4.5 Article

Cytokine signature clusters as a tool to compare changes associated with tobacco product use in upper and lower airway samples

出版社

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/ajplung.00299.2021

关键词

cytokine clusters; tobacco product use; unsupervised machine learning; upper and lower airways

资金

  1. National Institutes of Health (NIH) [T32 ES007126, P50 HL120100, P30ES010126]
  2. NIH [P50 HL120100]
  3. US Food and Drug Administration Center for Tobacco Products (CTP)
  4. University of North Carolina (UNC) Institute for Environmental Health Solutions (IEHS)

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This study compared cytokine levels in different sample types and found significant correlations among individual cytokines in respiratory tract samples (nasal lavage fluid, nasal epithelial lining fluid, and sputum), while serum samples showed no significant difference compared to random permutations. Furthermore, the study found that tobacco product use can alter coexpressed cytokine clusters, especially with e-cigarette use, indicating a degree of uniformity in cytokine host defense and immune cell recruitment responses in the upper and lower airways.
Inhalation exposure to cigarette smoke and e-cigarette aerosol is known to alter the respiratory immune system, particularly cytokine signaling. In assessments of health impacts of tobacco product use, cytokines are often measured using a variety of sample types, from serum to airway mucosa. However, it is currently unclear whether and how well cytokine levels from different sample types and the airway locations they represent are correlated, making comparing studies that utilize differing sample types challenging. To address this challenge, we compared baseline cytokine signatures in upper and lower airways and systemic samples and evaluated how groups of coexpressed cytokines change with tobacco product use. Matched nasal lavage fluid (NLF), nasal epithelial lining fluid (NELF), sputum, and circulating serum samples were collected from 14 nonsmokers, 13 cigarette smokers, and 17 e-cigarette users and analyzed for levels of 22 cytokines. Individual cytokine signatures were first compared across each sample type, followed by identification of cytokine clusters within each sample type. Identified clusters were then evaluated for potential alterations following tobacco product use using eigenvector analyses. Individual cytokine signatures in the respiratory tract were significantly correlated (NLF, NELF, and sputum) compared with randomly permutated signatures, whereas serum was not significantly different from random permutations. Cytokine clusters that were similar across airway sample types were modified by tobacco product use, particularly e-cigarettes, indicating a degree of uniformity in terms of how cytokine host defense and immune cell recruitment responses cooperate in the upper and lower airways. Overall, cluster-based analyses were found to be especially useful in small cohort assessments, providing higher sensitivity than individual signatures to detect biologically meaningful differences between tobacco use groups. This novel cluster analysis approach revealed that eigencytokine patterns in noninvasive upper airway samples simulate cytokine patterns in lower airways.

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