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. Author manuscript; available in PMC: 2021 Sep 1.
Published in final edited form as: J Cyst Fibros. 2020 May 30;19(5):791–800. doi: 10.1016/j.jcf.2020.05.003

Metabolomics profiling of tobacco exposure in children with cystic fibrosis

Benjamin L Wisniewski 1,2, Chandra L Shrestha 2, Shuzhong Zhang 2, Rohan Thompson 1, Myron Gross 3, Judith A Groner 4, Karan Uppal 5, Octavio Ramilo 6,7, Asuncion Mejias 6,7, Benjamin T Kopp 1,2
PMCID: PMC7492400  NIHMSID: NIHMS1599552  PMID: 32487493

Abstract

Background:

Inflammation is integral to early disease progression in children with CF. The effect of modifiable environmental factors on infection and inflammation in persons with CF is poorly understood. Our prior studies determined that secondhand smoke exposure (SHSe) is highly prevalent in young children with CF. SHSe is associated with increased inflammation, heightened bacterial burden, and worsened clinical outcomes. However, the specific metabolite and signaling pathways that regulate responses to SHSe in CF are relatively unknown.

Methods:

High-resolution metabolomics was performed on plasma samples from infants (n=25) and children (n=40) with CF compared to non-CF controls (n=15). CF groups were stratified according to infant or child age and SHSe status.

Results:

Global metabolomic profiles segregated by age and SHSe status. SHSe in CF was associated with changes in pathways related to steroid biosynthesis, fatty acid metabolism, cysteine metabolism, and oxidative stress. CF infants with SHSe demonstrated enrichment for altered metabolite localization to the small intestine, liver, and striatum. CF children with SHSe demonstrated metabolite enrichment for organs/tissues associated with oxidative stress including mitochondria, peroxisomes, and the endoplasmic reticulum. In a confirmatory analysis, SHSe was associated with changes in biomarkers of oxidative stress and cellular adhesion including MMP-9, MPO, and ICAM-1.

Conclusions:

SHSe in young children and infants with CF is associated with altered global metabolomics profiles and specific biochemical pathways, including enhanced oxidative stress. SHSe remains an important but understudied modifiable variable in early CF disease.

Keywords: Cystic fibrosis, Metabolites, Tobacco

INTRODUCTION

Inflammation is integral to early disease progression in children with CF.1–4 However, the effect of modifiable environmental factors on infection and inflammation is poorly understood. Our previous studies determined that secondhand smoke exposure (SHSe) is highly prevalent in young children with CF and associated with worse clinical outcomes.5,6 Furthermore, our objective measurement of hair nicotine indicates that SHSe disrupts arachidonic acid metabolism, which results in worsened respiratory and nutritional outcomes, increased inflammation, and heightened bacterial burden.7 The specific metabolite and signaling pathway changes that regulate responses to SHSe in CF remain relatively unknown.

Metabolomics is a growing field that has been utilized in prior CF studies to understand lung microbial microenvironments and to monitor treatment responses.8–26 Information garnered from metabolomics analyses may also be applied to the early detection and monitoring of disease progression due to new environmental exposures (e.g., SHSe). Metabolomics has not been previously studied in young children with CF and SHSe.

In this study we aimed to define the metabolome associated with SHSe in young children with CF to enhance our understanding of how SHSe modulates infection, inflammation, and respiratory health. Our central hypothesis was that young children with CF and SHSe have altered metabolic pathways compared to children without SHSe.

METHODS

Subjects

Children 3 months to <10 years with CF were recruited from outpatient CF clinics at baseline health and stratified according to age (infants <1 year, and children 1-10 years). The diagnosis of CF was defined by two disease-causing mutations or a sweat chloride test ≥ 60 mmol/L. A parent or guardian of the child participant provided informed consent on their behalf. Healthy non-CF age-matched children were recruited from primary care clinics as control subjects. For each participant, plasma was collected during a state of baseline non-fasting health as infants would not be able to fast for collection. Plasma was immediately flash frozen at −80°C for metabolite preservation. Most subjects also had serum collected and frozen for biologic studies of oxidative stress. The study was approved by the Institutional Review Board at Nationwide Children’s Hospital (IRB #12-00084).

