Target gene expression levels and competition between transfected and endogenous microRNAs are strong confounding factors in microRNA high-throughput experiments
Takaya Saitoa b, Pål Sætromb c✉
- aDepartment of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Prinsesse Kristinsgt, 1, NO-7491 Trondheim, Norway. pal.satrom@ntnu.no
- bDepartment of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Prinsesse Kristinsgt. 1, NO-7491, Trondheim, Norway
- cDepartment of Computer and Information Science, Norwegian University of Science and Technology, Sem Sælands vei 9, NO-7491, Trondheim, Norway
- ✉Corresponding author
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Abstract
BACKGROUND: MicroRNA (miRNA) target genes tend to have relatively long and conserved 3' untranslated regions (UTRs), but to what degree these characteristics contribute to miRNA targeting is poorly understood. Different high-throughput experiments have, for example, shown that miRNAs preferentially regulate genes with both short and long 3' UTRs and that target site conservation is both important and irrelevant for miRNA targeting. RESULTS: We have analyzed several gene context-dependent features, including 3' UTR length, 3' UTR conservation, and messenger RNA (mRNA) expression levels, reported to have conflicting influence on miRNA regulation. By taking into account confounding factors such as technology-dependent experimental bias and competition between transfected and endogenous miRNAs, we show that two factors - target gene expression and competition - could explain most of the previously reported experimental differences. Moreover, we find that these and other target site-independent features explain about the same amount of variation in target gene expression as the target site-dependent features included in the TargetScan model. CONCLUSIONS: Our results show that it is important to consider confounding factors when interpreting miRNA high throughput experiments and urge special caution when using microarray data to compare average regulatory effects between groups of genes that have different average gene expression levels.
TL;DRIt is shown that it is important to consider confounding factors when interpreting miRNA high throughput experiments and to urge special caution when using microarray data to compare average regulatory effects between groups of genes that have different average gene expression levels. (Semantic Scholar)