Gene network landscape of the ciliate Tetrahymena thermophila.

Background: Genome-wide expression data of gene microarrays can be used to infer gene networks. At a cellular level, a gene network provides a picture of the modules in which genes are densely connected, and of the hub genes, which are highly connected with other genes. A gene network is useful to identify the genes involved in the same pathway, in a protein complex or that are co-regulated. In this study, we used different methods to find gene networks in the ciliate Tetrahymena thermophila, and describe some important properties of this network, such as modules and hubs.

Methodology/principal Findings: Using 67 single channel microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient (PCC), the Spearman correlation coefficient (SCC) and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast. The CLR network with a Z-score threshold 3.49 was determined to be the most robust. The TGN was partitioned, and 55 modules were found. In addition, analysis of the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to their expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and provide evidence indicating that some of these are involved in the same process in Tetrahymena as in human.

Conclusions/significance: This study constructed a Tetrahymena gene network, provided new insights to the properties of this biological network, and presents an important resource to study Tetrahymena genes at the pathway level.