Identification of sputum metabolomic signatures in patients with different inflammatory asthma phenotypes using untargeted and targeted metabolomics

Baiqu Metabolomics Information: Is Asthma Really Related to Sputum? Let's ask the scientific researchers what they think
.

Let's follow Xiaoqu and learn about different inflammatory subtypes of asthma through "sputum metabolomics"
.

Recently, the team of Professor Wang Gang from West China Hospital of Sichuan University published a paper entitled "Sputum Metabolic Profiling Reveals Metabolic Pathways and Signatures Associated With Inflammatory Phenotypes in Patients With Asthma" on Allergy Asthma & Immunology Research (IF=5.
096)
.
Professor Wang Gang from West China Hospital of Sichuan University is the first corresponding author of the paper, and Dr.
Liu Ying is the first author of the paper; researcher Liu Zhipeng and researcher Wang Hongguang from the founding team of Shanghai Baiqu Metabolomics Technology Research Center are the co-authors of the paper
.
Baiqu provided untargeted metabolomics + targeted metabolomics services for this study
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Asthma is a severe global chronic inflammatory airway disease characterized by recurrent episodes of wheezing, shortness of breath, chest tightness, and coughing due to airway hyperresponsiveness and reversible airway obstruction
.
At the same time, asthma is also a heterogeneous disease with different phenotypes and endotypes, among which the inflammatory phenotypes of asthma (eosinophilic asthma [EA], neutrophilic asthma [NA] and Asthma [PGA]) is widely recognized according to the induction of different proportions of sputum granulocytes
.


Although management approaches can be tailored for patients with different inflammatory phenotypes, therapeutic strategies targeting specific inflammatory phenotypes, especially NA, are lacking, representing an unmet medical need for asthma
.
At the same time, the molecular mechanisms driving different inflammatory phenotypes in asthma are poorly understood and may be heterogeneous
.
Therefore, it is important to identify specific biomarkers and understand the molecular mechanisms underlying different inflammatory phenotypes in asthma, which may lead to more personalized medicine
.


This study used untargeted and targeted metabolomic approaches to identify sputum metabolomic signatures and underlying molecular pathways in patients with different inflammatory asthma phenotypes, with a view to exploring whether these signatures are associated with asthma control and exacerbation risk
.


1.
Sample setting
Discovery set (discovery set) 119 sputum samples:

Validation set (validation set) 114 sputum samples:

2.
Clinical characteristics
In the validation set, the results of inflammatory cytokines showed that among the asthma phenotypes, EA patients had the highest levels of IL-5, and the lowest levels of TNF-α, IL-1β, and IL-8 (p<0.
05)
.
In contrast, the NA group had the highest level of IL-1β and the lowest level of IL-5 (both P<0.
05) (Table 1)
.
At 12 months of follow-up, 3 (10%) NA patients and 3 (6.
1%) PGA patients were lost to follow-up
.
Finally, 108 asthmatic patients (EA, n=35; NA, n=27; PGA, n=46) were included in the asthma exacerbation analysis
.
The results showed that the proportion of patients with severe asthma attacks (EA vs NA vs PGA: 17.
1% vs 37% vs 6.
5%, P=0.
004) and the frequency of severe asthma attacks (EA vs NA vs PGA: 0.
29±0.
75) in different phenotype groups vs 0.
70±1.
33 vs 0.
09±0.
35, P=0.
004) was significantly different (Table 1)
.

Table 1.
Values ​​are expressed as mean ± standard deviation, number (%) or median (lower quartile, upper quartile)
.
EA, eosinophilic asthma; NA, neutrophilic asthma; PGA, oligomyelocytic asthma; BMI, body mass index; BDP, beclomethasone dipropionate; LTRA, leukotriene receptor antagonist; SPT , skin prick test; ACQ, asthma control questionnaire; AQLQ, asthma quality of life questionnaire; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; BDR, bronchodilator response; FeNO, fraction of exhaled nitric oxide; IgE , immunoglobulin E; TNF, tumor necrosis factor; interferon, interferon; IL, interleukin; MDC, macrophage-derived chemokine
.
Compared with EA group, aP<0.
01, bP<0.
05; compared with NA group, cP<0.
01, dP<0.
05
.