Study design

Children with CF were part of a larger published study, (Determining Infectious Signatures in CF, DISC) which was designed to determine how SHSe influences infection, inflammation, and clinical outcomes.7,27 DISC was an observational cohort with previous design methods described.7 For this metabolomics sub-analysis, CF children comprised 65 of the original 77 CF cohort members with available plasma for analysis. Non-CF controls included 15 of the original 20 cohort controls. Of the 80 CF and non-CF children included in the plasma metabolomics analysis, 52 had a serum sample available for studies to determine the biologic significance (oxidative stress and cellular adhesion) of metabolomics findings (10 non-CF controls, 19 CF children and infants with SHSe, and 23 CF children and infants without SHSe).

Secondhand smoke exposure (SHSe)

Hair nicotine concentrations were determined as an objective measure of SHSe as previously reported.7 Hair nicotine provides a long-term, stable measure of SHSe as nicotine is integrated into the growing hair shaft over multiple months.28 Samples were processed by reverse-phase high-performance liquid chromatography with electrochemical detection as described.28 Lower limits of detection (0.1 ng/mg) may poorly distinguish SHSe from no SHSe,29 therefore we selected a more conservative cutoff (1.0ng/mg) to dichotomize groups into (+) and (−) SHSe.

High-resolution metabolomics

We sought to identify biochemical differences in the plasma of infants (n=25) and children (n=40) with CF compared to 15 non-CF controls. CF groups were stratified by the presence or absence of SHSe, as well as by infant or child age.

High-resolution metabolomics (HRM) profiling was completed using standardized methods.30–31 Samples were received in frozen aliquots (~250 μL). Prior to analysis, plasma aliquots were removed from storage at −80°C, thawed on ice, and prepared for HRM analysis. NIST SRM 195032 samples were analyzed at the beginning and end of the entire analytical run. Additional pooled human plasma samples were analyzed at the beginning, middle, and end of each batch of 40 samples for quality control, normalization, and batch effect evaluation.31,33,34 Each cryotube was vortexed briefly to ensure homogeneity, and 50 μLs were transferred to a clean microfuge tube. The plasma was immediately treated with 100 μL of ice-cold LC-MS grade acetonitrile (Sigma Aldrich) containing 2.5 μL of internal standard solution with eight stable isotopic chemicals selected to cover a range of chemical properties. Following the addition of acetonitrile, plasma was equilibrated for 30 min on ice and precipitated proteins were removed by centrifuge (14,000 rpm at 4°C for 10 min). The resulting supernatant (100 μL) was removed and added to a low volume autosampler vial and maintained at 4°C until analysis (<22 h).

Sample extracts were analyzed using liquid chromatography and Fourier transform high-resolution mass spectrometry (Dionex Ultimate 3000, Q-Exactive HF, Thermo Scientific). The chromatography system was operated in a dual pump configuration that enabled parallel analyte separation and column flushing. For each sample, 10 μL aliquots were analyzed in triplicate using hydrophilic interaction liquid chromatography (HILIC) with electrospray ionization (ESI) source operated in positive mode for metabolomics profiling and reverse phase chromatography with ESI operated in negative mode. During the HILIC separation method, 10 μL of sample is injected onto the HILIC column while the reverse phase column is flushing with wash solution. Flow rate is maintained at 0.35 mL/min for 1.5 min, increased to 0.4 mL/min at 4 min and then held for 1 min. Solvent A is 100% LC-MS grade water, solvent B is 100% LC-MS grade acetonitrile and solvent C is 2% formic acid (v/v) in LC-MS grade water. Initial mobile phase conditions are 22.5% A, 75% B, 2.5% C hold for 1.5 min, with linear gradient to 77.5% A, 20% B, 2.5% C at 4 min, hold for 1 min, resulting in a total analytical run time of 5 min. During the flushing phase (reverse phase analytical separation), the HILIC column is equilibrated with a wash solution of 77.5% A, 20% B, 2.5% C. The C18 column is operated parallel to the HILIC column for simultaneous analytical separation and column flushing through use of a dual head HPLC pump equipped with 10-port and 6-port switching valves. During operation of the C18 method, the MS is operated in negative ion mode and 10 AZA½L of sample is injected onto the C18 column while the HILIC column is flushing with wash solution. Flow rate is maintained at 0.4 mL/min until 1.5 min, increased to 0.5mL/min at 2 min and held for 3 min. Solvent A is 100% LC-MS grade water, solvent B is 100% LC-MS grade acetonitrile and solvent C is 10mM ammonium acetate in LC-MS grade water. Initial mobile phase conditions are 60% A, 35% B, 5% C hold for 0.5 min, with linear gradient to 0% A, 95% B, 5% C at 1.5 min, hold for 3.5 min, resulting in a total analytical run time of 5 min. During the flushing phase (HILIC analytical separation), the C18 column is equilibrated with a wash solution of 0% A, 95% B, 5% C until 2.5 min, followed by an equilibration solution of 60% A, 35% B, 5% C for 2.5 min.

