Multiomic Investigations into Lung Health and Disease

Authors

Sarah E Blutt
Cristian Coarfa
Josef Neu
Mohan Pammi

Publication Date

8-19-2023

Journal

Microorganisms

DOI

10.3390/microorganisms11082116

PMID

37630676

PMCID

PMC10459661

PubMedCentral® Posted Date

8-19-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

multiomics, lung, pulmonary, disease models, machine learning

Abstract

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

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