UNRAVELLING TRANSCRIPTOME 3-END DIVERSITY IN AN ESTROGEN-RESPONSIVE BREAST CANCER CELL LINE MODEL

Date

2023-8-28

Author

Döken, Didem Naz

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Next-generation sequencing techniques allowed a better understanding of cancer transcriptomes. Despite advancements, technical limitations still exist to fully decipher the non-canonical aspects of the transcriptome. Here, we focused on mRNA 3'end diversity in a breast cancer cell line model to better understand estrogen-regulated gene expression patterns. I analyzed the 3'sequencing data generated in our laboratory to decipher estrogen-responsive changes of the 3'ends of mRNA isoforms. While microarrays and RNA-sequencing approaches provided in-depth views of estrogen-related gene expression changes, these studies mainly focused on individual genes and/or canonical isoforms. Our results suggested estrogen-responsive premature termination events in large gene bodies from a 3'end perspective. The decrease in open chromatin marks and RNA polymerase-II occupancy at specific transcript termination sites suggested potential interruptions in the transcriptional process of a group of genes. Our investigation further revealed that repetitive elements, particularly SINEs, are enriched within the large introns of genes that generate intronic transcripts as early as 45 minutes of estrogen stimulation. Of interest, the early expression of intronic transcripts decreases during prolonged estrogen stimulation while the full-length isoforms of the same genes are gradually upregulated. In addition to these intronic polyadenylation events, there are cases of alternative 3'UTR usage regulated by E2 in MCF7 cells. In conclusion, our 3'end sequencing perspective allowed the discovery of unreported intronic isoforms that seem to play a role in transcriptional dynamics and provided the preliminary data for future work. Altogether, these findings will provide a comprehensive view of transcriptional dynamics regulated by estrogen.

Subject Keywords

Alternative Polyadenylation, RNA Biology, Cancer Genetics, Transcriptomics, Bioinformatics

URI

https://hdl.handle.net/11511/105255

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Graduate School of Natural and Applied Sciences, Thesis