Raw data files were then extracted using apLCMS35 with modifications by xMSanalyzer.36 Uniquely detected ions consisted of m/z, retention time and ion abundance, referred to as m/z features. Prior to data analysis, m/z features were batch corrected using ComBat37 and filtered to remove those with coefficient of variation (CV) ≥ 100% and > 10% non-detected values.

Oxidative stress and cell adhesion markers

Serum matrix metalloproteinase (MMP)-9, myeloperoxidase (MPO), intercellular adhesion molecule (ICAM)-1, and vascular adhesion molecule (VCAM)-1 were measured simultaneously with a custom Protein Simple SimplePlex 4-Plex assay, on the Protein Simple Ella platform. Assay parameters are described below.

Analyte LLOQ Control %CV
MMP-9 4,000 pg/mL 3.2 at 46,995 pg/mL; 4.5 at 937.3 pg/mL; 4.9 at 26,871 pg/mL
MPO 314 pg/mL 5.9 at 7,787 pg/mL; 4.0 at 147.7 pg/mL; 3.1 at 18,239 pg/mL
ICAM-1 410 pg/mL 6.3 at 4,654 pg/mL; 4.7 at 87.35 pg/mL; 4.8 at 376,016 pg/mL
VCAM-1 13,700 pg/mL 5.7 at 23,818 pg/mL; 8.9 at 467.3 pg/mL; 7.5 at 403,262 pg/mL
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E-selectin was measured by ELISA (R&D Systems, Item No. DSLE00), on a Beckman Coulter PARADIGM Detection Platform. The lower limit of quantitation (LLOQ) was 0.5 ng/mL, with control CVs ranging from 5.1% at 43.85 ng/mL to 10.3% at 0.71 ng/mL.

Statistical analysis

Demographic characteristics were compared using one-way ANOVA or unpaired t-test between infants and children. Oxidative stress and cellular adhesion marker significance was determined with one-way ANOVA. For metabolomics data, Sparse Partial Least Squares discriminant analysis (sPLS-DA) was performed with standard methodology including leave one out cross-validation (LOOCV). Standard statistical analyses were performed in MetaboAnalyst (4.0) on log-transformed data using the R (http://cran.r-project.org/) program. Random forest analysis was performed as reported.38 Hierarchical clustering was performed with the average clustering algorithm and a Pearson’s distance measure. Pathway analysis was performed using Ingenuity Pathway Analysis software (IPA) and MetaboAnalyst (4.0). Pathway topology analysis utilized the node importance measure of relative betweenness centrality with mapping against −log(p) and a false discovery rate < 0.05.39 Metabolite Set Enrichment Analysis (MESA) was performed with two libraries, the 1) metabolic pathway-associated metabolite sets (99 metabolite sets based on normal human metabolic pathways) and the 2) metabolite sets based on locations (73 metabolite sets based on organ, tissue, and subcellular localizations). Pathway enrichment analysis utilized the package globaltest for human pathways.40 For all analyses two sided p<0.05 was considered significant.

RESULTS

Demographics

The study cohort was derived from a recently described cohort of CF and non-CF infants and children who had a quantifiable measure of SHSe using hair nicotine as a surrogate.41 The demographic profiles of the 80 participants with samples available for this study are listed in Table 1. Importantly, no CF patients were on CFTR modulators at the time of sample acquisition. CF groups were similar in respect to genotypes and SHSe status. Approximately 52% of both groups had SHSe as denoted by elevated hair nicotine concentrations. Of the non-CF controls, 6 were infants and 9 were children. Demographics for the CF cohort based upon SHSe are presented in Supplemental Table 1. There were no significant differences in CF children and infants with or without SHSe, including medications or markers of low socioeconomic status (e.g. insurance).

