Ege A, a novel C2 domain containing protein, is essential for GPCR-mediated gene expression in dictyostelium

New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data

Background

Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.

Results

Here we describe a Bayesian network approach that addresses a specific network within a large dataset to discover new components. Our algorithm draws individual genes from a large gene-expression repository, and ranks them as potential members of a known pathway. We apply this method to discover new components of the cAMP-dependent protein kinase (PKA) pathway, a central regulator of Dictyostelium discoideum development. The PKA network is well studied in D. discoideum but the transcriptional networks that regulate PKA activity and the transcriptional outcomes of PKA function are largely unknown. Most of the genes highly ranked by our method encode either known components of the PKA pathway or are good candidates. We tested 5 uncharacterized highly ranked genes by creating mutant strains and identified a candidate cAMP-response element-binding protein, yet undiscovered in D. discoideum, and a histidine kinase, a candidate upstream regulator of PKA activity.

Conclusions

The single-gene expansion method is useful in identifying new components of known pathways. The method takes advantage of the Bayesian framework to incorporate prior biological knowledge and discovers higher-order dependencies among genes while greatly reducing the computational resources required to process high-throughput datasets.

Chemoattractant signaling in dictyostelium discoideum

Dictyostelium is an accessible organism for studies of signaling via chemoattractant receptors. Chemoattractant-mediated signaling events and components are reviewed and presented as a series of connected modules, including excitation, inhibition, G protein-independent responses, early gene expression, inositol lipids, PH domain-containing proteins, cyclic AMP signaling, polarization acquisition, actin polymerization, and cortical myosin. The network incorporates information from biochemical, genetic, and cell biological experiments carried out on living cells. The modules and connections represent current understanding, and future information is expected to modify and build upon this structure.

A user's guide to restriction enzyme-mediated integration in Dictyostelium

Restriction enzyme-mediated integration (REMI) has been used to study a number of cellular and developmental processes in Dictyostelium discoideum. In this paper we review the basics of this powerful method of introducing random mutations in Dictyostelium. Here we discuss several mutation screens that have been devised and some of the genes that have been discovered through this approach to mutagenesis. Included in this discussion is how one goes about isolating a gene that has been disrupted by REMI, and how one confirms that this disruption is actually responsible for the observed phenotype. Finally, we describe how REMI can be used as an effective teaching tool in undergraduate cell biology laboratory courses.