Thrombocytopenia, a most common complication of radiotherapy and chemotherapy, is an important cause of morbidity and mortality in cancer patients. However, there are still no approved agents for the treatment of radiation- and chemotherapy-induced thrombocytopenia (RIT and CIT, respectively). In this study, a drug screening model for predicting compounds with activity in promoting megakaryocyte (MK) differentiation and platelet production was established based on machine learning (ML), and a natural product ingenol was predicted as a potential active compound. Then, in vitro experiments showed that ingenol significantly promoted MK differentiation in K562 and HEL cells. Furthermore, a RIT mice model and c-MPL knock-out (c-MPL-/-) mice constructed by CRISPR/Cas9 technology were used to assess the therapeutic action of ingenol on thrombocytopenia. The results showed that ingenol accelerated megakaryopoiesis and thrombopoiesis both in RIT mice and c-MPL-/- mice. Next, RNA-sequencing (RNA-seq) was carried out to analyze the gene expression profile induced by ingenol during MK differentiation. Finally, through experimental verifications, we demonstrated that the activation of PI3K/Akt signaling pathway was involved in ingenol-induced MK differentiation. Blocking PI3K/Akt signaling pathway abolished the promotion of ingenol on MK differentiation. Nevertheless, inhibition of TPO/c-MPL signaling pathway could not suppress ingenol-induced MK differentiation. In conclusion, our study builds a drug screening model to discover active compounds against thrombocytopenia, reveals the critical roles of ingenol in promoting MK differentiation and platelet production, and provides a promising avenue for the treatment of RIT.
Keywords: Ingenol; Ingenol (PubChem CID: 442042); Machine learning; Megakaryocyte differentiation; Platelets; Thrombocytopenia.
Copyright © 2022 Elsevier Ltd. All rights reserved.