Understanding the Alternative Splicing-regulated Transcriptomic Changes During Mouse Retinal Development and Disease Through Global and Gene-centric Approaches

Alternative splicing (AS) is an important layer of gene regulation and has been shown to control various cellular processes including splicing, mRNA export, translation and cell cycle. Mis-regulation in AS has been implicated in many diseases. But, the role of AS in the retinal development and diseases remains unexplored. To this end, I employed a two-pronged approach, (i.e), gene-centric approach and an en-mass transcriptome analysis approach to address this question. For the first approach, I studied the role of an alternative splicing factor, Sfrs10 and a kinase, Citron Kinase (CitK) and its spliced isoforms in murine retinal development and diseases. Expression analysis of Sfrs10 in mouse and human retinae showed that unlike mouse, it was not expressed in normal human retina but was observed only in AMD retina, suggesting a specific role in response to oxidative stress. In parallel, I showed that the loss of CitK affected the cell division of a subset of retinal progenitor cells which in turn affected the late neurogenesis, specifically that of the Islet1+ bipolar neurons. In the second approach, global analyses were performed by employing RNA deep sequencing on cytoplasmic and nuclear fractions of developing retinal tissue. We investigated if the nuclear transcriptome would be ahead of that of the cytoplasm where it simultaneously executes the current molecular program whilst preparing for the next program i.e., de novo transcription. Also, I employed a custom bioinformatics pipeline to reverse-engineer the order in which the molecular programs are set up as the retinal tissue develops. Further, I extended the study to Nrl gene knockout to identify the perturbation of molecular pathways in the absence of the gene. Here, our bioinformatics strategy could predict the perturbed molecular programs well before its histological manifestation. We also compared our methodology with the existing methods of data analysis and show that our pipeline could give information on transcription kinetics of genes segregated into each bin. Thus, this pipeline was employed in the temporal comparison of a triple microRNA cluster knockout and its wild type counterpart across different stages of development