Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

Biophysical and computational investigations into G-quadruplex structural polymorphism and interaction with small molecules.

In the cell, guanine-rich nucleic acids can self-assemble into unique four stranded tertiary structures known as G-quadruplexes. G-quadruplex formation in the telomere leads inhibits telomerase, an enzyme activated in cancer cells to maintain the telomere and allowing for cancer cells to achieve immortality. G-quadruplex formation in the promoters and 5’-untranslated regions regulates the expression of many oncogenes. Furthermore, G-quadruplex formation during cellular replication promotes genomic instability, a characteristic which enables tumor development. Because of their implication in cancer, G-quadruplex structures have emerged as attractive drug targets for anti-tumor therapeutics. In the current dissertation work, we present three experimental approaches to investigate G-quadruplex structures, biophysical properties, small molecule interaction, and the thermodynamics of G-quadruplex formation. Current approaches to study G-quadruplex structures often employ sequence modifications or changes to the experimental condition, as a way of resolving the structural polymorphism associated with many G-quadruplex-forming sequences, to select for a single conformation for high-resolution structural studies. Our strategy for resolving G-quadruplex structural polymorphism is superior in that the experimental approaches do not result in drastic perturbation of the system. In the first approach, we employed size exclusion chromatography to separate a mixture of G-quadruplex structures formed from a G-quadruplex-forming sequence. We demonstrated that it is possible to isolate distinct species of G-quadruplex structures for further biophysical studies. In the second approach, we employed hydrodynamic bead modeling to study the structural polymorphism of a G-quadruplex-forming sequence. We showed that properties calculated from models agreed with experimentally determined values and could be used to predict the folding of G-quadruplex-forming oligonucleotides whose high-resolution structures are ambiguous or not available. In our third approach, we presented a virtual screening platform that was successful in identifying a new Gquadruplex-interacting small molecule. The results of the virtual screen were validated with extensive biophysical testing. Our target for the virtual screen was a G-quadruplex structure generated in silico, which represents one approach to receptor-based drug discovery when high-resolution structures of the binding site are not available. Taken together, our three approaches represent a new paradigm for drug discovery from guaninerich sequence to anti-cancer drugs