Back

Transcriptomic responses to multigenerational environmental warming in a cold-adapted salmonid using de novo RNA sequencing

Penney, C. M.; Burness, G.; Wilson, C. C.

2022-10-22 genetics
10.1101/2022.10.21.513272 bioRxiv
Show abstract

Cold-adapted species are particularly threatened by climate change as rates of environmental warming outpace the ability of many populations adapt. Recent evidence suggests that transgenerational thermal plasticity may play a role in the response of cold-adapted organisms to long-term changes in temperature. Using RNA sequencing, we explored differential gene expression of lake trout (Salvelinus namaycush), a cold-adapted species, to examine the molecular processes that respond to elevated temperatures under conditions of within-generation (offspring) and transgenerational (parental) warm acclimation. We hypothesized that genes associated with metabolism, growth and thermal stress/tolerance would be differentially expressed in juvenile lake trout offspring depending on their own acclimation temperature and that of their parents. While parental warm acclimation did have a transgenerational effect on gene expression in their offspring, within-generation (offspring) warm acclimation had a larger effect on the number of differentially expressed genes. Differentially expressed genes enriched pathways for thermal stress, signaling processes, immune function, and transcription regulation and depended on the acclimation temperature of the offspring in isolation or in combination with parental warm acclimation. We provide evidence of the transgenerational response to warming at the transcriptional level in lake trout, which should be useful for future studies of transcriptomics and plasticity in this and other cold-adapted species.

Matching journals

The top 8 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.