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Makorins are evolutionary conserved proteins that contain C3H-type zinc finger modules and a RING E3 ubiquitin ligase domain. In Drosophila, maternal Makorin 1 (Mkrn1) has been linked to embryonic patterning but the mechanism remained unsolved. Here, we show that Mkrn1 is essential for axis specification and pole plasm assembly by translational activation of oskar (osk). We demonstrate that Mkrn1 interacts with poly(A) binding protein (pAbp) and binds specifically to osk 3’ UTR in a region adjacent to A-rich sequences. Using Drosophila S2R+ cultured cells we show that this binding site overlaps with a Bruno1 (Bru1) responsive element (BREs) that regulates osk translation. We observe increased association of the translational repressor Bru1 with osk mRNA upon depletion of Mkrn1, indicating that both proteins compete for osk binding. Consistently, reducing Bru1 dosage partially rescues viability and Osk protein level in ovaries from Mkrn1 females. We conclude that Mkrn1 controls embryonic patterning and germ cell formation by specifically activating osk translation, most likely by competing with Bru1 to bind to osk 3’ UTR.
Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understanding the complex post-transcriptional regulation of gene expression. This information can be obtained from individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) experiments. Here, we present a complete data analysis workflow to reliably detect RBP binding sites from iCLIP data. The workflow covers all steps from the initial quality control of the sequencing reads up to peak calling and quantification of RBP binding. For each tool, we explain the specific requirements for iCLIP data analysis and suggest optimised parameter settings.
Most cellular processes are regulated by RNA-binding proteins (RBPs). These RBPs usually use defined binding sites to recognize and directly interact with their target RNA molecule. Individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) experiments are an important tool to de- scribe such interactions in cell cultures in-vivo. This experimental protocol yields millions of individual sequencing reads from which the binding spec- trum of the RBP under study can be deduced. In this PhD thesis I studied how RNA processing is driven from RBP binding by analyzing iCLIP-derived sequencing datasets.
First, I described a complete data analysis pipeline to detect RBP binding sites from iCLIP sequencing reads. This workflow covers all essential process- ing steps, from the first quality control to the final annotation of binding sites. I described the accurate integration of biological iCLIP replicates to boost the initial peak calling step while ensuring high specificity through replicate re- producibility analysis. Further I proposed a routine to level binding site width to streamline downstream analysis processes. This was exemplified in the re- analysis of the binding spectrum of the U2 small nuclear RNA auxiliary factor 2 (U2AF2, U2AF65). I recaptured the known dominance of U2AF65 to bind to intronic sequences of protein-coding genes, where it likely recognizes the polypyrimidine tract as part of the core spliceosome machinery.
In the second part of my thesis, I analyzed the binding spectrum of the serine and arginine rich splicing factor 6 (SRSF6) in the context of diabetes. In pancreatic beta-cells, the expression of SRSF6 is regulated by the transcription factor GLIS3, which encodes for a diabetes susceptibility gene. It is known that SRSF6 promotes beta-cell death through the splicing dysregulation of genes essential to beta-cell function and survival. However, the exact mechanism of how these RNAs are targeted by SRSF6 remains poorly understood. Here, I applied the defined iCLIP processing pipeline to describe the binding landscape of the splicing factor SRSF6 in the human pancreatic beta-cell line EndoC-H1. The initial binding sites definition revealed a predominant binding to coding sequences (CDS) of protein-coding genes. This was followed up by extensive motif analysis which revealed a so far, in human, unknown purine-rich binding motif. SRSF6 seemed to specifically recognize repetitions of the triplet GAA. I also showed that the number of contiguous triplets correlated with increasing binding site strength. I further integrated RNA-sequencing data from the same cell type, with SRSF6 in KD and in basal conditions, to analyze SRSF6- related splicing changes. I showed that the exact positioning of SRSF6 on alternatively spliced exons regulates the produced transcript isoforms. This mechanism seemed to control exons in several known susceptibility genes for diabetes.
In summary, in my PhD thesis, I presented a comprehensive workflow for the processing of iCLIP-derived sequencing data. I applied this pipeline on a dataset from pancreatic beta-cells to unveil the impact of SRSF6-mediated splicing changes. Thus, my analysis provides novel insights into the regulation of diabetes susceptibility genes.
Cancer-associated fibroblasts (CAFs) in the tumor microenvironment contribute to all stages of tumorigenesis and are usually considered to be tumor-promoting cells. CAFs show a remarkable degree of heterogeneity, which is attributed to developmental origin or to local environmental niches, resulting in distinct CAF subsets within individual tumors. While CAF heterogeneity is frequently investigated in late-stage tumors, data on longitudinal CAF development in tumors are lacking. To this end, we used the transgenic polyoma middle T oncogene-induced mouse mammary carcinoma model and performed whole transcriptome analysis in FACS-sorted fibroblasts from early- and late-stage tumors. We observed a shift in fibroblast populations over time towards a subset previously shown to negatively correlate with patient survival, which was confirmed by multispectral immunofluorescence analysis. Moreover, we identified a transcriptomic signature distinguishing CAFs from early- and late-stage tumors. Importantly, the signature of early-stage CAFs correlated well with tumor stage and survival in human mammary carcinoma patients. A random forest analysis suggested predictive value of the complete set of differentially expressed genes between early- and late-stage CAFs on bulk tumor patient samples, supporting the clinical relevance of our findings. In conclusion, our data show transcriptome alterations in CAFs during tumorigenesis in the mammary gland, which suggest that CAFs are educated by the tumor over time to promote tumor development. Moreover, we show that murine CAF gene signatures can harbor predictive value for human cancer.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3' untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3’ untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.
Key Points:
* Deep profiling identifies novel targets of miR-181 associated with global gene regulation.
* miR-181 MREs in repeat elements in the coding sequence act through translational inhibition.
* High-resolution analysis reveals an alternative seed match in functional MREs.
MicroRNAs (miRNAs) are critical post-transcriptional regulators in many biological processes. They act by guiding RNA-induced silencing complexes to miRNA response elements (MREs) in target mRNAs, inducing translational inhibition and/or mRNA degradation. Functional MREs are expected to predominantly occur in the 3' untranslated region and involve perfect base-pairing of the miRNA seed. Here, we generate a high-resolution map of miR-181a/b-1 (miR-181) MREs to define the targeting rules of miR-181 in developing murine T-cells. By combining a multi-omics approach with computational high-resolution analyses, we uncover novel miR-181 targets and demonstrate that miR-181 acts predominantly through RNA destabilization. Importantly, we discover an alternative seed match and identify a distinct set of targets with repeat elements in the coding sequence which are targeted by miR-181 and mediate translational inhibition. In conclusion, deep profiling of MREs in primary cells is critical to expand physiologically relevant targetomes and establish context-dependent miRNA targeting rules.