Transcriptional Transitions during Dictyostelium Spore Germination

Abstract

Many protozoa form spores in response to adversity; therefore, spore germination is a key process in their life cycle. Dictyostelium discoideum sporulates in response to starvation following a developmental program. Germination is characterized by two visible changes, spore swelling and the emergence of amoeba from the spore capsule. Several studies have indicated that an additional process termed spore activation is also required, but the physiological changes that characterize the three phases are largely uncharacterized. We used microarrays to monitor global transcriptional transitions as a surrogate measure of the physiological changes that occur during germination. Using two independent methods to induce germination, we identified changes in mRNA levels that characterized the germination process rather than changes that resulted from the induction method. We found that germination is characterized by three transitions. The first transition occurs during activation, while the spores appear dormant, the largest transition occurs when swelling begins, and the third transition occurs when emergence begins. These findings indicate that activation and swelling are not passive occurrences, such as dilution of inhibitors or spore rehydration, but are active processes that are accompanied by dramatic events in mRNA degradation and de novo transcription. These findings confirm and extend earlier reports that genes such as celA are regulated during spore germination. We also found by mutation analysis that the unconventional myosin gene myoI, which is induced during early germination, plays roles in the maintenance of dormancy and in spore swelling. This finding suggests that some of the observed transcriptional changes are required for spore germination.

The first fifty microarray studies in filamentous fungi

“…Transcriptional profiles were consistent with previously published data and corresponded with known biochemical processes associated with conidial germination. The results were also compared to previous microarray studies detailing spore germination in the dimorphic fungus Ustilago maydis (Zahiri et al, 2005) and the amoeba Dictyostelium discoideum (Xu et al, 2004). Of the 1287 genes differentially regulated in N. crassa, 25 were orthologous to those regulated in U. maydis and 16 orthologous to those regulated in D. discoideum.…”

Characterization of Ctr family genes and the elucidation of their role in the life cycle of Neurospora crassa

“…Yet, most of the molecular mechanisms responsible for determining the fate of the Neurospora developmental stages remain to be elucidated. A cross-species examination of gene expression patterns during spore germination in N. crassa, Ustilago maydis and Dictyostelium discoideum indicate that there might be common mechanisms governing breaking of dormancy and other early events of germination (Kasuga et al, 2005;Zahiri et al, 2005;Xu et al, 2004).…”

Global transcriptional responses to cisplatin in Dictyostelium discoideum identify potential drug targets

“…The latter is a surrogate measure of cell physiology and an indicator of differences between mutant strains (15)(16)(17)(18)(19)(20). Recently, the organism has been used as a model for drug target discovery using genetics and genomic approaches (1, 2).…”

“…Transcriptional profiling with cDNA microarrays can identify groups of genes whose expression is altered by experimental treatments. It is also used as a surrogate measure of cell physiology and to detect differences between mutant strains (15)(16)(17)(18)(19)(20). The availability of two isogenic cisplatin-resistant strains with alterations in sphingolipid metabolism allowed us to compare the mutants' transcriptional responses to that of wildtype cells after cisplatin exposure and to understand how the mutations affected the response.…”

“…The correlation between an individual gene's expression and gene function is rather poor, but it is quite good for groups of genes (15,16,20,23). We therefore used GOAT, a computational tool for identifying groups of gene annotations that are overrepresented in expression profiles (23), to analyze the 472 genes.…”