Background: The blood transcriptome is expected to provide a detailed picture of
an organism’s physiological state with potential outcomes for applications in medical
diagnostics and molecular and epidemiological research.We here present the analysis of
blood specimens 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. This 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 identified two major blood transcriptome types where type
1 was found more in men, the elderly, and overweight people and it upregulated
genes associated with inflammation and increased heme metabolism, while type 2 was
predominantly found in women, younger, and normal weight participants and it was
associated with activated immune responses, transcriptional, ribosomal, mitochondrial,
and telomere-maintenance cell-functions. We find a striking overlap of signatures shared
by multiple diseases, aging, and obesity driven by an underlying common pattern, which
was associated with the immune response and the increase of inflammatory processes.
Conclusions: Machine learning applications for large and heterogeneous omics data
provide 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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:84537 |
| Date | 03 April 2023 |
| Creators | Schmidt, Maria, Hopp, Lydia, Arakelyan, Arsen, Kirsten, Holger, Engel, Christoph, Wirkner, Kerstin, Krohn, Knut, Burkhardt, Ralph, Thiery, Joachim, Löffler, Markus, Löffler-Wirth, Henry, Binder, Hans |
| Publisher | Frontiers Research Foundation |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 2624-909X, 548873 |
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