Trajectories of childhood immune development and respiratory health relevant to asthma and allergy

  1. Howard HF Tang  Is a corresponding author
  2. Shu Mei Teo
  3. Danielle CM Belgrave
  4. Michael D Evans
  5. Daniel J Jackson
  6. Marta Brozynska
  7. Merci MH Kusel
  8. Sebastian L Johnston
  9. James E Gern
  10. Robert F Lemanske
  11. Angela Simpson
  12. Adnan Custovic
  13. Peter D Sly
  14. Patrick G Holt
  15. Kathryn E Holt
  16. Michael Inouye  Is a corresponding author
  1. Baker Heart and Diabetes Institute, Australia
  2. Imperial College London, United Kingdom
  3. University of Wisconsin School of Medicine and Public Health, United States
  4. University of Western Australia, Australia
  5. University of Manchester, United Kingdom
  6. The University of Melbourne, Australia
  7. University of Cambridge, United Kingdom

Abstract

Events in early life contribute to subsequent risk of asthma; however, the causes and trajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly, not all atopic individuals develop wheeze, and vice versa. The reasons for these differences are unclear. Using unsupervised model-based cluster analysis, we identified latent clusters within a prospective birth cohort with deep immunological and respiratory phenotyping. We characterised each cluster in terms of immunological profile and disease risk, and replicated our results in external cohorts from the UK and USA. We discovered three distinct trajectories, one of which is a high-risk 'atopic' cluster with increased propensity for allergic diseases throughout childhood. Atopy contributes varyingly to later wheeze depending on cluster membership. Our findings demonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthma pathogenesis and provide a foundation for improving management and prevention of childhood asthma.

Data availability

This study utilises extensive data from human subjects, specifically paediatric cohorts, for which eLife's policies recognise that there can be strong reasons to restrict access. For each of the cohorts involved in our study (CAS, COAST, MAAS), parents were consented on the use of biomedical data for allergy and asthma research, but not for the open sharing of their or their children's data. Studies were run in the late 1990s and early 2000s and we do not have ethics permission to attempt to recontact families to seek consent. Importantly, we note that key data features could risk re-identification of subjects (e.g. demographic data from small communities).However, we have provided public data at the summary level which can be used for subsequent studies, such as replication and meta-analysis. This is standard practice in sensitive data settings, such as genome-wide association studies. These data have been uploaded as Excel spreadsheets to FigShare for ease of data extraction:Supplementary Table 4 https://figshare.com/articles/Supplementary_File_1_1/6934052Supplementary Table 7 https://figshare.com/articles/Supplementary_File_1_2/6934055

Article and author information

Author details

  1. Howard HF Tang

    Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
    For correspondence
    Howard.Tang@baker.edu.au
    Competing interests
    The authors declare that no competing interests exist.
  2. Shu Mei Teo

    Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
    Competing interests
    The authors declare that no competing interests exist.
  3. Danielle CM Belgrave

    Department of Paediatrics, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Michael D Evans

    University of Wisconsin School of Medicine and Public Health, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7449-3993
  5. Daniel J Jackson

    University of Wisconsin School of Medicine and Public Health, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Marta Brozynska

    Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
    Competing interests
    The authors declare that no competing interests exist.
  7. Merci MH Kusel

    Telethon Kids Institute, University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  8. Sebastian L Johnston

    Airway Disease Infection Section, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. James E Gern

    University of Wisconsin School of Medicine and Public Health, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Robert F Lemanske

    University of Wisconsin School of Medicine and Public Health, Madison, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Angela Simpson

    Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Adnan Custovic

    Department of Paediatrics, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5218-7071
  13. Peter D Sly

    Telethon Kids Institute, University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  14. Patrick G Holt

    Telethon Kids Institute, University of Western Australia, Perth, Australia
    Competing interests
    The authors declare that no competing interests exist.
  15. Kathryn E Holt

    Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Melbourne, Australia
    Competing interests
    The authors declare that no competing interests exist.
  16. Michael Inouye

    Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    mi336@medschl.cam.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9413-6520

Funding

National Health and Medical Research Council (1049539)

  • Michael Inouye

National Health and Medical Research Council (PhD Scholarship)

  • Howard HF Tang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Ethics approval and consent requirements for each cohort were met as follows: The CAS study was approved by the ethics committees of the King Edward Memorial and Princess Margaret Hospitals in Western Australia; fully informed parental consent was obtained for all subjects. The COAST study was approved by the Human Subjects Committee of the University of Wisconsin. The MAAS study was approved by a Manchester Local Research Ethics Committee (ERP/94/032; SOU/00/258; 03/SM/400; Study registration ISRCTN72673620); fully informed parental consent was obtained for all subjects across all cohorts.

Copyright

© 2018, Tang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,968
    views
  • 293
    downloads
  • 23
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Howard HF Tang
  2. Shu Mei Teo
  3. Danielle CM Belgrave
  4. Michael D Evans
  5. Daniel J Jackson
  6. Marta Brozynska
  7. Merci MH Kusel
  8. Sebastian L Johnston
  9. James E Gern
  10. Robert F Lemanske
  11. Angela Simpson
  12. Adnan Custovic
  13. Peter D Sly
  14. Patrick G Holt
  15. Kathryn E Holt
  16. Michael Inouye
(2018)
Trajectories of childhood immune development and respiratory health relevant to asthma and allergy
eLife 7:e35856.
https://doi.org/10.7554/eLife.35856

Share this article

https://doi.org/10.7554/eLife.35856

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Fangluo Chen, Dylan C Sarver ... G William Wong
    Research Article

    Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.

    1. Computational and Systems Biology
    Huiyong Cheng, Dawson Miller ... Qiuying Chen
    Research Article

    Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.