Thromb Haemost 2019; 119(08): 1247-1264
DOI: 10.1055/s-0039-1693165
Theme Issue Article
Georg Thieme Verlag KG Stuttgart · New York

Using Context-Sensitive Text Mining to Identify miRNAs in Different Stages of Atherosclerosis

Markus Joppich
1  Department of Informatics, LFE Bioinformatics, Ludwig-Maximilians-Universität München, Munich, Germany
,
Christian Weber
2  Institute for Cardiovascular Prevention, Ludwig-Maximilians-Universität München, Munich, Germany
,
Ralf Zimmer
1  Department of Informatics, LFE Bioinformatics, Ludwig-Maximilians-Universität München, Munich, Germany
› Author Affiliations
Funding This work has been supported by the DFG (Deutsche Forschungsgemeinschaft) via SFB1123/2 (projects A1 and Z2).
Further Information

Publication History

14 March 2019

14 May 2019

Publication Date:
02 August 2019 (online)

Abstract

790 human and mouse micro-RNAs (miRNAs) are involved in diseases. More than 26,428 miRNA–gene interactions are annotated in humans and mice. Most of these interactions are posttranscriptional regulations: miRNAs bind to the messenger RNAs (mRNAs) of genes and induce their degradation, thereby reducing the gene expression of target genes. For atherosclerosis, 667 miRNA–gene interactions for 124 miRNAs and 343 genes have been identified and described in numerous publications. Some interactions were observed through high-throughput experiments, others were predicted using bioinformatic methods, and some were determined by targeted experiments. Several reviews collect knowledge on miRNA–gene interactions in (specific aspects of) atherosclerosis.

Here, we use our bioinformatics resource (atheMir) to give an overview of miRNA–gene interactions in the context of atherosclerosis. The interactions are based on public databases and context-based text mining of 28 million PubMed abstracts. The miRNA–gene interactions are obtained from more than 10,000 publications, of which more than 1,000 are in a cardiovascular disease context (266 in atherosclerosis). We discuss interesting miRNA–gene interactions in atherosclerosis, grouped by specific processes in different cell types and six phases of atherosclerotic progression. All evidence is referenced and easily accessible: Relevant interactions are provided by atheMir as supplementary tables for further evaluation and, for example, for the subsequent data analysis of high-throughput measurements as well as for the generation and validation of hypotheses. The atheMir approach has several advantages: (1) the evidence is easily accessible, (2) regulatory interactions are uniformly available for subsequent high-throughput data analysis, and (3) the resource can incrementally be updated with new findings.

Note: The review process for this paper was fully handled by Gregory Y. H. Lip, Editor-in-Chief.


Supplementary Material