The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health - Schmidt, Maria; Hopp, Lydia; Arakelyan, Arsen; Kirsten, Holger; Engel, Christoph; Wirkner, Kerstin; Krohn, Knut; Burkhardt, Ralph; Thiery, Joachim; Loeffler, Markus; Loeffler-Wirth, Henry; Binder, Hans (Frontiers in Big Data, 2020)
Background: The blood transcriptome is expected to provide a detailed picture of organism’s physiological state with potential impact for applications in medical diagnostics and molecular and epidemiological research. We here present the analysis of blood specimen of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction and data integration. Methods: Self Organizing Maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. The method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identify two major blood transcriptome types where type 1 accumulates more men, elderly and overweight people and it upregulates genes associating with inflammation and increased heme metabolism, while type 2 accumulates women, younger and normal weight participants and it associates with activated immune response, transcriptional, ribosomal, mitochondrial and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, ageing and obesity driven by an underlying common pattern, which associates with immune response and the increase of inflammatory processes. Conclusions: Machine learning application to large and heterogeneous omics data provides a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.