bioRxivGenomicsPreprint
Nutrient deprivation differentially affects gene expression, immunity, and pathogen susceptibility across symbiotic states in a model cnidarian
Maria Valadez-IngersolliD, Pablo J. Aguirre CarrióniD, Caoimhe A. Bodnar, Niharika A. DesaiiD, Thomas D. GilmoreiD, Sarah W. DaviesiD
Abstract
Mutualistic symbioses between cnidarians and photosynthetic algae are modulated by complex interactions between host immunity and environmental conditions. Here, we investigate how symbiosis interacts with nutrient limitation to influence gene expression and stress response programming in the sea anemone Exaiptasia pallida (Aiptasia). Transcriptomic responses to starvation were similar between symbiotic and aposymbiotic Aiptasia; however, aposymbiotic anemone responses were stronger. Starved Aiptasia of both symbiotic states exhibited increased protein levels of immune-related transcription factor NF-{kappa}B, its associated gene pathways, and putative target genes. However, this starvation-induced increase in NF-{kappa}B only correlated with increased immunity in symbiotic anemones. Furthermore, starvation had opposite effects on Aiptasia susceptibility to pathogen and oxidative stress challenges, suggesting distinct energetic priorities under nutrient scarce conditions. Finally, when we compared starvation responses in Aiptasia to those of a facultative coral and nonsymbiotic anemone, "defense" responses were similarly regulated in Aiptasia and the facultative coral, but not in the nonsymbiotic anemone. This pattern suggests that capacity for symbiosis influences immune responses in cnidarians. In summary, expression of certain immune pathways - including NF-{kappa}B - does not necessarily predict susceptibility to pathogens, highlighting the complexities of cnidarian immunity and the influence of symbiosis under varying energetic demands.
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- Citations
- 5
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