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.

LPS produced by Gram-negative bacteria effectively stimulates the maturation of BMDCs. Previous studies have shown that DClps might induce tolerance in autoimmune diseases and cancer in vivo, whereas it remains unclear whether DClps can modulate the immune microenvironment in allergic asthma. We sought to elucidate the potential effects of DClps on OVA-sensitized/challenged airway inflammation in a mouse model of asthma, which may help facilitate the application of specific tolDCs in allergic asthma patients in the future. We generated and obtained DClps from wild-type mice to evaluate their functional characteristics by ELISA and FACS. We also induced OVA-sensitized/challenged asthmatic mice and intraperitoneally treated these mice with DClps to assess the effects of these injected cells by histopathologic analysis and performing inflammatory cell counts in BALF. Changes in memory CD4+ T cells, Tregs and phosphorylated protein in lung digests were analyzed. DClps exhibited lower levels of CD80 and MHCII and increased levels of anti-inflammatory cytokines such as IL-10 and TGF-β than DCia. Additionally, DClps treatment dramatically ameliorated airway inflammation and diminished the infiltration of pulmonary inflammatory cells. In addition, we prolonged the modeling time of asthmatic mice and demonstrated that DClps treatment decreased the proliferation activity of pulmonary memory CD4+ T cells, which further rendered the downregulation of Th2 cytokines. However, the number of pulmonary Tregs did not discernibly change. DClps treatment also markedly reduced the phosphorylation level of STAT6 protein.