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Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Howard H.F. Tang, Peter D. Sly, Patrick G. Holt, Kathryn E. Holt, Michael Inouye
European Respiratory Journal 2020 55: 1900844; DOI: 10.1183/13993003.00844-2019
Howard H.F. Tang
1Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
2Cambridge Baker Systems Genomics Initiative, Dept of Public Health and Primary Care, University of Cambridge, Cambridge, UK
3School of BioSciences, The University of Melbourne, Parkville, Australia
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  • ORCID record for Howard H.F. Tang
  • For correspondence: Howard.Tang@baker.edu.au
Peter D. Sly
4Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia
5Telethon Kids Institute, University of Western Australia, Perth, Australia
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Patrick G. Holt
4Queensland Children's Medical Research Institute, The University of Queensland, Brisbane, Australia
5Telethon Kids Institute, University of Western Australia, Perth, Australia
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Kathryn E. Holt
6Dept of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia
7London School of Hygiene and Tropical Medicine, London, UK
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Michael Inouye
1Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
2Cambridge Baker Systems Genomics Initiative, Dept of Public Health and Primary Care, University of Cambridge, Cambridge, UK
3School of BioSciences, The University of Melbourne, Parkville, Australia
8The Alan Turing Institute, London, UK
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Abstract

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent “omic”-level findings, and examine how these findings have been systematically integrated to generate further insight.

Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or “endotypes” that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.

The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Abstract

With the recent influx of “big data” in asthma research, clinicians and scientists need to become familiar with analytical approaches that use systems-based methods to make sense of large datasets http://bit.ly/2oUO1tG

Footnotes

  • This article has supplementary material available from erj.ersjournals.com

  • Conflict of interest: H.H.F. Tang has nothing to disclose.

  • Conflict of interest: P.D. Sly has nothing to disclose.

  • Conflict of interest: P.G. Holt has nothing to disclose.

  • Conflict of interest: K.E. Holt has nothing to disclose.

  • Conflict of interest: M. Inouye has nothing to disclose.

  • Support statement: Supported by the National Health and Medical Research Council (Grant: APP1114753). Funding information for this article has been deposited with the Crossref Funder Registry.

  • Received April 29, 2019.
  • Accepted September 12, 2019.
  • Copyright ©ERS 2020
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Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges
Howard H.F. Tang, Peter D. Sly, Patrick G. Holt, Kathryn E. Holt, Michael Inouye
European Respiratory Journal Jan 2020, 55 (1) 1900844; DOI: 10.1183/13993003.00844-2019

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Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges
Howard H.F. Tang, Peter D. Sly, Patrick G. Holt, Kathryn E. Holt, Michael Inouye
European Respiratory Journal Jan 2020, 55 (1) 1900844; DOI: 10.1183/13993003.00844-2019
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