Computational Approaches Reveal New Insights into Regulation and Function of Non; coding RNAs and their Targets

  • Tanvir Alam

Student thesis: Doctoral Thesis

Abstract

Regulation and function of protein-coding genes are increasingly well-understood, but no comparable evidence exists for non-coding RNA (ncRNA) genes, which appear to be more numerous than protein-coding genes. We developed a novel machine-learning model to distinguish promoters of long ncRNA (lncRNA) genes from those of protein-coding genes. This represents the first attempt to make this distinction based on properties of the associated gene promoters. From our analyses, several transcription factors (TFs), which are known to be regulated by lncRNAs, also emerged as potential global regulators of lncRNAs, suggesting that lncRNAs and TFs may participate in bidirectional feedback regulatory network. Our results also raise the possibility that, due to the historical dependence on protein-coding gene in defining the chromatin states of active promoters, an adjustment of these chromatin signature profiles to incorporate lncRNAs is warranted in the future. Secondly, we developed a novel method to infer functions for lncRNA and microRNA (miRNA) transcripts based on their transcriptional regulatory networks in 119 tissues and 177 primary cells of human. This method for the first time combines information of cell/tissueVspecific expression of a transcript and the TFs and transcription coVfactors (TcoFs) that control activation of that transcript. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues and associated knowledgebase (FARNA) is developed. FARNA, having the most comprehensive function annotation of considered ncRNAs across the widest spectrum of cells/tissues, has a potential to contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. Thirdly, we developed a novel machine-learning model to identify LD motif (a protein interaction motif) of paxillin, a ncRNA target that is involved in cell motility and cancer metastasis. Our recognition model identified new proteins not previously known to harbor LD motifs and we experimentally confirmed some of our predicted motifs. This novel discovery will expand our knowledge of cancer metastasis and will facilitate therapeutic targeting linking specific ncRNAs via paxillin proteins to diseases. Finally, through bioinformatics approaches, we identified lncRNAs as markers that distinguish classical from alternative activation of macrophage. This result may have good use in the diagnosis of infectious diseases.
Date of AwardNov 28 2016
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Science and Engineering
SupervisorVladimir Bajic (Supervisor)

Keywords

  • Long non-coding RNA
  • MiRNA
  • LD motif
  • Macrophage
  • Machine Learning
  • Bioinformatics

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