The aim of this Program Project is to discover the function of most genes in the Dictyostelium genome. In Project II we utilize concepts and methods that are centered on gene expression analysis to serve this goal. In previous years we have discovered that transcriptional profiling is a robust method for phenotypic analysis of Dictyostelium mutants and a useful means of assigning function to genes. Here we propose to study the networks that regulate gene expression during development while applying Next Generation sequencing technologies to the task. First, we will establish a 'reference transcriptome'for direct analysis of transcript abundance in the developing wild type, examining both mRNAs and non-coding RNAs. This analysis of the transcriptome will also serve as a reference for comparison with mutant strains in this project and for the entire community. Next, we will test which regulators are responsible for the dramatic transcriptional changes that accompany Dictyostelium development. We hypothesize that evolutionarily conserved transcription factors are likely to be master regulators of developmental gene expression. We will test that hypothesis by mutating the most conserved transcription factor genes and analyzing the resulting developmental phenotypes. Once we identify the transcription factors necessary for developmental progression we will use RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation (ChlP-seq) to identify genes that are directly regulated by these transcription factors. Finally, we will analyze many mutants from Project 1 by transcriptional profiling, with an emphasis on mutants in genes that participate in bacterial recognition. We will intersect the transcriptional data with the fitness and the physiological data provided by Project 1 to find links between gene function and patterns of gene expression. The data we produce will also be used by Project 111 for extracting information about the genetic networks that coordinate the functions of individual genes in Dictyostelium development.
The lessons we learn can be applied to other systems, improving our ability to predict gene function from gene expression in development and in disease. Another aspect is the transcriptional response of eukaryotic cells to bacteria. This response to infection is important due of its obvious relation to human health and because our work will reveal conserved pathways used by eukaryotes in their interactions with bacteria.
| Dinh, Christopher; Farinholt, Timothy; Hirose, Shigenori et al. (2018) Lectins modulate the microbiota of social amoebae. Science 361:402-406 |
| Hirose, Shigenori; Chen, Gong; Kuspa, Adam et al. (2017) The polymorphic proteins TgrB1 and TgrC1 function as a ligand-receptor pair in Dictyostelium allorecognition. J Cell Sci 130:4002-4012 |
| Swatson, William S; Katoh-Kurasawa, Mariko; Shaulsky, Gad et al. (2017) Curcumin affects gene expression and reactive oxygen species via a PKA dependent mechanism in Dictyostelium discoideum. PLoS One 12:e0187562 |
| Stajdohar, Miha; Rosengarten, Rafael D; Kokosar, Janez et al. (2017) dictyExpress: a web-based platform for sequence data management and analytics in Dictyostelium and beyond. BMC Bioinformatics 18:291 |
| Rosengarten, Rafael D; Santhanam, Balaji; Kokosar, Janez et al. (2017) The Long Noncoding RNA Transcriptome of Dictyostelium discoideum Development. G3 (Bethesda) 7:387-398 |
| Zitnik, Marinka; Zupan, Blaz (2016) Jumping across biomedical contexts using compressive data fusion. Bioinformatics 32:i90-i100 |
| Chen, Xinlu; Köllner, Tobias G; Jia, Qidong et al. (2016) Terpene synthase genes in eukaryotes beyond plants and fungi: Occurrence in social amoebae. Proc Natl Acad Sci U S A 113:12132-12137 |
| Li, Cheng-Lin Frank; Santhanam, Balaji; Webb, Amanda Nicole et al. (2016) Gene discovery by chemical mutagenesis and whole-genome sequencing in Dictyostelium. Genome Res 26:1268-76 |
| Zhang, Xuezhi; Zhuchenko, Olga; Kuspa, Adam et al. (2016) Social amoebae trap and kill bacteria by casting DNA nets. Nat Commun 7:10938 |
| Zitnik, Marinka; Zupan, Blaz (2016) COLLECTIVE PAIRWISE CLASSIFICATION FOR MULTI-WAY ANALYSIS OF DISEASE AND DRUG DATA. Pac Symp Biocomput 21:81-92 |
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