGG). The top 4 pathways for each cluster are shown. Pathway IDs and number of plasma proteins associated are represented in the table.

Extended Data Fig. 7 DE-SWAN age effect for multiple q-values cutoffs, windows size and after phenotypes permutations.

Different Q-value cutoffs are represented in (a). Similar analysis with different after phenotype permutations (b) and different windows size in (c). The 3 local peaks identified at age 34, 60 and 78 are indicated by colored vertical lines.

Extended Data Fig. 8 Cis-associations and aging waves.

Enrichment for cis-association in the waves of aging proteins identified by DE-SWAN. Aging proteins were ranked based on p-values at age 34, 60 and 78 and the cumulative number of cis-associations was counted. One-sided permutation tests (1e + 5 permutations) were used to assess significance.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4

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Supplementary Tables 1–17

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Lehallier, B., Gate, D., Schaum, N. et al. Undulating changes in human plasma proteome profiles across the lifespan. Nat Med 25, 1843–1850 (2019). https://doi.org/10.1038/s41591-019-0673-2

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