bioRxivGenomicsPreprint
Gene transcriptional profiling to assess Chinook salmon osmoregulatory resilience to salinity challenges
Shahinur S Islam, Ken M. JeffriesiD, John W. HeathiD, Daniel D. HeathiD
Abstract
Given unprecedented environmental change, Pacific salmonids are becoming increasingly threatened; however, genomic technologies provide an opportunity to assess Pacific salmonid resilience to on-going environmental change. Also, given increasing conservation and animal welfare concerns, non-lethal tissue sampling is emerging for assessing the physiological status of wild species. Relatively few studies, however, have compared the transcriptomic response of fish sampled non-lethally versus lethally for fish health and stress quantification. Here, we conducted a comparative gene transcriptional profiling study using 28 qPCR assays on the OpenArray platform as an osmoregulatory stress transcriptional profiling chip. We sampled both non-lethal (gill biopsies, skin swabs, and water [eRNA]) and lethal (whole gill and liver) tissue samples from Chinook salmon (Oncorhynchus tshawytscha) smolts exposed for 0 h, 24 h, and 72 h to freshwater (0 PSU), brackish water (15 PSU) or seawater (30 PSU). We observed that most assays showed higher qPCR amplification success in both non-lethal gill biopsies and lethal gill tissues, compared to other tissues. We found more significant differences in transcript abundance in traditional lethal tissues compared to non-lethal tissues. With increasing duration of salinity exposure, gene transcript levels were typically upregulated in seawater samples compared to freshwater samples for both non-lethal and lethal tissues. The targeted genes for both non-lethal and lethal tissues were significantly and positively correlated across time points. Our findings support the use of non-lethal tissue sampling to improve Chinook salmon hatchery rearing, inform hatchery release timing, and facilitate future field application for the conservation of wild stocks.
- organism: non-human primate
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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