Urinary metabolomics reveals unique metabolic signatures in infants with cystic fibrosis

Abstract

Background: Biologic pathways and metabolic mechanisms underpinning early systemic disease in cystic fibrosis (CF) are poorly understood. The Baby Observational and Nutrition Study (BONUS) was a prospective multi-center study of infants with CF with a primary aim to examine the current state of nutrition in the first year of life. Its secondary aim was to prospectively explore concurrent nutritional, metabolic, respiratory, infectious, and inflammatory characteristics associated with early CF anthropometric measurements. We report here metabolomics differences within the urine of these infants as compared to infants without CF.

Methods: Urine metabolomics was performed for 85 infants with predefined clinical phenotypes at approximately one year of age enrolled in BONUS via Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Samples were stratified by disease status (non-CF controls (n = 22); CF (n = 63, All-CF)) and CF clinical phenotype: respiratory hospitalization (CF Resp, n = 22), low length (CF LL, n = 23), and low weight (CF LW, n = 15).

Results: Global urine metabolomics profiles in CF were heterogeneous, however there were distinct metabolic differences between the CF and non-CF groups. Top pathways altered in CF included tRNA charging and methionine degradation. ADCYAP1 and huntingtin were identified as predicted unique regulators of altered metabolic pathways in CF compared to non-CF. Infants with CF displayed alterations in metabolites associated with bile acid homeostasis, pentose sugars, and vitamins.

Conclusions: Predicted metabolic pathways and regulators were identified in CF infants compared to non-CF, but metabolic profiles were unable to discriminate between CF phenotypes. Targeted metabolomics provides an opportunity for further understanding of early CF disease.

Trial registration: United States ClinicalTrials.Gov registry NCT01424696 (clinicaltrials.gov).

Keywords: Biomarkers; CF; Metabolites.

Conflict of interest statement

Conflict of Interest statement: BI works for Metabolon, a precision metabolomics company

Copyright © 2018 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

Figures

Figure 1:. Global CF urine metabolomics profiles are heterogeneous, but unique compared to non-CF.

2-D Sparse Partial Least Squares discriminant analysis (sPLS-DA) of serum metabolomics profiles for patients with CF and non-CF controls. CF patients were grouped by low length but normal weight (CF LL, n = 23), low weight but normal length (CF LW, n = 15) and hospitalization for respiratory conditions with normal weight and length (CF Resp, n=25). Grouping are indicated by colors and shading. Metabolomics performed by UPLC-MS/MS. B) 3-D sPLS-DA with 4-component analysis for groupings in 1A.

Figure 2:. Variables selected by the sPLS-DA model for a given component.

Loadings plots for the ten metabolites selected for each of the 4 components chosen in Figure 1B sPLS-DA. Color plots demonstrate relative expression of individual metabolites within groups from high (dark red) to low (dark green). LL, LW, and Resp represent CF groups compared to non-CF controls.

Figure 3:. Targeted metabolite analysis differentiates CF phenotypes from non-CF.

Random forest biochemical importance plot of metabolites found to be important in discriminating between A) CF Resp versus non-CF, B) CF LL versus non-CF, and C) CF LW versus non-CF. The top 30 biochemicals are presented in order of increasing importance to group separation. Random Forest Confusion Matrixes of predicted classification for each comparison are also presented. Figure 3A predicted accuracy 87%, 3B predicted accuracy 91%, and 3C predicted accuracy 92%.

Figure 4:. Top regulator effect networks in CF.

Pathway analysis was performed on significantly altered metabolites between CF groups and non-CF. The top regulators and their effect networks are displayed for A) CF Resp and CF LL versus non-CF and B) CF LW versus non-CF. A color-coded prediction legend displays predicted activation/inhibition as well as intensity of measurement.

Figure 5:. Increased fat and non-fat soluble vitamin metabolites in CF urine.

A) Changes in non-fat soluble vitamin C pathway metabolites with corresponding box-plots for each metabolite within the displayed pathway. Individual metabolites are compared between CF groups and non- CF. B) Changes in fat-soluble Vitamin E pathway metabolites with corresponding box-plots for each metabolite within the displayed pathway. Individual metabolites are compared between CF groups and non-CF.

Figure 6:. CF has altered urinary pentose sugar metabolites.

Differences in the urinary pentose sugar metabolites A) xylose and B) fucose between the CF and non-CF groups. Metabolites differences displayed as box plots with log scaled intensity. B) Heatmap showing differences in urinary pentose sugar metabolites. Numerical values represent fold change in metabolites between group comparisons. A color legend indicates significance of p values for fold changes greater than 1.00 (red).