National Heart and Lung Institute, Imperial College London, London, UK.
Data Science Institute, Imperial College London, London, UK.
Clin Transl Med. 2024 Jul;14(7):e1771. doi: 10.1002/ctm2.1771.
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.
To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma.
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.
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.
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.
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.
使用单一组学平台的聚类方法越来越多地用于描述嗜酸性粒细胞和中性粒细胞性哮喘的分子表型。多组学平台的有效整合应能在分子维度上更精细地细分哮喘表型，并指出干预或生物标志物开发的关键靶点。
确定痰的多组学整合是否能提高严重哮喘分子分类的粒度。
我们分析了 U-BIOPRED 欧洲队列中 57 例严重哮喘患者、15 例轻中度哮喘患者和 13 例健康志愿者的诱导痰样本中的 6 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、鸟枪法蛋白质组学、16S 微生物组测序和鸟枪法宏基因组测序。我们使用 Monti 共识聚类算法对聚类结果进行聚合，并使用相似网络融合来整合 72 例哮喘患者的 6 个多组学数据集。
确定了 5 个稳定的与组学相关的聚类（OAC）。OAC1 具有最佳的肺功能，严重哮喘患者中痰液少粒细胞性炎症的数量最少。OAC5 也有较少的严重哮喘患者，但特应性和过敏性鼻炎的发生率最高，伴有少粒细胞性炎症。OAC3 仅由严重哮喘患者组成，痰液中嗜酸性粒细胞最多。OAC2 痰液中嗜中性粒细胞最多，其次是 OAC4，这两个聚类都主要由严重哮喘患者组成，但 OAC4 中更多的是现/曾经吸烟者。与 OAC4 相比，OAC2 中鼻息肉、过敏性鼻炎和湿疹的发生率更高。OAC2 存在微生物失调，富含卡他莫拉菌和流感嗜血杆菌。OAC4 与与 IL-22 细胞因子激活相关的途径有关，预测对抗 IL-22 抗体治疗有反应。
对哮喘痰的多组学分析以更高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表型。对各种类型的高维相互作用进行建模将有助于更全面地了解复杂的表型。
对哮喘受试者的痰进行无监督聚类分析，确定了 5 个聚类中的 3 个主要为严重哮喘。一个严重哮喘聚类与 2 型炎症和痰嗜酸性粒细胞增多有关，而另 2 个聚类与痰中性粒细胞增多有关。一个严重的中性粒细胞性哮喘聚类与卡他莫拉菌有关，与流感嗜血杆菌的关系较小，而第二个聚类与 IL-22 的激活有关。
