bioRxivEcologyPreprint
Prior selection affects phenotypic and transcriptional response to hypoxia
Abstract
Hypoxia has profound and diverse effects on aerobic organisms, disrupting oxidative phosphorylation and activating several protective pathways. Predictions have been made that exposure to mild intermittent hypoxia may be protective against more severe exposure and may extend lifespan. Both effects are likely to depend on prior selection on phenotypic and transcriptional plasticity in response to hypoxia, and may therefore show signs of local adaptation. Here we report the lifespan effects of chronic, mild, intermittent hypoxia (CMIH) and short-term survival in acute severe hypoxia (ASH) in four clones of Daphnia magna originating from either permanent or intermittent habitats, the latter regularly drying up with frequent hypoxic conditions. We show that CMIH extended the lifespan in the two clones originating from intermittent habitats but had the opposite effect in the two clones from permanent habitats, which also showed lower tolerance to ASH. Exposure to CMIH did not protect against ASH; to the contrary, Daphnia from the CMIH treatment had lower ASH tolerance than normoxic controls. Few transcripts changed their abundance in response to the CMIH treatment in any of the clones. After 12 hours of ASH treatment, the transcriptional response was more pronounced, with numerous protein-coding genes with functionality in mitochondrial and respiratory metabolism, oxygen transport, and gluconeogenesis showing up-regulation. While clones from intermittent habitats showed somewhat stronger differential expression in response to ASH than those from permanent habitats, there were no significant hypoxia-by-habitat of origin or CMIH-by-ASH interactions. GO enrichment analysis revealed a possible hypoxia tolerance role by accelerating the molting cycle and regulating neuron survival through up-regulation of cuticular proteins and neurotrophins, respectively.
- organism: Drosophila
- assay: bulk RNA-seq
- research focus: cell type annotation
- approach: statistical model
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