Back

The circadian clock gene bmal1 is necessary for co-ordinated circatidal rhythms in the marine isopod Eurydice pulchra (Leach).

Kyriacou, C.; Zhang, L.; Green, E. W.; Webster, S. G.; Hastings, M. H.; Wilcockson, D. C.

2022-12-21 genetics
10.1101/2022.12.21.521378 bioRxiv
Show abstract

Circadian clocks in terrestrial animals are encoded by molecular feedback loops involving the negative regulators PERIOD, TIMELESS or CRYPTOCHROME2 and positive transcription factors CLOCK and BMAL1/CYCLE. The molecular basis of circatidal ([~]12.4 hour) or other lunar-mediated cycles ([~]15 day, [~]29 day), widely expressed in coastal organisms, is unknown. Disrupting circadian clockworks does not appear to affect lunar-based rhythms suggesting a molecular independence of the two cycles. Nevertheless, pharmacological inhibition of casein kinase 1 (CK1) that targets PERIOD stability in mammals and flies, affects both circadian and circatidal phenotypes in Eurydice pulchra (Ep), the speckled sea-louse. Here we show that these drug inhibitors of CK1 also affect the phosphorylation of EpCLK and EpBMAL1 and disrupt EpCLK-BMAL1-mediated transcription in Drosophila S2 cells, revealing a potential link between the positive circadian regulators and circatidal behaviour. We therefore performed dsRNAi knockdown of Epbmal1 as well as the major negative regulator in Eurydice, Epcry2. Epcry2 and Epbmal1 knockdown disrupted Eurydices circadian phenotypes as expected but in addition, circatidal behaviour was also sensitive to Epbmal1 knockdown. Thus three Eurydice negative circadian regulators, EpCRY2, in addition to EpPER and EpTIM, do not appear to be required for the expression of circatidal behaviour, in contrast to the positive regulator EpBMAL1. We suggest a neurogenetic model whereby the positive circadian regulators BMAL1-CLK are shared between circadian and circatidal mechanisms in Eurydice but circatidal rhythms require a novel, as yet unknown negative regulator.

Matching journals

The top 9 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.