DOI

https://doi.org/10.25772/CJ43-D074

Defense Date

2010

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Integrative Life Sciences

First Advisor

Danail G. Bonchev

Abstract

MicroRNAs represent a special class of small (~21–25 nucleotides) non-coding RNA molecules which exert powerful post-transcriptional control over gene expression in eukaryotes. Indeed microRNAs likely represent the most abundant class of regulators in animal gene regulatory networks. This study describes the recovery and network analyses of a suite of homologous microRNA targets recovered through two different prediction methods for whole gene regions across twelve Drosophila species. Phylogenetic criteria under an accepted tree topology were used as a reference frame to 1) make inference into microRNA-target predictions, 2) study mathematical properties of microRNA-gene regulatory networks, 3) and conduct novel phylogenetic analyses using character data derived from weighted edges of the microRNA-target networks. This study investigates the evidences of natural selection and phylogenetic signatures inherent within the microRNA regulatory networks and quantifies time and mutation necessary to rewire a microRNA regulatory network. Selective factors that appear to operate upon seed aptamers include cooperativity (redundancy) of interactions and transcript length. Topological analyses of microRNA regulatory networks recovered significant enrichment for a motif possessing a redundant link in all twelve species sampled. This would suggest that optimization of the whole interactome topology itself has been historically subject to natural selection where resilience to attack have offered selective advantage. It seems that only a modest number of microRNA–mRNA interactions exhibit conservation over Drosophila cladogenesis. The decrease in conserved microRNA-target interactions with increasing phylogenetic distance exhibited a cure typical of a saturation phenomena. Scale free properties of a network intersection of microRNA target predictions methods were found to transect taxonomic hierarchy.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

December 2010

Included in

Life Sciences Commons

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