Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs

Abstract

The genome and interactome of Saccharomyces cerevisiae have been characterized extensively over the course of the past few decades. However, despite many insights gained over the years, both functional studies and evolutionary analyses continue to reveal many complexities and confounding factors in the construction of reliable transcriptional regulatory network models. We present here a graph-based technique for comparing transcriptional regulatory networks based on network motif similarity for gene pairs. We construct interaction graphs for duplicated transcription factor pairs traceable to the ancestral whole-genome duplication as well as other paralogues in Saccharomyces cerevisiae. We create a set of network divergence metrics predicated on the presence and size of bi-fan arrays that are associated in the literature with gene duplication, within other network motifs. We compare the developed metrics to paralogue protein, gene and promoter alignment-free sequence dissimilarity to validate our results. We observe that our network divergence metrics generally agree with paralogue protein and gene sequence dissimilarity, and notice a weaker agreement with promoter dissimilarity. Our findings indicate that genetic divergence between paralogues is accompanied by a corresponding divergence in their interaction networks, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.