Table 1:

Cohort Demographics

Non-CF (n=15) CF infants (n=25) CF children (n=40) P value
Age (years) 2.8 ± 1.7 0.4 ± 0.07 4.7 ± 2.9 0.004
Sex (% female) 40.0% 40.0% 55.0% 0.42
Genotype
 Phe508del homozygous N/A 52.0% 57.5% 0.67
 Phe508del heterozygous N/A 44.0% 32.5% 0.36
Secondhand smoke exposure 0% 52.0% 52.5% 0.0007
Systemic Medications
 Pancreatic Enzymes 0% 96.0% 95.0% 0.85
 Fat-soluble vitamin (ADEK) 0% 100% 100% N/A
 Ursodiol 0% 0% 15.0% 0.04
 Enteral antibiotics 0% 8.0% 15.0% 0.41
 Antihistamines 0% 0% 20.0% 0.02
Insurance status
 Public N/A 60.0% 55.0% 0.70
 Private N/A 40.0% 45.0% 0.70
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P-value determined by one-way ANOVA except for genotype, medications, and insurance status comparison by t-test.

Metabolomics reveals alterations in global profiles

Metabolite values were log-transformed and overall profiles were compared between three groups: 1) non-CF, 2) CF without SHSe, and 3) CF with SHSe. We used sPLS-DA to determine discriminative differences in metabolite profiles classified by the defined groups. There was moderate discrimination between all groups when visualized by 3D sPLS-DA (Fig. 1A). Due to some overlap within overall CF profiles, we further analyzed the CF samples by age, with comparison of CF by infant or child stratified upon SHSe status. This analysis showed distinct separation of profiles for both SHSe and no-SHSe CF patients based on age (Fig. 1B). Further analysis via sPLS-DA of only infants with CF (Fig. 1C) or children with CF (Fig. 1D) showed distinct profiles that clustered on the presence or absence of SHSe. Child and infant sPLS-DA models were associated with 35% classification error rates by LOOCV. All metabolite expression values are displayed in supplemental table 2, with comparison of metabolite expression ratios classified by age, gender, CF status, and the presence or absence of SHSe.

Figure 1: CF global metabolomics profiles are altered by SHSe.

Figure 1:

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A) Synchronized 3D sPLS-DA plots of overall plasma metabolomics profiles. Red dots represent individual non-CF controls, green dots represent CF patients without SHSe, and blue dots represent CF patients with SHSe. B) Synchronized 3D sPLS-DA plots of overall CF plasma metabolomics profiles stratified by age and SHSe. Red dots represent individual CF children without SHSe, dark blue dots represent CF children with SHSe, green dots represent CF infants without SHSe, and light blue dots represent CF infants with SHSe. The top 3 components are presented on the X, Y and Z axes for comparisons in 1A and 1B. C) 2D sPLS-DA plot of overall metabolomics profiles for comparison of CF infants with and without SHSe. CF infants with SHSe are shown in green, and CF infants without SHSe are shown in red. D) 2D sPLS-DA plot of overall metabolomics profiles for comparison of CF children with and without SHSe. CF children with SHSe are shown in green, and CF children without SHSe are shown in red. The top 2 components are presented on the X and Y axes for comparisons in 1C and 1D.

We then generated loadings plots for the metabolites used to generate the overall discriminative profiles for Figures 1C and 1D. Loadings plots for the 15 metabolites used for the top component for CF infants (Fig. 2A) and CF children (Fig. 2B) are shown. These metabolites were similar to the top metabolite variables of importance determined in a separate validation via random forest analysis of CF infants (Supplemental Fig. 1A) and CF children (Supplemental Fig. 1B). Surprisingly, there was limited overlap in the loading plots or variables of importance between the infant and child groups, with distinct profiles demonstrated for each group comparison according to SHSe. While palmitic acid and 4-pyridoxic acid were the top metabolites identified in CF infants with SHSe, 3-sulfinoalanine and 2,4-dihydroxyacetophenone were the top altered metabolites in CF children with SHSe. A list of all metabolites for the 5-component model for CF infants (supplemental table 3) and CF children (supplemental table 4) are shown.

Figure 2: Metabolite profiles differentiate CF infants and children with SHSe.

Figure 2:

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A) Loadings plots of the top 15 metabolites for the top component in the sPLS-DA analysis in Figure 1C comparing CF infants with and without SHSe. Metabolites that are over-expressed are shown in red, and under-expressed metabolites are shown in green. Metabolites are plotted by their increasing importance to separation of profiles from top to bottom. B) Loadings plots of the top 15 metabolites for the top component in the sPLS-DA analysis in Figure 1D comparing CF children with and without SHSe. Metabolites that are over-expressed are shown in red, and under-expressed metabolites are shown in green. Metabolites are plotted by their increasing importance to separation of profiles from top to bottom.

