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.

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

Figure 1.

Cytokine profiles across compartments in nonsmokers, representing baseline cytokine signatures that exist in the absence of tobacco product use exposures. Experimentally measured cytokine concentrations are plotted, with average levels as the lines and standard deviations as the shaded region, by compartment. Cytokines are ordered from those with the highest (left) to lowest (right) average concentration in NLF samples. NLF, nasal lavage fluid.

Figure 2.

Comparison of cytokine distribution similarity across compartments stratified by smoking status, identified through correlation and random permutation analyses. Two “data types” are included here: 1) “Real” data (in red boxes), which refer to the correlation results between two experimentally observed cytokine concentration datasets and 2) “real and simulated” data (in turquoise triangles), which refer to the correlation results between one experimentally observed cytokine concentration data set (i.e., the “real” data set) and one simulated dataset that was randomly generated based on the original data set. Points were labeled with the compartment comparison text identifiers if they were highly correlated and statistically significant (R > 0.7 and P < 0.05).

Figure 3.

Cytokine principal component plots showing clusters at baseline (in nonsmokers). The cytokine concentration distributions are visualized according to the evaluated compartments, NLF, NELF, sputum, and serum. Cytokine concentration values were transformed using data reduction approaches (i.e., PCA) to illustrate the overall trends in cytokine groupings in 2-D. The illustrated principal components show the resulting reduced values, which captured the majority of variance in data across samples. Cytokine clusters are shown as shaded shapes within these PCA plots. NELF, nasal epithelial lining fluid; NLF, nasal lavage fluid; PCA, principal component analysis; 2-D, two dimensional.

Figure 4.

Individual cytokine levels and eigencytokine weights for those identified in NLF in the example derivation of cytokine clusters. Subjects are ordered from lowest to highest average cytokine concentration from left to right. Within each cluster, cytokines are ordered from lowest to highest average cytokine concentration from bottom to top. NLF, nasal lavage fluid; NS, nonsmoker.

Figure 5.

Deriving “Consensus Clusters” of cytokines that consistently demonstrate comodulation patterns across regions of the respiratory tract commonly evaluated in clinical and toxicological research. Venn diagrams are used here to compare which cytokines clustered together across each respiratory compartment, yielding Consensus Cluster A, including cytokines involved in host defense (A); Consensus Cluster B, including cytokines involved in chemotactic immune cell recruitment (B); and Consensus Cluster C, including cytokines involved in immune cell adhesion and inflammation (C). NELF, nasal epithelial lining fluid; NLF, nasal lavage fluid.

Figure 6.

Individual cytokine expression levels (A) and eigencytokine values (B) describing cluster-level trends for the derived Consensus Clusters across all subjects. Subjects were ordered first according to tobacco use status, starting with nonsmokers (NS) then cigarette smokers (CS) and e-cigarette users (Ecig). Within tobacco use groups, subjects are ordered from lowest to highest average cytokine concentration (or eigenvector value) from left to right. Within each cluster, cytokines are ordered from lowest to highest average cytokine concentration from bottom to top. This figure depicts minimal changes occurring at the individual cytokine level (A) and much more robust changes occurring at the cytokine cluster level associated with tobacco use (B), particularly in e-cigarette users compared with nonsmokers.

Figure 7.

Cytokine principal component plots showing clusters at baseline (in nonsmokers) in NELF samples from the primary cohort (left) and separate validation cohort (right). Cytokine concentration distributions were transformed using data reduction approaches (i.e., PCA) to illustrate the overall trends in cytokine groupings in 2-D. The illustrated principal components show the resulting reduced values, which captured the majority of variance in data across NELF samples. Cytokine clusters are shown as shaded shapes within these PCA plots. NELF, nasal epithelial lining fluid; PCA, principal component analysis; 2-D, two dimensional.