Transcriptional milestones in Dictyostelium development

Abstract

Development of the social amoeba Dictyostelium discoideum begins by starvation of single cells and ends in multicellular fruiting bodies 24 hours later. These major morphological changes are accompanied by sweeping gene expression changes, encompassing nearly half of the 13,000 genes in the genome. To explore the relationships between the transcriptome and developmental morphogenesis, we performed time-series RNA-sequencing analysis of the wild type and 20 mutant strains with altered morphogenesis. These strains exhibit arrest at different developmental stages, accelerated development, or terminal morphologies that are not typically seen in the wild type. Considering eight major morphological transitions, we identified 1,371 milestone genes whose expression changes sharply between two consecutive transitions. We also identified 1,099 genes as members of 21 regulons, which are groups of genes that remain coordinately regulated despite the genetic, temporal, and developmental perturbations in the dataset. The gene annotations in these milestones and regulons validate known transitions and reveal several new physiological and functional transitions during development. For example, we found that DNA replication genes are co-regulated with cell division genes, so they are co-expressed in mid-development even though chromosomal DNA is not replicated at that time. Altogether, the dataset includes 486 transcriptional profiles, across developmental and genetic conditions, that can be used to identify new relationships between gene expression and developmental processes and to improve gene annotations. We demonstrate the utility of this resource by showing that the cycles of aggregation and disaggregation observed in allorecognition-defective mutants involve a dedifferentiation process. We also show unexpected variability and sensitivity to genetic background and developmental conditions in two commonly used genes, act6 and act15, and robustness of the coaA gene. Finally, we propose that gpdA should be used as a standard for mRNA quantitation because it is less sensitive to genetic background and developmental conditions than commonly used standards. The dataset is available for democratized exploration without the need for programming skills through the web application dictyExpress and the data mining environment Orange.