通过气道蛋白质组学特征对哮喘表型进行分层
2019/04/19
摘要
背景：嗜酸性粒细胞和中性粒细胞计数的分层增加了我们对哮喘的理解并有助于靶向治疗，但我们在预测治疗反应的准确性和更好地理解潜在机制的需要方面仍存在改进的空间。我们的目的是鉴定由蛋白质组学特征定义的哮喘的分子亚型，以改善分层。
方法：使用无偏无标记定量质谱和拓扑数据分析方法分析来自246名参与者（206名哮喘患者）的痰上清液的蛋白质组学，作为哮喘分层的新方法。痰细胞的微阵列分析提供了转录组学数据，另外提供了基础机制的信息。
结果：基于蛋白质组学特征的相似性，分析痰蛋白质组学导致了10个簇，蛋白质类型，代表了哮喘的离散分子亚型。将粒细胞计数覆盖在10个簇上，因为元数据进一步将其中三个定义为高度嗜酸性粒细胞，三个为高度中性粒细胞，两个为具有相对低粒细胞炎症的高度特应性。对于这三种表型中的每一种，逻辑回归分析鉴定了候选蛋白质生物标志物，并且匹配的转录组学数据指向差异激活的潜在机制。
结论：该研究提供了目前通过量化粒细胞炎症进行分类的哮喘的进一步分层，并进一步了解其可能成为新疗法目标的潜在机制。
Stratification of asthma phenotypes by airway proteomic signatures.
Schofield JPR, Burg D, Nicholas B, Strazzeri F, Brandsma J, Staykova D, Folisi C, Bansal AT, Xian Y, Guo Y, Rowe A, Corfield J, Wilson S, Ward J, Lutter R, Shaw DE, Bakke PS, Caruso M, Dahlen SE, Fowler SJ, Horváth I, Howarth P, Krug N, Montuschi P, Sanak M, Sandström T, Sun K, Pandis I, Riley J, Auffray C, De Meulder B, Lefaudeux D, Sousa AR, Adcock IM, Chung KF, Sterk PJ, Skipp PJ, Djukanović R; U-BIOPRED Study Group.
Abstract
BACKGROUND:Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy to predict treatment responses and a need for better understanding of the underlying mechanisms. Identify molecular sub-phenotypes of asthma defined by proteomic signatures for improved stratification.
METHODS:Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyse the proteomes of sputum supernatants from 246 participants (206 asthmatics) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms.
RESULTS:Analysis of the sputum proteome resulted in 10 clusters, proteotypes, based on similarity in proteomics features, representing discrete molecular sub-phenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined three of these as highly eosinophilic, three as highly neutrophilic, and two as highly atopic with relatively low granulocytic inflammation. For each of these three phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms.
CONCLUSIONS:This study provides further stratification of asthma currently classified by quantifying granulocytic inflammation and gives additional insight into their underlying mechanisms which could become targets for novel therapies.
背景：嗜酸性粒细胞和中性粒细胞计数的分层增加了我们对哮喘的理解并有助于靶向治疗，但我们在预测治疗反应的准确性和更好地理解潜在机制的需要方面仍存在改进的空间。我们的目的是鉴定由蛋白质组学特征定义的哮喘的分子亚型，以改善分层。
方法：使用无偏无标记定量质谱和拓扑数据分析方法分析来自246名参与者（206名哮喘患者）的痰上清液的蛋白质组学，作为哮喘分层的新方法。痰细胞的微阵列分析提供了转录组学数据，另外提供了基础机制的信息。
结果：基于蛋白质组学特征的相似性，分析痰蛋白质组学导致了10个簇，蛋白质类型，代表了哮喘的离散分子亚型。将粒细胞计数覆盖在10个簇上，因为元数据进一步将其中三个定义为高度嗜酸性粒细胞，三个为高度中性粒细胞，两个为具有相对低粒细胞炎症的高度特应性。对于这三种表型中的每一种，逻辑回归分析鉴定了候选蛋白质生物标志物，并且匹配的转录组学数据指向差异激活的潜在机制。
结论：该研究提供了目前通过量化粒细胞炎症进行分类的哮喘的进一步分层，并进一步了解其可能成为新疗法目标的潜在机制。
(中日友好医院呼吸与危重症医学科 王瑞茵 摘译 林江涛 审校)
(J Allergy Clin Immunol. 2019 Mar 27. pii: S0091-6749(19)30415-4. doi: 10.1016/j.jaci.2019.03.013. [Epub ahead of print])
(J Allergy Clin Immunol. 2019 Mar 27. pii: S0091-6749(19)30415-4. doi: 10.1016/j.jaci.2019.03.013. [Epub ahead of print])
Stratification of asthma phenotypes by airway proteomic signatures.
Schofield JPR, Burg D, Nicholas B, Strazzeri F, Brandsma J, Staykova D, Folisi C, Bansal AT, Xian Y, Guo Y, Rowe A, Corfield J, Wilson S, Ward J, Lutter R, Shaw DE, Bakke PS, Caruso M, Dahlen SE, Fowler SJ, Horváth I, Howarth P, Krug N, Montuschi P, Sanak M, Sandström T, Sun K, Pandis I, Riley J, Auffray C, De Meulder B, Lefaudeux D, Sousa AR, Adcock IM, Chung KF, Sterk PJ, Skipp PJ, Djukanović R; U-BIOPRED Study Group.
Abstract
BACKGROUND:Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy to predict treatment responses and a need for better understanding of the underlying mechanisms. Identify molecular sub-phenotypes of asthma defined by proteomic signatures for improved stratification.
METHODS:Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyse the proteomes of sputum supernatants from 246 participants (206 asthmatics) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms.
RESULTS:Analysis of the sputum proteome resulted in 10 clusters, proteotypes, based on similarity in proteomics features, representing discrete molecular sub-phenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined three of these as highly eosinophilic, three as highly neutrophilic, and two as highly atopic with relatively low granulocytic inflammation. For each of these three phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms.
CONCLUSIONS:This study provides further stratification of asthma currently classified by quantifying granulocytic inflammation and gives additional insight into their underlying mechanisms which could become targets for novel therapies.
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气道嗜酸性粒细胞在哮喘气道重塑中的作用：MMP-10和MET的作用
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内皮Sox17促进过敏性气道炎症
