The tumor suppressor microRNA let-7 inhibits human LINE-1 retrotransposition

Nearly half of the human genome is made of transposable elements (TEs) whose activity continues to impact its structure and function. Among them, Long INterspersed Element class 1 (LINE-1 or L1) elements are the only autonomously active TEs in humans. L1s are expressed and mobilized in different cancers, generating mutagenic insertions that could affect tumor malignancy. Tumor suppressor microRNAs are ∼22nt RNAs that post-transcriptionally regulate oncogene expression and are frequently downregulated in cancer. Here we explore whether they also influence L1 mobilization. We show that downregulation of let-7 correlates with accumulation of L1 insertions in human lung cancer. Furthermore, we demonstrate that let-7 binds to the L1 mRNA and impairs the translation of the second L1-encoded protein, ORF2p, reducing its mobilization. Overall, our data reveals that let-7, one of the most relevant microRNAs, maintains somatic genome integrity by restricting L1 retrotransposition.European Research Council (ERC) ERC-2009-StG 243312French National Research Agency (ANR) ANR-11-LABX-0028-01 ANR-15-IDEX-01Centre National de la Recherche Scientifique (CNRS) 3546University Hospital Federation (FHU) OncoAgeMINECO PEJ-2014-A-31985 SAF2015-71589-PMINECO by European Regional Development Fund SAF2015-71589-PSpanish Government RYC-2016-21395Career Integration Grant-Marie Curie FP7-PEOPLE-2011-CIG-30381

Measuring and interpreting transposable element expression

International audienceTransposable elements (TEs) are insertional mutagens that contribute greatly to the plasticity of eukaryotic genomes, influencing the evolution and adaptation of species as well as physiology or disease in individuals. Measuring TE expression helps to understand not only when and where TE mobilization can occur, but also how this process alters gene expression, chromatin accessibility or cellular signalling pathways. Although genome-wide gene expression assays such as RNA-sequencing include transposon-derived transcripts, the majority of computational analytical tools discard or misinterpret TE-derived reads. Emerging approaches are improving the identification of expressed TE loci and helping to discriminate TE transcripts that permit TE mobilization from gene-TE chimeric transcripts or pervasive transcription. Here, we review the main challenges associated with the detection of TE expression, including mappability, insertional and internal sequence polymorphisms, and the diversity of the TE transcriptional landscape, as well as the different experimental and computational strategies to solve them