This Program Project is aimed at understanding the mechanisms that control growth and multicellular development in Dictyostelium. In the previous project period we took a functional genomics approach to high-throughput mutant phenotyping, transcriptional profiling, and computational modeling that allowed us to draw functional inferences for hundreds of genes. Over the next five years we will focus our efforts on understanding transcriptional control during development and bacterial recognition, both during the growth of solitary Dictyostelium amoebae and in the context of an innate immune response during their development. We will intersect transcriptional profiling data with physiological data to extract information about the genetic networks that coordinate bacterial recognition in Dictyostelium and characterize the genes and pathways involved. We will also test which regulators are responsible for the dramatic transcriptional changes that accompany Dictyostelium development. We will use RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation (ChlP-seq) to identify genes that are directly regulated by these transcription factors. We will develop computational techniques and integrative data mining to infer gene function and to construct consensus gene network models for use as scaffolds upon which we can propose additional experiments and add layers of information from other experiments. We also propose to implement the new methods within modem server-based software architecture with visualization-rich interactive interfaces that will make the entire planned data analytics transparent and operable by biologists with no computer science background. This work will help establish the amoeba as a model system for the study of innate immunity, leading to the development of tools and techniques that can be applied to understanding the response of eukaryotic cells to bacteria. Our strains, data, and software will be freely available to the research community and well integrated with dictyBase, the primary Dictyostelium community resource and data warehouse.
The response of amoebae to bacteria is relevant to infections in humans because it likely involves conserved pathways used by eukaryotes to defend against bacteria. Using diverse, rich, high-quality data sets we will devise new computational methods to accurately infer gene function, equipping researchers with improved means to analyze their own biomedical data. The methods we develop can be applied to other systems, improving our ability to predict gene function in development and in disease.
|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|
|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|
|Katoh-Kurasawa, Mariko; Santhanam, Balaji; Shaulsky, Gad (2016) The GATA transcription factor gene gtaG is required for terminal differentiation in Dictyostelium. J Cell Sci :|
|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|
|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|
|Zitnik, Marinka; Zupan, Blaz (2016) Jumping across biomedical contexts using compressive data fusion. Bioinformatics 32:i90-i100|
|Žitnik, Marinka; Nam, Edward A; Dinh, Christopher et al. (2015) Gene Prioritization by Compressive Data Fusion and Chaining. PLoS Comput Biol 11:e1004552|
Showing the most recent 10 out of 62 publications