A novel technique to combine and analyse spatial and temporal expression datasets: A case study with the sea anemone Nematostella vectensis to identify potential gene interactions

Abstract

Understanding genetic interactions during early development of a given organism, is the first step toward unveiling gene regulatory networks (GRNs) that govern a biological process of interest. Predicting such interactions from large expression datasets by performing targeted knock-down/knock-out approaches is a challenging task. We use the currently available expression datasets (in situ hybridization images & qPCR time series) for a basal anthozoan the sea anemone N. vectensis to construct continuous spatiotemporal gene expression patterns during its early development. Moreover, by combining cluster results from each dataset we develop a method that provides testable hypotheses about potential genetic interactions. We show that the analysis of spatial gene expression patterns reveals functional regions of the embryo during the gastrulation. The clustering results from qPCR time series unveils significant temporal events and highlights genes potentially involved in N. vectensis gastrulation. Furthermore, we introduce a method for merging the clustering results from spatial and temporal datasets by which we can group genes that are expressed in the same region and at the time. We demonstrate that the merged clusters can be used to identify GRN interactions involved in various processes and to predict possible activators or repressors of any gene in the dataset. Finally, we validate our methods and results by predicting the repressor effect of NvErg on NvBra in the central domain during the gastrulation that has recently been confirmed by functional analysis.

Keywords: Cluster analysis; Gene regulatory networks; In situ hybridization (ISH); Reverse engineering gene regulatory networks; Spatiotemporal gene expression analysis; qPCR gene expression.