3.
Untargeted metabolic profiling can discriminate between different inflammatory asthma phenotypes in the discovery set and
OPLS-DA analysis in healthy subjects showed a clear separation between different asthma inflammatory phenotypes
.
In addition, the asthma group was also significantly separated from the healthy control group (Fig.
1)
.
The low value of the Q2 intercept in each model indicates the robustness of these models and thus shows a low risk of overfitting in all comparisons, with significant between-group differences
.

Figure 1.
Findings focused, untargeted metabolic profiles differ in asthma and healthy subjects with different inflammatory phenotypes
.
(AD) Score plot of PCA model of different inflammatory asthma phenotypes and healthy subjects; (EH) Score plot of OPLS-DA model of different inflammatory asthma phenotypes and healthy subjects; (IL) Score plot of different inflammatory asthma phenotypes Permutation test

combined with univariate and multivariate statistical analysis of OPLS-DA model with healthy subjects , based on VIP>1, fold change <0.
83 or >1.
2 and FDR<0.
05 of OPLS-DA model, in different asthma phenotypes A total of 77 differential metabolites were found between and healthy subjects (Fig.
2)
.

4.
Metabolomic pathway analysis in the discovery set The
MetPA metabolomic pathway topology enrichment analysis method was applied to assess the importance of pathways underlying pathophysiological mechanisms in different asthma phenotypes and healthy subjects (Fig.
3)
.
The results showed that the histidine metabolic pathway was different between the asthma group and the healthy control group (P=0.
019; impact value=0.
189)
.
Differential metabolite enrichment between EA and NA was in glycerophospholipid metabolism (P=0.
015; impact value=0.
121), niacin and nicotinamide metabolism (P=0.
020; impact value=0.
332) and histidine metabolism (P=0.
332).
0.
022; impact value = 0.
189)
.
Differential metabolite enrichment between EA and PGA was in glycerophospholipid metabolism (P=0.
001; impact value=0.
186), linoleic acid metabolism (P=0.
001; impact value=1.
000), histidine metabolism (P=0.
014; Impact value = 0.
189) and biosynthesis of phenylalanine, tyrosine and tryptophan (P = 0.
046; impact value = 0.
500)
.
The differential metabolite enrichment between NA and PGA was in linoleic acid metabolism (P=0.
002; impact value=1.
000), glycerophospholipid metabolism (P=0.
010; impact value=0.
160) and niacin and niacinamide metabolism (P= 0.
015; impact value = 0.
332)
.

Figure 3.
Metabolic pathway analysis based on metabolites that can differentiate between different inflammatory phenotypes in asthma and healthy subjects' sputum samples
.
(A) Asthma and health; (B) EA and NA; (C) EA and PGA; (D) NA and PGA
.
Circles indicate metabolic pathways that may be involved in class segregation
.
EA, eosinophilic asthma; NA, neutrophilic asthma; PGA, oligogranulocytic asthma
.


V.
Differential metabolites between different inflammatory asthma phenotypes and healthy subjects in the targeted quantitative
validation set Targeted quantification, in which 24 metabolites were significantly differentially expressed between asthma inflammatory phenotypes (Table 2), with significant P values ​​of 0.
001 or less for 9 metabolites
.
Histamine and histidine metabolism-related metabolites expressed the highest levels, while 5-L-glutamyl-L-alanine, nicotinamide, dihydrothymine, L-leucine, L-phenylalanine , Alanylleucine, Phenylalanylserine and Phenylalanylphenylalanine had the lowest expression in EA patients
.
These data are consistent with the results of metabolic pathway topological enrichment analysis, suggesting that histidine metabolism may play an important role in EA
.
In patients with NA, adenosine 5'-monophosphate, glyceric acid, taurine, dicarboxylic acid, glycerol, taurine, bisphosphonate, glycerol, taurine, taurine, taurine, and taurine are involved in purine metabolism, glyoxylate and dicarboxylic acid metabolism, taurine and hypotaurine metabolism, and aminoacyl tRNA biosynthesis.
Hydrothymine, L-leucine, tyramine, L-glutamate, alanylleucine, phenylalanylserine, and threonylphenylalanine had the highest concentrations, while glycerophospholipid metabolism-related metabolites Contrary to the lowest concentration of glycerophosphocholine, the highest concentration of glycerophosphocholine was found in PGA
.
Moreover, PGA patients also had the highest biosynthesis of unsaturated fatty acid-related metabolites, heptadecanoic acid and oleic acid, but the lowest concentration of dodecanoic acid (Table 2)
.

