Moritz Schäfer


Last Name

Schäfer

First Name

Moritz

ORCID

0000-0001-6489-1947

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2020 - 20223
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Publications 1 - 3 of 3
  • SF3B1 facilitates HIF1-signaling and promotes malignancy in pancreatic cancer
    Item type: Journal Article
    Simmler, Patrik; Cortijo, Cédric; Koch, Lisa Maria; et al. (2022)
    Cell Reports
    Mutations in the splicing factor SF3B1 are frequently occurring in various cancers and drive tumor progression through the activation of cryptic splice sites in multiple genes. Recent studies also demonstrate a positive correlation between the expression levels of wild-type SF3B1 and tumor malignancy. Here, we demonstrate that SF3B1 is a hypoxia-inducible factor (HIF)-1 target gene that positively regulates HIF1 pathway activity. By physically interacting with HIF1α, SF3B1 facilitates binding of the HIF1 complex to hypoxia response elements (HREs) to activate target gene expression. To further validate the relevance of this mechanism for tumor progression, we show that a reduction in SF3B1 levels via monoallelic deletion of Sf3b1 impedes tumor formation and progression via impaired HIF signaling in a mouse model for pancreatic cancer. Our work uncovers an essential role of SF3B1 in HIF1 signaling, thereby providing a potential explanation for the link between high SF3B1 expression and aggressiveness of solid tumors.
  • Prediction of the miRNA interactome – Established methods and upcoming perspectives
    Item type: Review Article
    Schäfer, Moritz; Ciaudo, Constance (2020)
    Computational and Structural Biotechnology Journal
    MicroRNAs (miRNAs) are well-studied small noncoding RNAs involved in post-transcriptional gene regulation in a wide range of organisms, including mammals. Their function is mediated by base pairing with their target RNAs. Although many features required for miRNA-mediated repression have been described, the identification of functional interactions is still challenging. In the last two decades, numerous Machine Learning (ML) models have been developed to predict their putative targets. In this review, we summarize the biological knowledge and the experimental data used to develop these ML models. Recently, Deep Neural Network-based models have also emerged in miRNA interaction modeling. We thus outline established and emerging models to give a perspective on the future developments needed to improve the identification of genes directly regulated by miRNAs.
Publications 1 - 3 of 3