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The ability of an organism to regrow tissues is regulated by various signaling pathways. One such pathway that has been studied widely both in the context of regeneration and development is the Notch signaling pathway. Notch is required for the development of the eye and regeneration of tissues in multiple organisms, but it is unknown if Notch plays a role in the regulation of Xenopus laevis embryonic eye regrowth. We found that Notch1 is required for eye regrowth and regulates retinal progenitor cell proliferation. Chemical and molecular inhibition of Notch1 significantly decreased eye regrowth by reducing retinal progenitor cell proliferation without affecting retinal differentiation. Temporal inhibition studies showed that Notch function is required during the first day of regrowth. Interestingly, Notch1 loss-of-function phenocopied the effects of the inhibition of the proton pump, vacuolar-type ATPase (V-ATPase), where retinal proliferation but not differentiation was blocked during eye regrowth. Overexpression of a form of activated Notch1, the Notch intracellular domain (NICD) rescued the loss of eye regrowth due to V-ATPase inhibition. These findings highlight the importance of the Notch signaling pathway in eye regeneration and its role in inducing retinal progenitor cell proliferation in response to injury. 

Abstract MotivationMHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed. ResultsWe presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods. Availability and implementationThe software can be found in https://github.com/minrq/pMHC. Supplementary informationSupplementary data are available at Bioinformatics online.