Sputum metabolomic profiling revels metabolic pathways and signatures associated with inflammatory phenotypes in patients with asthma

Background: The molecular links between metabolism and inflammation that drive different inflammatory phenotypes in asthma are poorly understood. Objectives: To identify the metabolic signatures and underlying molecular pathways of different inflammatory asthma phenotypes. Method: In the discovery set (n=119), untargeted ultra-high performance liquid chromatography–mass spectrometry (UHPLC-MS) were applied to characterize the induced sputum metabolic profiles from asthmatic patients classified by different inflammatory phenotypes using orthogonal partial least-squares discriminant analysis (OPLS-DA) and pathway topology enrichment analysis. In the validation set (n = 114), differential metabolites were selected to perform targeted quantification. Correlations between targeted metabolites and clinical indexes in asthma patients were analyzed. Logistic and negative binomial regression models were established to assess the association between metabolites and severe asthma exacerbation. Results: 77 differential metabolites were identified in the discovery set. Pathway topology analysis uncovered that histidine metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, linoleic acid metabolism, phenylalanine, tyrosine and tryptophan biosynthesis were involved in the pathogenesis of different asthma phenotypes. In the validation set, 24 targeted quantification metabolites were significantly differentially expressed between asthma inflammatory phenotypes. Finally, adenosine 5’-monophosphate (RRadj = 1.000, 95%CI = [1.000, 1.000], P = 0.050), allantoin (RRadj = 1.000, 95%CI = [1.000, 1.000], P = 0.043) and nicotinamide (RRadj = 1.001, 95%CI = [1.000, 1.002], P = 0.021) were demonstrated to predict severe asthma exacerbation rate ratios. Conclusions: Different inflammatory asthma phenotypes have specific metabolic profiles in induced sputum. The potential metabolic signatures may serve as identification and therapeutic target in different inflammatory asthma phenotypes.

Telomere Maintenance Pathway Activity Analysis Enables Tissue- and Gene-Level Inferences

Telomere maintenance is one of the mechanisms ensuring indefinite divisions of cancer and stem cells. Good understanding of telomere maintenance mechanisms (TMM) is important for studying cancers and designing therapies. However, molecular factors triggering selective activation of either the telomerase dependent (TEL) or the alternative lengthening of telomeres (ALT) pathway are poorly understood. In addition, more accurate and easy-to-use methodologies are required for TMM phenotyping. In this study, we have performed literature based reconstruction of signaling pathways for the ALT and TEL TMMs. Gene expression data were used for computational assessment of TMM pathway activities and compared with experimental assays for TEL and ALT. Explicit consideration of pathway topology makes bioinformatics analysis more informative compared to computational methods based on simple summary measures of gene expression. Application to healthy human tissues showed high ALT and TEL pathway activities in testis, and identified genes and pathways that may trigger TMM activation. Our approach offers a novel option for systematic investigation of TMM activation patterns across cancers and healthy tissues for dissecting pathway-based molecular markers with diagnostic impact.

Telomere maintenance pathway activity analysis enables tissue- and gene-level inferences

ABSTRACTTelomere maintenance is one of the mechanisms ensuring indefinite divisions of cancer and stem cells. Good understanding of telomere maintenance mechanisms (TMM) is important for studying cancers and designing therapies. However, molecular factors triggering selective activation of either the telomerase dependent (TEL) or the alternative lengthening of telomeres (ALT) pathway are poorly understood. In addition, more accurate and easy-to-use methodologies are required for TMM phenotyping. In this study, we have performed literature based reconstruction of signaling pathways for the ALT and TEL TMMs. Gene expression data were used for computational assessment of TMM pathway activities and compared with experimental assays for TEL and ALT. Explicit consideration of pathway topology makes bioinformatics analysis more informative compared to computational methods based on simple summary measures of gene expression. Application to healthy human tissues showed high ALT and TEL pathway activities in testis, and identified genes and pathways that may trigger TMM activation. Our approach offers a novel option for systematic investigation of TMM activation patterns across cancers and healthy tissues for dissecting pathway-based molecular markers with diagnostic impact.