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

Abstract

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.

Nasal biomarkers of immune function differ based on smoking and respiratory disease status

“…These include a cluster with IP-10, IFNγ, IL-2, IL-4 (r = 0.39, gray, Figure 2), and a cluster with IL-5, Eotaxin, and TNFβ (r = 0.26, green, Figure 2). Differences in clusters of mediators may identify patterns among immune mediators with interacting correlated functions that are not evident when evaluating individual mediators alone, a finding that has previously been reported in other airway biomarker studies of smokers (Payton et al, 2022). These clusters have some biological plausibility, as increases in included mediators have been reported with cigarette smoke exposure in multiple studies.…”

“…This exploratory study further suggests that the nasal passage may be an ideal target for biomarker detection. It has already been demonstrated that serum biomarkers are often not the best representative source for information on respiratory immune status; rather, airway samples, even from sources such as the nose, are better correlates for lower airway than circulating blood (Payton et al, 2022 ). In the future, nasosorption may be a feasible method to evaluate respiratory biomarkers of disease or exposure in the clinical environment with further validation.…”

“…Such findings on serum biomarkers may not translate to the inflammatory processes within the respiratory tract of patients with chronic airway disease or smoking-related injury (Gianniou et al, 2016;Hurst et al, 2005;Rebuli et al, 2018). We have previously demonstrated that levels of immune mediators measured in upper respiratory compartment, particularly the nose, and lower respiratory compartments (e.g., sputum), correlate well with each other, but poorly with levels of the same mediators measured in the bloodstream (Payton et al, 2022). We posit that the upper airway, including the nose, which shares physiology and histomorphology with the lower airway according to the "unified airway" hypothesis, is a more relevant sampling site for respiratory biomarker research (Chang, 2014;Giavina-Bianchi et al, 2016).…”

Burn Pit Smoke Condensate-Mediated Toxicity in Human Airway Epithelial Cells

“…To further investigate the impact of each condensate (plastic or cardboard) and burn conditions (control, smoldering, or flaming) on cytokine and gene expression profiles after exposure, principal component analysis (PCA) was performed to understand if these sample conditions yield similar expression profiles at an aggregate level ignoring the donor-to-donor variations, similar to our previous study. 36 This subset of sample conditions was prioritized to enhance the resulting visualizations while focusing on conditions of high importance. PCA is an unsupervised machine learning technique that seeks to compress the variance from an original data set into the fewest number of principal components.…”

Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data

“…One unsupervised ML algorithm, k-means, groups observations into the optimal number of clusters with each observation belonging to the clusters with the closest mean (Ecosystem, 2018). As an example, an inhalation toxicology study carried out by our group evaluated cytokine signatures derived from multiple biological samples that commonly inform respiratory responses, including nasal lavage fluid, nasal epithelial lining fluid, sputum, and circulating serum, to determine if cytokine levels differed based on these sampling locations (Payton et al, 2022). K-means grouped cytokines into clusters within each respiratory tract region, separately.…”