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Observational Study
. 2022 Apr 21;17(4):e0266897.
doi: 10.1371/journal.pone.0266897. eCollection 2022.

Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers

Michael Rode  1 , Kolja Nenoff  1 , Kerstin Wirkner  2 , Katrin Horn  1   2 , Andrej Teren  2   3 , Ralf Regenthal  4 , Markus Loeffler  1   2 , Joachim Thiery  2   5 , Achim Aigner  4 , Janne Pott  1   2 , Holger Kirsten  1   2 , Markus Scholz  1   2
Affiliations
Observational Study

Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers

Michael Rode et al. PLoS One. .

Abstract

Background: For many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome.

Methods and results: From a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants' medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B. In the case of carvedilol, we showed that, beside genes with well-established association with hypertension (GPER1, PDE4B and TNFAIP3), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis.

Conclusions: Our developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Replication of LIFE-Adult results in LIFE-Heart.
Top graph shows results of hierarchical multiple testing correction, which we selected as replication criteria. For information purposes, we include results for nominal significance (graph in the middle) and sign-test (lower graph). Results show strong transferability of results for whole blood to PBMC for carvedilol, timolol and prednisolone.
Fig 2
Fig 2. Polymedication of LIFE-Adult and LIFE-Heart participants and most common substances.
Top: Polymedication of LIFE-Adult and LIFE-Heart participants, shown by number of active substances consumed. Participants taking no medication were used as control group. Bottom: Most common substances used in both cohorts.
Fig 3
Fig 3. Differential gene expression caused by carvedilol, prednisolone and Timolol.
Original results as obtained from LIFE-Adult and successfully replicated in LIFE-Heart. Genes may be captured on multiple probes and are then shown multiple times. All replicated genes show the same effect direction.
Fig 4
Fig 4. Genes overexpressed by more than one substance.
Analysis shows high overlap between timolol and carvedilol.
Fig 5
Fig 5. Pathways with significant enrichment in LIFE-Adult and LIFE-Heart (FDR < 5%).
If two pathways are enriched due to the identical set of replicated genes, only the pathway with the higher enrichment (i.e. higher Odds ratio) is shown here. All significantly enriched pathways are reported in S5 Table. Differentially expressed genes per pathway are shown in S6 Fig.
See this image and copyright information in PMC

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