Derivation of an interaction/regulation network describing pluripotency in human

Identification of the key genes/proteins of pluripotency and their interrelationships is an important step in understanding the induction and maintenance of pluripotency. Experimental approaches have accumulated large amounts of interaction/regulation data in mouse. We investigate how far such information can be transferred to human, the species of maximum interest, for which experimental data are much more limited. To address this issue, we mapped an existing mouse pluripotency network (the PluriNetWork) to human. We transferred interaction and regulation links between genes/proteins from mouse to human on the basis of orthologous relationship of the genes/proteins (called interolog mapping). To reduce the number of false positives, we used four different methods: phylogenetic profiling, Gene Ontology semantic similarity, gene co-expression, and RNA interference (RNAi) data. The methods and the resulting networks were evaluated by a novel approach using the information about the genes known to be involved in pluripotency from the literature. The RNAi method proved best for filtering out unlikely interactions, so it was used to construct the final human pluripotency network. The RNAi data are based on human embryonic stem cells (hESCs) that are generally considered to be in a (primed) epiblast stem cell state. Therefore, we assume that the final human network may reflect the (primed) epiblast stem cell state more closely, while the mouse network reflects the (unprimed/naïve) embryonic stem cell state more closely.

► We derived a putative human pluripotency network from mouse.
► We filtered the interactions using four different methods.
► The resulting networks were evaluated against the literature using a novel approach.
► The best method (RNAi) was used to construct the final human pluripotency network.
► We examined why the most widely used methods performed worse than the RNAi method.