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Abstract：
BACKGROUND: Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development.
OBJECTIVE: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma.
METHODS: We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics.
RESULTS: Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy.
CONCLUSIONS: Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes.
CONCLUSIONS: Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22.
OBJECTIVE: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma.
METHODS: We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics.
RESULTS: Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy.
CONCLUSIONS: Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes.
CONCLUSIONS: Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22.
摘要：
背景：使用单一组学平台的聚类方法越来越多地用于表征嗜酸性粒细胞和嗜中性粒细胞哮喘的分子表型。多组学平台的有效整合应导致跨分子维度的哮喘内生型的更大细化，并指示干预或生物标志物开发的关键靶标。
目的：确定痰的多组学整合是否可以改善重症哮喘的分子分类粒度。
方法：我们分析了六个组学数据块-微阵列转录组学，微阵列转录组学的基因集变异分析，SomaSCAN蛋白质组学测定，鸟枪蛋白质组学，16S微生物组测序，和鸟枪宏基因组测序-从57例严重哮喘患者的诱导痰样本，15名轻度-中度哮喘患者，和欧洲U-BIOPRED队列中的13名健康志愿者。我们使用Monti共识聚类算法聚合聚类结果和相似性网络融合来整合72例哮喘患者的6个多组学数据集。
结果：确定了五个稳定的组学相关簇（OAC）。OAC1具有最佳的肺功能，而重症哮喘患者的痰液性粒细胞炎症最少。OAC5也有较少的严重哮喘患者，但特应性和过敏性鼻炎的发病率最高，伴有少粒粒细胞炎症。OAC3仅包括痰嗜酸性粒细胞最高的严重哮喘患者。OAC2的痰嗜中性粒细胞最高，其次是OAC4，这两个簇主要是严重的哮喘，但OAC4中有更多的前/当前吸烟者。与OAC4相比，鼻息肉的发病率更高，过敏性鼻炎,和OAC2中的湿疹。OAC2具有微生物菌群失调，具有丰富的卡他莫拉菌和流感嗜血杆菌。OAC4与IL-22细胞因子激活相关的途径，预测抗IL22抗体治疗的治疗反应。
结论：对哮喘患者痰液进行的多组学分析以更大的粒度定义了与嗜中性粒细胞和嗜酸性粒细胞炎症相关的哮喘内生型。模拟不同类型的高维相互作用将有助于更全面地理解复杂的内生型。
结论：哮喘受试者痰多组学的无监督聚类发现5个主要为重度哮喘的聚类中的3个。一个严重的哮喘集群与2型炎症和痰嗜酸性粒细胞增多有关，而其他两个集群与痰嗜中性粒细胞增多有关。一个严重的嗜中性粒细胞哮喘簇与卡他莫拉菌有关，在较小程度上与流感嗜血杆菌有关，而第二个簇与IL-22的激活有关。
目的：确定痰的多组学整合是否可以改善重症哮喘的分子分类粒度。
方法：我们分析了六个组学数据块-微阵列转录组学，微阵列转录组学的基因集变异分析，SomaSCAN蛋白质组学测定，鸟枪蛋白质组学，16S微生物组测序，和鸟枪宏基因组测序-从57例严重哮喘患者的诱导痰样本，15名轻度-中度哮喘患者，和欧洲U-BIOPRED队列中的13名健康志愿者。我们使用Monti共识聚类算法聚合聚类结果和相似性网络融合来整合72例哮喘患者的6个多组学数据集。
结果：确定了五个稳定的组学相关簇（OAC）。OAC1具有最佳的肺功能，而重症哮喘患者的痰液性粒细胞炎症最少。OAC5也有较少的严重哮喘患者，但特应性和过敏性鼻炎的发病率最高，伴有少粒粒细胞炎症。OAC3仅包括痰嗜酸性粒细胞最高的严重哮喘患者。OAC2的痰嗜中性粒细胞最高，其次是OAC4，这两个簇主要是严重的哮喘，但OAC4中有更多的前/当前吸烟者。与OAC4相比，鼻息肉的发病率更高，过敏性鼻炎,和OAC2中的湿疹。OAC2具有微生物菌群失调，具有丰富的卡他莫拉菌和流感嗜血杆菌。OAC4与IL-22细胞因子激活相关的途径，预测抗IL22抗体治疗的治疗反应。
结论：对哮喘患者痰液进行的多组学分析以更大的粒度定义了与嗜中性粒细胞和嗜酸性粒细胞炎症相关的哮喘内生型。模拟不同类型的高维相互作用将有助于更全面地理解复杂的内生型。
结论：哮喘受试者痰多组学的无监督聚类发现5个主要为重度哮喘的聚类中的3个。一个严重的哮喘集群与2型炎症和痰嗜酸性粒细胞增多有关，而其他两个集群与痰嗜中性粒细胞增多有关。一个严重的嗜中性粒细胞哮喘簇与卡他莫拉菌有关，在较小程度上与流感嗜血杆菌有关，而第二个簇与IL-22的激活有关。