Socioeconomic status and SHSe were recently shown to be independent risk factors for pulmonary decline in CF.42 Therefore, we separately analyzed the CF children metabolite profiles by sPLS-DA for insurance type as a measure of socioeconomic status. Children with CF and SHSe showed separation of metabolite profiles by insurance type (Supplemental Figs. 2A, B), but there was a high error rate associated with this model (47.5%). There was also limited separation when profiles were segregated based upon insurance type only, independent of SHSe (Supplemental Figs. 2C, D, 32.5% error rate). Metabolite profiles associated with insurance type were unique compared to those associated with SHSe.

Metabolomics profiles cluster upon SHSe

Next, we generated heat maps of the top fifteen differentially expressed metabolites within CF groups by age (infants and children) using hierarchical clustering. Clustering algorithms separated infants by SHSe status (Fig. 3A). CF infants with SHSe demonstrated suppression of several metabolites including linoleic acid, palmitic acid, and myristic acid (Fig. 3A). Infants with CF and SHSe also demonstrated elevations in several other metabolites compared to the non-SHSe group (Fig. 3A). Interestingly, children with CF and SHSe had changes in a completely different set of top metabolites when compared to children with CF and no SHSe (Fig. 3B). SHSe in CF children was associated with decreased expression of several metabolites including arachidic acid, hypotaurine, and 5-hydroxyindoleacetate (Fig. 3B).

Figure 3: Metabolomics profiles cluster upon SHSe.

Figure 3:

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A) Heat map of differentially expressed metabolites for CF infants with and without SHSe. Each vertical column represents an individual with grouping by hierarchical clustering using the average clustering algorithm with a pearson distance measure. CF infants with SHSe are in green and CF infants without SHSe are in red. The top 15 metabolites are displayed in the horizontal columns with differential expression shown in the color-coded legend. B) Heat map of differentially expressed metabolites for CF children with and without SHSe. Each vertical column represents an individual with grouping by hierarchical clustering using the average clustering algorithm with a Pearson distance measure. CF children with SHSe are in green and CF children without SHSe are in red. The top 15 metabolites are displayed in the horizontal columns with differential expression shown in the color-coded legend.

Pathway analysis reveals alterations associated with SHSe

We then sought to identify biologically meaningful patterns in the metabolomics data. We used the MetaboAnalyst pathway analysis module with a pathway topology feature to identify relevant biological pathways impacted by SHSe in CF infants and children. This analysis provides visualization of pathways into nodes with increased importance based upon node centrality to allow visualization of pathways with the greatest impact. CF infants with SHSe demonstrated multiple large nodes of altered metabolic pathways compared to CF infants without SHSe including linoleic acid metabolism, alanine/aspartate/glutamine metabolism, and taurine/hypotaurine metabolism (Fig. 4A). CF children with SHSe demonstrated similar top nodes of the aforementioned altered metabolic pathways in CF infants, but also unique nodes such as phenylalanine metabolism (Fig. 4B).

Figure 4: Pathway analysis reveals alterations in metabolomics profiles by SHSe.

Figure 4:

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A) Pathway enrichment analysis (GlobalTest) combined with pathway topology analysis (degree centrality and betweenness centrality) for CF infants with SHSe compared to CF infants without SHSe. Metabolites are clustered in nodes with pathway impact on the X axis calculated by pathway topology analysis and plotted according to −log (p) values on the Y axis. CF infants with SHSe demonstrated multiple large nodes of altered metabolic pathways compared to CF infants without SHSe. B) Pathway enrichment analysis (GlobalTest) combined with pathway topology analysis (degree centrality and betweenness centrality) for CF children with SHSe compared to CF children without SHSe. Metabolites are clustered in nodes with pathway impact on the X axis calculated by pathway topology analysis and plotted according to −log (p) values on the Y axis. CF children with SHSe demonstrated multiple large nodes of altered metabolic pathways compared to CF children without SHSe.

