Florencia Schlamp1, Sofie Y N Delbare2, Angela M Early3, Martin T Wells2, Sumanta Basu4, Andrew G Clark5,6. 1. Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA. mfs97@cornell.edu. 2. Statistics and Data Science, Cornell University, Ithaca, NY, USA. 3. Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA. 4. Statistics and Data Science, Cornell University, Ithaca, NY, USA. sb2457@cornell.edu. 5. Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA. ac347@cornell.edu. 6. Statistics and Data Science, Cornell University, Ithaca, NY, USA. ac347@cornell.edu.
Abstract
BACKGROUND: Immune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. RESULTS: To investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. CONCLUSIONS: This high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.
BACKGROUND: Immune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. RESULTS: To investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. CONCLUSIONS: This high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.
Entities:
Keywords:
Drosophila melanogaster; Granger causality; Immune response; Time course RNA-seq
Authors: Justin R DiAngelo; Michelle L Bland; Shelly Bambina; Sara Cherry; Morris J Birnbaum Journal: Proc Natl Acad Sci U S A Date: 2009-10-27 Impact factor: 11.205
Authors: Crystal M Vincent; Esteban J Beckwith; Carolina J Simoes da Silva; William H Pearson; Katrin Kierdorf; Giorgio F Gilestro; Marc S Dionne Journal: PLoS Pathog Date: 2022-09-21 Impact factor: 7.464