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Hypoxia is a condition in which cells are deprived of adequate oxygen supply and represents a main feature of solid tumours. Cells under hypoxic stress activate transcriptional responses driven by hypoxia-inducible factors (HIFs), which affect multiple cellular pathways, including angiogenesis, metabolic adaptation and cell proliferation. While the transcriptional changes induced in hypoxic tumours are well characterised, it is still poorly understood how hypoxia contributes to the aberrant post-transcriptional regulation observed in tumours. In this PhD thesis, I studied the RNA response to hypoxia in cancer, to provide novel insights into its regulation.
Using deep RNA-Sequencing (RNA-Seq), I investigated transcriptome changes of three human cell lines from lung, cervical and breast cancer under hypoxia, advancing our knowledge of post-transcriptional gene regulation in hypoxic cancer. I show that hypoxia induced consistent changes in transcript abundance in the three cancer types. This was coupled to divergent splicing responses, highlighting the cell type specificity of alternative splicing programs. While the mRNA levels of RNA-binding proteins were mainly reduced, hypoxia upregulated muscleblind-like protein 2 (MBNL2) in all three cell lines. Hypoxia control was specific for MBNL2, since it did not affect its paralogs MBNL1 and MBNL3. Via knockdown experiments of MBNL2 in hypoxic cells, I could show that MBNL2 induction promotes adaptation of cancer cells to low oxygen by regulating both transcript abundance and alternative splicing of hypoxia response genes. In addition, depletion of MBNL2 reduced the proliferation and migration of cancer cells, corroborating a function of MBNL2 as cancer driver.
In the last few years, a novel class of RNAs has gained attention, namely circular RNAs (circRNAs), which are produced by a particular splicing mechanism, known as back-splicing. CircRNAs have been reported to change their abundance in cancer and their high stability makes them promising candidates as diagnostic biomarkers. In this study, I took advantage of deep rRNA-depleted RNA-Seq data to comprehensively investigate the expression of circRNAs in human cancer cells and their changes in response to hypoxia. To reliably identify circRNAs, I established a pipeline that integrates two available tools. for circRNA detection with custom approaches for quantification and statistical analysis. Using this pipeline, I identified 12006 circRNAs in the three cancer cell lines. Their molecular features suggest an involvement of complementary RNA sequences as well as trans-acting factors in circRNA biogenesis, including the splicing factor HNRNPC. Remarkably, I detected 210 circRNAs that are more abundant than their linear counterparts. Upon hypoxic stress, 64 circRNAs were differentially expressed in cancer cells, in most cases in a cell type-specific manner. In summary, in this PhD thesis, I present a comparative transcriptome profiling in human cancer cell lines. It reveals MBNL2 as an important player in hypoxic cancer progression and provides novel insights into the biogenesis and regulation of circRNAs under hypoxic stress.
Background: Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment–based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task.
Results: In this study we compare alignment–based and alignment–free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment–free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish.
Conclusions: The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs.