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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Program Projects (P01)
Project #
5P01HD039691-12
Application #
8469535
Study Section
Special Emphasis Panel (ZHD1-DSR-N (50))
Program Officer
Coulombe, James N
Project Start
2001-06-19
Project End
2017-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
12
Fiscal Year
2013
Total Cost
$854,874
Indirect Cost
$281,449
Name
Baylor College of Medicine
Department
Biochemistry
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
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
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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

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