Back

Genetic interference with HvNotch provides new insights into the role of the Notch-signalling pathway for developmental pattern formation in Hydra

Pan, Q.; Mercker, M.; Klimovich, A.; Wittlieb, J.; Marciniak-Czochra, A.; Boettger, A.

2024-02-02 developmental biology
10.1101/2024.01.31.578171 bioRxiv
Show abstract

The Notch-signalling pathway plays an important role in pattern formation in Hydra. Using pharmacological Notch inhibitors (DAPT and SAHM1), it has been demonstrated that HvNotch is required for head regeneration and tentacle patterning in Hydra. HvNotch is also involved in establishing the parent-bud boundary and instructing buds to develop feet and detach from the parent. To further investigate the functions of HvNotch, we successfully constructed NICD (HvNotch intracellular domain)-overexpressing and HvNotch-knockdown transgenic Hydra strains. NICD-overexpressing transgenic Hydra showed a pronounced inhibition on the expression of predicted HvNotch-target genes, suggesting a dominant negative effect of ectopic NICD. This resulted in a "Y-shaped" phenotype, which arises from the parent-bud boundary defect seen in polyps treated with DAPT. Additionally, "multiple heads", "two-headed" and "ectopic tentacles" phenotypes were observed. The HvNotch-knockdown transgenic Hydra with reduced expression of HvNotch exhibited similar, but not identical phenotypes, with the addition of a "two feet" phenotype. Furthermore, approximately 20% of the HvNotch-knockdown polyps were unable to regenerate a new head after decapitation. We integrated these findings into a mathematical model based on long-range gradients of signalling molecules underlying sharply defined positions of HvNotch-signalling cells at the Hydra tentacle and bud boundaries.

Matching journals

The top 4 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.