Back

Nutrient deprivation differentially affects gene expression, immunity, and pathogen susceptibility across symbiotic states in a model cnidarian

Valadez-Ingersoll, M.; Aguirre Carrion, P. J.; Bodnar, C. A.; Desai, N. A.; Gilmore, T. D.; Davies, S. W.

2023-07-30 genomics
10.1101/2023.07.30.551141 bioRxiv
Show 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.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.