Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0.
Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0.
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DOI：
10.1186/s12931-020-01544-4
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发表时间：
2020-10-19
影响因子：
5.8
通讯作者：
Goldmann T
中科院分区：
文献类型：
--
作者：
Groth EE;Weber M;Bahmer T;Pedersen F;Kirsten A;Börnigen D;Rabe KF;Watz H;Ammerpohl O;Goldmann T
迄今为止，大多数涉及哮喘和COPD痰液高通量分析的研究都集中在识别疾病的转录组特征上。尚未进行痰液细胞的全基因组甲基化分析。在这种情况下，痰液的高度可变的细胞组成有可能混淆分子分析。对9名哮喘患者、10名健康受试者和10名COPD受试者的痰液样本进行全基因组转录（Agilent Human 4 × 44 k阵列）和甲基化（Illumina 450 k BeadChip）分析。通过毛细管电泳检查RNA完整性，并用于计算机模拟校正生物样本库样品储存期间RNA降解产生的偏倚。基于痰液细胞分类计数，通过二次规划回归得出细胞类型特异性分子谱的估计值。所有分析均使用开源R/Bioconductor软件框架进行。发现线性回归步骤在去除基因表达数据的主要主成分中的RNA降解相关偏倚方面表现良好，增加了可检测为在哮喘和COPD急性发作中差异表达的基因的数量（与对照相比）。我们观察到细胞组成对混合细胞痰液分析结果有很大影响。例如，来自哮喘混合细胞数据的上调基因主要由去卷积后主要在嗜酸性粒细胞中表达的基因主导。然而，去卷积允许在单个细胞类型的水平上进行差异表达和甲基化分析，尽管我们只分析了有限数量的生物学重复，但发现与先前发表的关于哮喘中肺嗜酸性粒细胞基因表达的数据相比，提供了良好的估计。痰液甲基化组的分析表明在感兴趣的基因组区域中存在差异甲基化，例如，定位到与哮喘和COPD巨噬细胞中的主要组织相容性复合体（MHC）I类和II类分子相关的许多人类白细胞抗原（HLA）基因。此外，我们发现SMAD 3（SMAD家族成员3）基因，除其他外，位于差异甲基化的区域，这是以前在哮喘的背景下报告。在这项以方法学为导向的研究中，我们表明甲基化分析可以很容易地整合到痰液分析工作流程中，并表现出很强的潜力，有助于分析和理解肺部炎症。无论在何处RNA降解是关注的，在计算机校正可以有效地提高下游分析的灵敏度和特异性。我们建议，应尽可能将去卷积方法整合到痰液组学分析工作流程中，以促进炎症分子模式的公正发现和解释。
To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses. Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework. A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma. In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.
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影响因子：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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ALLIANCE Study Group as part of the German Centre for Lung Research (DZL)
DOI：
10.1164/rccm.201808-1599oc
发表时间：
2019-07-15
影响因子：
24.7
作者：
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通讯作者：
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