Table 2.
Values ​​expressed as medians (lower quartile, upper quartile)
.
EA, eosinophilic asthma; NA, neutrophilic asthma; PGA, oligocytic asthma
.
Compared with EA group, aP<0.
01, bP<0.
05; compared with NA group, cP<0.
01, dP<0.
05
.


Furthermore, these differentially expressed metabolites were detected by receiver operating characteristic (ROC) curves to distinguish different inflammatory asthma phenotypes
.
Taurine, alanyl leucine, phenylalanyl serine and threonyl phenylalanine can be used as candidate metabolic markers to distinguish NA from EA or PGA, with AUC ranging from 0.
816 to 0.
975 (P<0.
05) (Figure 4)
.
However, since the AUCs of the single metabolites were all less than 0.
7, EA and PGA could not be effectively distinguished
.

Figure 4.
Receiver operating characteristic (ROC) curves of metabolites that differentiate between NA and EA (A) or NA and PGA (B)
.
EA, eosinophilic asthma; NA, neutrophilic asthma; PGA, oligogranulocytic asthma
.


6.
Correlation of differential metabolites with clinical indicators and inflammatory signatures in the validation set
Correlation analysis showed that differentially expressed metabolites were associated with lung function, asthma control and inflammatory signatures in all asthma subjects (Figure 5)
.
At 12-month follow-up, logistic regression and negative binomial regression models were used to explore the association between sputum metabolites and severe asthma exacerbations in all asthmatics
.
The results showed that adenosine 5'-monophosphate was significantly associated with severe asthma exacerbations (Table 3)
.
After adjusting for age, sex, BMI, and FEV1%, adenosine 5'-monophosphate was positively associated with the proportion of patients experiencing severe asthma attacks and the frequency of severe asthma attacks (RRadj=1.
000, 95%CI=[1.
000,1.
000], P= 0.
050; RRadj=1.
000, 95%CI=[1.
000, 1.
000], P=0.
008)
.
Meanwhile, allantoin and nicotinamide were positively correlated with the frequency of severe asthma attacks (RRadj=1.
000, 95%CI=[1.
000, 1.
000], P=0.
043; RRadj=1.
001, 95%CI=[1.
000, 1.
002], P =0.
021)
.
In addition, subgroup analyses were performed for eosinophilic asthma (EA) or non-eosinophilic asthma (NEA)
.
The results showed that adenosine 5'-monophosphate was still significantly associated with severe asthma exacerbations in the NEA group, but not in the EA group
.
Furthermore, we also found that adenine, allocathionine, and nicotinamide were also significantly associated with severe asthma exacerbations in the NEA group
.
However, due to the limited sample size in the EA group, only tyramine was found to be associated with the frequency of severe asthma attacks
.

 Figure 5.
Heatmap of the correlation of differential metabolites with clinical and inflammatory features across all asthma subjects in the validation set
.
FEV1, forced expiratory volume in one second; FVC, forced vital capacity; ACQ, asthma control questionnaire; AQLQ, asthma quality of life questionnaire; FeNO, fraction of exhaled nitric oxide; IgE, immunoglobulin E; TNF, tumor necrosis factor; interference IL, interleukin; MDC, macrophage-derived chemokine
.

Table 3.
Adjusted for age, sex, BMI, and FEV1
.
OR, odds ratio; CI, confidence interval; BMI, body mass index; FEV1, forced expiratory volume in one second
.


VII.
Conclusions
The metabolomics analysis of different inflammatory asthma phenotypes shows that there are 77 significant metabolite changes and 5 significant metabolic pathway changes accumulatively among different subtypes
.
These metabolomic pathways are mainly histidine metabolism, glycerophospholipid metabolism, niacin and nicotinamide metabolism, linoleic acid metabolism, and biosynthesis of phenylalanine, tyrosine and tryptophan
.
In addition, differential metabolites were found to be associated with clinical and inflammatory features of asthma, and these metabolites may serve as potential therapeutic targets for different inflammatory asthma phenotypes
.

Article/Aqu Metabolomics