Transcriptional changes of the aging lung

Author:

Jia Minxue12ORCID,Agudelo Garcia Paula A.3ORCID,Ovando‐Ricardez Jose A.3,Tabib Tracy4,Bittar Humberto T.4,Lafyatis Robert A.4,Mora Ana L.3,Benos Panayiotis V.125ORCID,Rojas Mauricio3

Affiliation:

1. Department of Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh Pennsylvania USA

2. Joint Carnegie Mellon ‐ University of Pittsburgh Computational Biology Ph.D. Program Pittsburgh Pennsylvania USA

3. Department of Internal Medicine Ohio State University Columbus Ohio USA

4. Division of Rheumatology and Clinical Immunology, Department of Medicine University of Pittsburgh School of Medicine Pittsburgh Pennsylvania USA

5. Department of Epidemiology University of Florida Gainesville Florida USA

Abstract

AbstractAging is a natural process associated with declined organ function and higher susceptibility to developing chronic diseases. A systemic single‐cell type‐based study provides a unique opportunity to understand the mechanisms behind age‐related pathologies. Here, we use single‐cell gene expression analysis comparing healthy young and aged human lungs from nonsmoker donors to investigate age‐related transcriptional changes. Our data suggest that aging has a heterogenous effect on lung cells, as some populations are more transcriptionally dynamic while others remain stable in aged individuals. We found that monocytes and alveolar macrophages were the most transcriptionally affected populations. These changes were related to inflammation and regulation of the immune response. Additionally, we calculated the LungAge score, which reveals the diversity of lung cell types during aging. Changes in DNA damage repair, fatty acid metabolism, and inflammation are essential for age prediction. Finally, we quantified the senescence score in aged lungs and found that the more biased cells toward senescence are immune and progenitor cells. Our study provides a comprehensive and systemic analysis of the molecular signatures of lung aging. Our LungAge signature can be used to predict molecular signatures of physiological aging and to detect common signatures of age‐related lung diseases.

Funder

National Institutes of Health

Publisher

Wiley

Subject

Cell Biology,Aging

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