We complemented this pathway analysis with MESA (using a set of 99 metabolite libraries) to identify pathways that were significantly enriched in the quantitative metabolomics dataset. MESA analysis demonstrated unique differences between enriched metabolic pathways based on age and SHSe status. CF infants with SHSe had alterations in steroid biosynthesis, fatty acid metabolism, and several pathways related to oxidative stress compared to infants without SHSe (Fig. 5A). CF children with SHSe had enrichment in several pathways found in CF infants with SHSe including steroidogenesis and oxidative stress-related pathways, but also demonstrated enrichment in unique pathways including taurine/hypotaurine and cysteine metabolism (Fig. 5B).

Figure 5: Enriched metabolite profiles differ by SHSe status.

Figure 5:

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A) Metabolite Set Enrichment Analysis (MESA) of the top 30 pathways generated utilizing 99 metabolic pathway-associated metabolite reference sets for CF infants with SHSe compared to CF infants without SHSe. Quantitative enrichment analysis performed using GlobalTest. Pathways are plotted by fold enrichment in descending order with a color-coded p value legend. B) MESA of the top 30 pathways generated utilizing 99 metabolic pathway-associated metabolite reference sets for CF children with SHSe compared to CF children without SHSe. Quantitative enrichment analysis performed using GlobalTest. Pathways are plotted by fold enrichment in descending order with a color-coded p value legend.

Pathway analysis was also performed with the MESA location module based upon organ, tissue, and subcellular locations of altered metabolites. CF infants with SHSe demonstrated enrichment in altered metabolite localization to the small intestine, liver, and striatum among several other organs or tissues (Supplemental Fig. 3A). CF children with SHSe demonstrated a different set of top metabolites enriched by location including the hypothalamus, spinal cord, and gonads (Supplemental Fig. 3B). CF children with SHSe also had increased enrichment for organelles and tissues associated with oxidative stress including mitochondria, peroxisomes, and the endoplasmic reticulum.

We then performed global pathway analysis using IPA for all CF subjects with SHSe compared to those without SHSe. SHSe was associated with alterations in several global pathways and the top canonical pathways represented in Table 2. Carbohydrate metabolism, hematologic function, nervous system function, and cellular movements were among the top biological functions impacted by SHSe across all CF age groups.

Table 2:

Top Canonical Pathways for CF with SHSe

Pathway P-value Overlap
tRNA Charging 7.04E-06 23.3%
Phenylalanine degradation 1.00E-05 35.0%
Citrulline biosynthesis 6.31E-05 33.3%
Purine ribonucleoside degradation 8.00E-05 41.7%
Proline biosynthesis 1.26E-04 38.5%
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SHSe alters markers of oxidative stress and cellular adhesion

To verify the biologic significance of the metabolomics studies, we measured a panel of oxidative stress and cellular adhesions markers in separate serum samples from 52 members of the cohort with available blood. This confirmatory cohort included 10 samples from healthy non-CF controls, 19 samples from CF children and infants with SHSe, and 23 samples from CF children and infants without SHSe. These samples were analyzed based on the 3 original groups of non-CF, CF with SHSe, and CF without SHSe due to the smaller sample size. We found that concentrations of MMP-9 (Fig. 6A) and MPO (Fig. 6B) were increased in CF patients with SHSe compared to CF without SHSe and non-CF controls. We also found that E-selectin (Fig. 6C), ICAM-1 (Fig. 6D), and VCAM-1 (Fig. 6E) were increased in CF with SHSe compared to non-CF controls, although ICAM-1 and VCAM-1 also increased in CF without SHSe compared to non-CF. These findings indicate that CF alterations in oxidative stress and cellular adhesion are worsened by SHSe, which helps to corroborate the metabolomics pathway analysis findings.

Figure 6: SHSe alters markers of oxidative stress and cellular adhesion.

Figure 6:

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SimplePlex immunoassay concentrations for A) MMP-9, B) MPO, C) E-selectin, D) ICAM-1, and E) VCAM-1 stratified by non-CF (black circles), CF with SHSe (red squares), and CF without SHSe (grey triangles). P-values determined by one-way ANOVA. “*” = p-value < 0.05, “**” = p-value < 0.01, and “***” = p-value <0.001. Concentrations for each assay are listed on the y-axis.

