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Am J Physiol Lung Cell Mol Physiol. 2019 Nov 1;317(5):L556-L568. doi: 10.1152/ajplung.00554.2018. Epub 2019 Aug 21.

Integrating multiomics longitudinal data to reconstruct networks underlying lung development.

Author information

1
Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania.
2
Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut.
3
Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, La Jolla, California.
4
Rady Children's Hospital of San Diego, San Diego California.
5
Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama.

Abstract

A comprehensive understanding of the dynamic regulatory networks that govern postnatal alveolar lung development is still lacking. To construct such a model, we profiled mRNA, microRNA, DNA methylation, and proteomics of developing murine alveoli isolated by laser capture microdissection at 14 predetermined time points. We developed a detailed comprehensive and interactive model that provides information about the major expression trajectories, the regulators of specific key events, and the impact of epigenetic changes. Intersecting the model with single-cell RNA-Seq data led to the identification of active pathways in multiple or individual cell types. We then constructed a similar model for human lung development by profiling time-series human omics data sets. Several key pathways and regulators are shared between the reconstructed models. We experimentally validated the activity of a number of predicted regulators, leading to new insights about the regulation of innate immunity during lung development.

KEYWORDS:

alveolar development; laser capture microdissection; time-series omics data

Comment in

PMID:
31432713
PMCID:
PMC6879899
[Available on 2020-11-01]
DOI:
10.1152/ajplung.00554.2018

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