1. Epidemiology and Global Health
  2. Immunology and Inflammation
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A Global Immunological Observatory to meet a time of pandemics

  1. Michael J Mina  Is a corresponding author
  2. C Jessica E Metcalf  Is a corresponding author
  3. Adrian B McDermott
  4. Daniel C Douek
  5. Jeremy Farrar
  6. Bryan T Grenfell
  1. Harvard School of Public Health, United States
  2. Princeton University, United States
  3. National Institutes of Health, United States
  4. The Wellcome Trust, United Kingdom
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Cite this article as: eLife 2020;9:e58989 doi: 10.7554/eLife.58989

Abstract

SARS-CoV-2 presents an unprecedented international challenge, but it will not be the last such threat. Here, we argue that the world needs to be much better prepared to rapidly detect, define and defeat future pandemics. We propose that a Global Immunological Observatory (GIO) and associated developments in systems immunology, therapeutics and vaccine design should be at the heart of this enterprise.

Data availability

No data is involved in this manuscript.

Article and author information

Author details

  1. Michael J Mina

    Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, United States
    For correspondence
    mmina@hsph.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0674-5762
  2. C Jessica E Metcalf

    Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States
    For correspondence
    cmetcalf@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3166-7521
  3. Adrian B McDermott

    Vaccine Research Center, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0616-9117
  4. Daniel C Douek

    Vaccine Research Center, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Jeremy Farrar

    The Wellcome Trust, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Bryan T Grenfell

    Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3227-5909

Funding

The authors declare that there was no funding for this work.

Reviewing Editor

  1. Peter Rodgers, eLife, United Kingdom

Publication history

  1. Received: May 18, 2020
  2. Accepted: June 5, 2020
  3. Accepted Manuscript published: June 8, 2020 (version 1)
  4. Version of Record published: June 12, 2020 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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Further reading

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    2. Genetics and Genomics
    Daniel W Belsky et al.
    Research Advance

    Background: Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al. 2020). Here we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).

    Methods: We used data from the Dunedin Study 1972-3 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.

    Results: DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.

    Conclusions: DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.

    Funding: This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.

    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Danni A Gadd et al.
    Research Article

    Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore – disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.