DISCUSSION

Host and environmental factors influence the recurrent and progressive cycles of infection and inflammation that characterize disease progression in CF. SHSe in children with CF is highly prevalent and has been associated in a dose-dependent manner with worsened nutritional and respiratory clinical outcomes.5–7,43–47 Although SHSe is known to affect inflammatory gene expression and immune system regulation,7,44 the principle metabolites and signaling pathways affected by SHSe in early CF disease have yet to be fully established. In this study, we analyzed the global metabolomics changes in patients with early CF and SHSe. Age-dependent pathway alterations were identified in fatty acid metabolism, steroid biosynthesis, cysteine metabolism, and oxidative stress. An improved understanding of the metabolite and pathway changes that occur in CF following harmful environmental exposures may help inform clinical prevention efforts, identify biomarkers of environmental exposures, and lead to the development of novel pathway-targeted therapeutics to alter disease severity.

Metabolomics analysis of pediatric CF patients exposed to SHS showed differential effects based on age. Infants with CF and SHSe demonstrated alterations in amino acids, linoleic acid, and taurine-hypotaurine metabolism, which corresponded to alterations in steroid biosynthesis, fatty acid metabolism, and oxidative stress pathways when compared to infants without SHSe. Children with CF and SHSe had similarly impacted pathways to infants, but also additional changes in selected pathways such as cysteine metabolism. In our prior study of this cohort we demonstrated that SHSe disproportionally affects CF infants.7 Hair nicotine concentrations from CF infants and children inversely correlated with age and were associated with an overexpression of inflammatory pathways and increased aerobic and anaerobic bacteria in oropharyngeal cultures. Remarkably, the hair nicotine concentrations measured in CF infants with SHSe were higher than those usually observed in adult active cigarette smokers. Whether the different metabolic alterations found in this study are a result of different nicotine exposures, age, or other factors remains unclear. Future investigations are needed to analyze the influence of host metabolism upon immune responses during SHSe in persons with CF.

Infants with CF exposed to SHS also demonstrated alterations in fatty acid and lipid metabolism. It has been previously established that persons with CF have altered baseline fatty acid metabolism in CFTR-expressing tissues, and that these alterations correlate with CFTR-mutation severity.48–50 In this study, CF infants with SHSe demonstrated metabolite suppression of several plasma fatty acids including: palmitic acid, myristic acid, and linoleic acid. Linoleic acid is known to be integral in the biosynthesis of arachidonic acid and prostaglandins. In our previous study, we provided evidence that SHSe in CF leads to alterations in arachidonic acid metabolism, specifically decreased prostaglandin D2 (PGD2) signaling. Children with CF and SHSe demonstrated decreased expression of arachidonic acid-derived genes involved in prostaglandin synthesis, namely prostaglandin reductase 2 (PTGR2) and prostaglandin E synthase 3 (PTGES3). Decreased PGD2 concentrations were clinically associated with hospitalizations and poor weight gain as well as a non-significant trend toward lower lung function measurements.2 In this way, genetic variations in CFTR and CFTR-modifying genes may be responsible for influencing arachidonic acid metabolism and magnifying the negative consequences of SHSe in early CF disease.

Interestingly, our results also demonstrated enrichment of pathways involving the metabolism of cysteine as well as its metabolites (e.g. taurine and hypotaurine) in infants and children with CF and SHSe. Increased expression of cysteine metabolites was more pronounced in children compared to infants, which suggests greater accumulation of cysteine byproducts over time. Cysteamine, a metabolite precursor of hypotaurine, has been shown in several recent CF studies to decrease inflammatory mediators, antagonize biofilm formation, and improve autophagy and macrophage-mediated bacterial killing.51–56 The efficacy of cysteamine in CF was recently assessed in phase II trials as an adjunctive therapy during pulmonary exacerbations. Our data suggest that further investigation into the role of therapeutics addressing altered cysteine metabolism during SHSe may be warranted.

This study was limited by its sample size, single-center design, lack of a non-CF cohort with SHSe, and lack of a replication cohort which increases the potential for sampling, indication, and non-random technical biases. A small sample size restricts definitive associations that can be made regarding the adverse effects of SHSe on metabolites in pediatric populations with CF. However, there were nearly equivalent percentages of SHSe within and between the CF cohorts analyzed. As an initial study, these parallel rates of objectively measured exposure allow for profile comparisons, which may provide important preliminary age-dependent understandings of potential metabolite changes. A larger sample size would help confirm variations in SHSe responses in future studies. A non-CF with SHSe control group would also have allowed for a further delineation of the metabolomics changes that are a result of SHSe from those that are secondary to underlying CF disease alone. The mechanisms of SHSe pathogenesis and the resulting effects on CF metabolomics pathway disruption and disease progression are almost certainly multifactorial and cannot be explained by a cross-sectional analysis. Although no replication cohort was available, the use of confirmatory analyses increases the validity of the described metabolomics pathway results and builds upon prior related findings.

