Cystic fibrosis cells compared to non-cystic fibrosis cells on the metabolome level

A  chemical similarity network diagram of a published metabolomics study. In this study, CF cells were compared against non-CF cells for metabolite levels. Each node is a metabolite and node size reflect the p-value. nodes without labels did not passed the p-value cut-off 0.05. Q- value could be mapped to node color but it did not had clear visible appearance. Fold change value and direction of ttest was not provided in the supplement section of the study. Also, they did not reported the metabolites with PubChem identifiers so I had to use other name to id conversion tools to get the CIDs. However, up to 30 metabolites could not be mapped to any identifiers even after manual efforts. 

Abstract can be easily tracked by cross-referencing the compound classes in the diagram. Impact of central energy metabolism, redox metabolism, some amino acids, purine is visible. No alterations in lipids is also visible. 

http://www.jbc.org/content/early/2010/07/30/jbc.M110.140806


METABOLOMIC PROFILING REVEALED BIOCHEMICAL PAHTWAYS AND BIOMARKERS ASSOCIATED WITH PATHOGENSIS IN CYSTIC FIBROSIS CELLS

Cystic fibrosis (CF) is a life-shortening disease caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. In order to gain understanding of the epithelial dysfunction associated with CF mutations and discover biomarkers for therapeutics development, untargeted metabolomic analysis was performed on primary human airway epithelial cell cultures from three separate cohorts of CF patients and non-CF subjects. Statistical analysis revealed a set of reproducible and significant metabolic differences between the CF and non-CF cells. Aside from changes that were consistent with known CF effects, such as diminished cellular regulation against oxidative stress and osmotic stress, new observations on the disease cellular metabolism were generated. In the CF cells, the levels of various purine nucleotides, which may function to regulate cellular responses via purinergic signaling, were significantly decreased. Furthermore, CF cells exhibited reduced glucose metabolism in glycolysis, pentose phosphate pathway, and sorbitol pathway, which may further exacerbate oxidative stress and limit the epithelial cell response to environmental pressure. Taken together, these findings reveal novel metabolic abnormalities associated with CF pathological process and identify a panel of potential biomarkers for therapeutic development using this model system.