Transient and Persistent Metabolomic Changes in Plasma following Chronic Cigarette Smoke Exposure in a Mouse Model

<div><p>Cigarette smoke exposure is linked to the development of a variety of chronic lung and systemic diseases in susceptible individuals. Metabolomics approaches may aid in defining disease phenotypes, may help predict responses to treatment, and could identify biomarkers of risk for developing disease. Using a mouse model of chronic cigarette smoke exposure sufficient to cause mild emphysema, we investigated whether cigarette smoke induces distinct metabolic profiles and determined their persistence following smoking cessation. Metabolites were extracted from plasma and fractionated based on chemical class using liquid-liquid and solid-phase extraction prior to performing liquid chromatography mass spectrometry-based metabolomics. Metabolites were evaluated for statistically significant differences among group means (<i>p</i>-value≤0.05) and fold change ≥1.5). Cigarette smoke exposure was associated with significant differences in amino acid, purine, lipid, fatty acid, and steroid metabolite levels compared to air exposed animals. Whereas 60% of the metabolite changes were reversible, 40% of metabolites remained persistently altered even following 2 months of smoking cessation, including nicotine metabolites. Validation of metabolite species and translation of these findings to human plasma metabolite signatures induced by cigarette smoking may lead to the discovery of biomarkers or pathogenic pathways of smoking-induced disease.</p></div

Inter-relation of pathways.

<p>Biological relationships among chemical classes of compounds showing the regulation of one group of metabolites and its effects on other biological classes down- or up-stream. Pathway was adapted from KEGG, Lipid Maps, and SMPDB. Metabolites within this pathway passed p-value≤0.05 and fold change ≥1.5 filters. ↓ indicates the number of differentially regulated metabolites which decreased due to smoking, ↑indicates the number of differentially regulated metabolites which increased due to smoking for a particular chemical class of compounds, ^NDno data was available for these compounds in this dataset.</p

Heat map of differentially regulated steroids and derivatives.

<p>Statistical analysis was performed using Mass Profiler Professional software (Agilent) with <i>p</i>-value cutoff ≤0.05. The mean abundance levels for each of the differentially regulated metabolites detected is shown. Blue represents a decrease in mean metabolite abundance, and yellow/beige represents an increase in mean metabolite abundance.</p

(A) Nicotine pathway and heat maps of CS metabolites in (B) four month and (C) six month comparisons.

<p>CS = cigarette smoke. Pathway diagram reprinted with permission from Hukkanen et al. Circled compounds represent the metabolites identified in the plasma of mouse samples in the current study. For each heat map, blue represents a decrease in mean metabolite abundance, and yellow/beige represents an increase in mean metabolite abundance. For example, nicotine glucuronide is increased in smoking in both the 4- and 6- month smoking mice. Samples were analyzed on an Agilent 6410 ESI-TOF in positive ionization mode, data was processed using Mass Profiler Professional, and quantitative data was obtained using Mass Hunter Quantitative analysis software. Tentative identification was performed using ID Browser within the Mass Profiler Professional software. ID Browser in-house database is comprised of Metlin, Lipid Maps and HMDB. * indicates <i>p</i>-value≤0.05 between the air control and cigarette smoking groups. Differences between other nicotine pathway metabolites are also shown but did not reach statistical significance.</p

Metabolites which (A) persistently increased and (B) persistently decreased with smoking.

<p>In panel A, these metabolites continued increasing even after smoking cessation. In panel B, these metabolites continued decreasing even after smoking cessation. Samples and data were analyzed as described in the methods section. ID Browser in-house database is comprised of Metlin, Lipid Maps and HMDB. PIP = phosphatidylinositol phosphate, PS = phosphatidylserine, TG = triglyceride, DG = diglyceride, PE = phosphatidylethanolamine. Fold change ≥1.5; <i>p</i>-value≤0.05, x-axis = Log2 normalized abundance scale; Error bars represent 95% confidence; Significance values *≤0.05, **≤0.01, ***≤0.001.</p

Heat map of differentially regulated amino acids and derivatives.

<p>Statistical analysis was performed using Mass Profiler Professional software (Agilent) with <i>p</i>-value cutoff ≤0.05. The mean abundance levels for each of the differentially regulated metabolites detected is shown. Blue represents a decrease in mean metabolite abundance, and yellow/beige represents an increase in mean metabolite abundance.</p

Quality Control using spiked-in standards for each fraction.

<p>Retention time % CVs are less than 1% and peak area % CVs are less than 10%. Aqueous fraction was analyzed using SB-C3 analytical column and neutral lipid and phospholipid fractions were analyzed using a C18 analytical column on an Agilent 6210 ESI-TOF. Data was analyzed using Agilent Mass Hunter Qualitative and Quantitative Analysis software.</p

Metabolites which were (A) reversibly decreased and (B) reversibly increased with smoking.

<p>In panel A (top), differentially regulated metabolites were higher in air controls, decreased with smoking, and increased toward air control levels following smoking cessation. In panel A (bottom), differentially regulated metabolites were higher in air controls, decreased with smoking, and surpassed air control levels following smoking cessation. In panel B (top) differentially regulated metabolites were lower in air controls, increased with smoking, and decreased following smoking cessation. In panel B (bottom), differentially regulated metabolites were lower in air controls, increased with smoking, and decreased beyond air control levels following smoking cessation. Samples and data were analyzed as described in the methods section. TG = triglyceride, CDP = cytidine-diphosphate, DG = diglyceride, PC = phosphatidylcholine, PS = phosphatidylserine, TG = triglyceride, PA = phosphatidic acid. Fold change ≥1.5; <i>p</i>-value≤0.05, x-axis = Log2 normalized abundance scale; Error bars represent 95% confidence; Significance values *≤0.05, **≤0.01, ***≤0.001.</p

Heat map of (A) purines and (B) purine pathway hits.

<p>The mean abundance levels for each of the differentially regulated metabolites detected is shown in the heat map in A. Blue represents a decrease in mean metabolite abundance, and yellow/beige represents an increase in mean metabolite abundance. In panel B, the compounds with indicated abundance levels represent the differentially expressed metabolites identified in the purine metabolism pathway in the current study. Statistical analysis was performed using Mass Profiler Professional software (Agilent) with <i>p</i>-value cutoff ≤0.05, NS indicates not significant, ND indicates no data is available, and error bars represent 95% confidence.</p

Time course of expression of genes expressed at higher levels in <i>C. albicans</i>-stimulated cPLA₂α^+/+ than cPLA₂α^-/- RPM.

<p>cPLA₂α^+/+ (WT, circles) and cPLA₂α^-/- (KO, triangles) RPM were incubated with (CA) or without (US) <i>C. albicans</i> for the indicated times. RNA was isolated and gene expression determined by real-time PCR using the RT² Profiler PCR Array System (SA Bioscience) as described in Experimental Design. The data were normalized to the housekeeping genes <i>Gapdh</i> and <i>Hprt</i>. The results are the average of 3 experiments ±S.E. Gene expression in <i>C. albicans</i> infected WT at 3 h was compared to <i>C. albicans</i> infected KO at 3 h to determine significance (*p<0.05).</p