Nutritional status, pharmacotherapy, and socioeconomic status are also potential confounders that could affect CF metabolomic profiles. All subjects in this study were on high protein-high calorie diets in accordance with Cystic Fibrosis Foundation (CFF) guidelines. No subjects in this study received gastrostomy-tube supplementation. However, biological samples were collected in a non-fasting state due to the young age of the infant enrollees, which likely influences metabolite levels and particularly those related to carbohydrate and fatty acid metabolism. CF subgroups in this study reported equivalent or comparable use of common systemic pharmacotherapies including fat-soluble vitamins, pancreatic enzymes, and enteral antibiotics. Many children with SHSe also have low socioeconomic status, which could influence metabolite profiles independent of SHSe through food insecurity or other factors. In recent studies, SHSe and socioeconomic status have been shown to be independent variables associated with early age-dependent decreases in CF pediatric lung function.42,57 To account for this, we showed that the distribution of low socioeconomic status was similar in all our groups examined and socioeconomic status was associated with differential changes in metabolite patterns compared to SHSe.

Sample collection in this study was limited to a single collection point and longitudinal metabolomics were not performed, which would have facilitated an assessment of metabolite changes over time. We also did not distinguish based on a source of secondhand nicotine exposure as our study was conducted at a time of predominant combustible tobacco cigarette use. Contemporary sources of secondhand exposure, namely electronic cigarettes, may affect different metabolite pathways than traditional combustible-based forms of SHSe. No patients in this study were on CFTR-modulators at the time of sample collection, and as a result, the therapeutic effect of CFTR-modulators on metabolite pathways important in SHSe cannot be ascertained. Last, our initial sPLS-DA classifications were associated with mild over-fitting, which may lead to inaccuracy in overall metabolite profile classifications. However, the potential limitations of our study were balanced by objective measures of SHSe to accurately classify children, which accounts for biases in studies that use parental-reported SHSe.

In summary, SHSe alters metabolomics profiles in infants and young children with CF. SHSe is associated with a variety of biochemical pathway changes including enhanced oxidative stress. SHSe remains an important but understudied modifiable environmental variable in early CF disease that needs further study in large, longitudinal, multi-center studies.

Supplementary Material

1

Highlights.

  • Secondhand smoke exposure alters metabolomics profiles in young children with CF

  • Metabolomics profiles show defects in steroids, fatty acids and cysteine metabolism

  • Secondhand smoke exposure is associated with biomarkers of oxidative stress

  • Altered metabolites localize to different systemic organs based upon age

  • Hematologic, nervous system, and cellular movements were top impacted functions

ACKNOWLEDGEMENTS

Thank you to all our patients with CF and their families for their participation. This work was supported in part by a Nationwide Children’s Hospital intramural grant (BTK, RT, AM), an American Academy of Pediatrics Julius B. Richmond Center New Investigator Grant (BTK), NIEHS CHEAR pilot and feasibility grant (BTK), National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number 1U2CES02653, and the Cure CF Columbus Translational Core (C3TC) and Immune Core (C3IC). C3TC and C3IC are supported by the Division of Pediatric Pulmonary Medicine, the Biopathology Center Core, and the Data Collaboration Team at Nationwide Children’s Hospital. C3TC and C3IC grant support provided by The Ohio State University Center for Clinical and Translational Science (National Center for Advancing Translational Sciences, Grant UL1TR002733) and by the Cystic Fibrosis Foundation (Research Development Program, Grant MCCOY19RO). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Thank you to the Minnesota CHEAR Exposure Assessment Hub and the National Exposure Assessment Laboratory at Emory for assistance with sample analysis, and Karin Vevang and Dr. Lisa Patterson for CHEAR project coordination.

Support: This work was supported in part by a Nationwide Children’s Hospital intramural grant (BTK, RT, AM), an American Academy of Pediatrics Julius B. Richmond Center New Investigator Grant (BTK), NIEHS CHEAR pilot and feasibility grant, and CTSA grant UL1TR001070.

Footnotes

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CONFLICT OF INTEREST

The authors have no relevant conflicts of interest.

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