Natural products play a pivotal role in medicine especially in the cancer arena. Many drugs that are currently used in cancer chemotherapy originated from or were inspired by nature. Jerantinine B (JB) is one of seven novel Aspidosperma indole alkaloids isolated from the leaf extract of Tabernaemontana corymbosa. Preliminary antiproliferative assays revealed that JB and JB acetate significantly inhibited growth and colony formation, accompanied by time- and dose-dependent apoptosis induction in human cancer cell lines. JB significantly arrested cells at the G2/M cell cycle phase, potently inhibiting tubulin polymerisation. Polo-like kinase 1 (PLK1; an early trigger for the G2/M transition) was also dose-dependently inhibited by JB (IC50 1.5 µM). Furthermore, JB provoked significant increases in reactive oxygen species (ROS). Annexin V+ cell populations, dose-dependent accumulation of cleaved-PARP and caspase 3/7 activation, and reduced Bcl-2 and Mcl-1 expression confirm apoptosis induction. Preclinical in silico biopharmaceutical assessment of JB calculated rapid absorption and bioavailability >70%. Doses of 8-16 mg/kg JB were predicted to maintain unbound plasma concentrations >GI50 values in mice during efficacy studies. These findings advocate continued development of JB as a potential chemotherapeutic agent.

Natural products play a pivotal role in the treatment of cancer; identification of compounds such as taxanes and the vinca alkaloids were seminal landmarks in natural product drug discovery. Jerantinine A, a novel Aspidosperma alkaloid isolated from plant species Tabernaemontana corymbosa, was previously reported to possess cytotoxic activity against vincristine-resistant nasopharyngeal carcinoma cells and is therefore an ideal candidate for biological investigation. Furthermore, Tabernaemontana corymbosa, has been placed in the endangered list of threatened species by the International Union for Conservation of Nature thus making it a priority to elucidate the biological activity of this alkaloid. Herein, we report detailed biological evaluation of jerantinine A on various human-derived carcinoma cell lines. Our preliminary screens showed that significant inhibition of cell growth and colony formation accompanied time- and dose-dependent induction of apoptosis in human cancer cell lines after treatment with jerantinine A. Dose-dependent accumulations of cleaved PARP and caspase 3 further confirmed apoptosis. Profound G2/M cell cycle arrest was observed 24 h after treatment in all cell lines. Characteristics of mitotic arrest including inhibition of tubulin polymerisation, microtubule disruption, aneuploidy, and cyclin B1 down-regulation were clearly observed. The potent anti-proliferative, pro-apoptotic, and tubulin-destabilising activities of jerantinine A warrant further development of this molecule as a potential chemotherapeutic agent.

Precursor mRNA (pre-mRNA) splicing is catalyzed by a large ribonucleoprotein complex known as the spliceosome. Numerous studies have indicated that aberrant splicing patterns or mutations in spliceosome components, including the splicing factor 3b subunit 1 (SF3B1), are associated with hallmark cancer phenotypes. This has led to the identification and development of small molecules with spliceosome-modulating activity as potential anticancer agents. Jerantinine A (JA) is a novel indole alkaloid which displays potent anti-proliferative activities against human cancer cell lines by inhibiting tubulin polymerization and inducing G2/M cell cycle arrest. Using a combined pooled-genome wide shRNA library screen and global proteomic profiling, we showed that JA targets the spliceosome by up-regulating SF3B1 and SF3B3 protein in breast cancer cells. Notably, JA induced significant tumor-specific cell death and a significant increase in unspliced pre-mRNAs. In contrast, depletion of endogenous SF3B1 abrogated the apoptotic effects, but not the G2/M cell cycle arrest induced by JA. Further analyses showed that JA stabilizes endogenous SF3B1 protein in breast cancer cells and induced dissociation of the protein from the nucleosome complex. Together, these results demonstrate that JA exerts its antitumor activity by targeting SF3B1 and SF3B3 in addition to its reported targeting of tubulin polymerization.

Cancer stem cells (CSCs) represent rare tumor cell populations capable of self-renewal, differentiation, and tumor initiation and are highly resistant to chemotherapy and radiotherapy. Thus, therapeutic approaches that can effectively target CSCs and tumor cells could be the key to efficient tumor treatment. In this study, we explored the function of SPHK1 in breast CSCs and non-CSCs. We showed that RNAi-mediated knockdown of SPHK1 inhibited cell proliferation and induced apoptosis in both breast CSCs and non-CSCs, while ectopic expression of SPHK1 enhanced breast CSC survival and mammosphere forming efficiency. We identified STAT1 and IFN signaling as key regulatory targets of SPHK1 and demonstrated that an important mechanism by which SPHK1 promotes cancer cell survival is through the suppression of STAT1. We further demonstrated that SPHK1 inhibitors, FTY720 and PF543, synergized with doxorubicin in targeting both breast CSCs and non-CSCs. In conclusion, we provide important evidence that SPHK1 is a key regulator of cell survival and proliferation in breast CSCs and non-CSCs and is an attractive target for the design of future therapies.

BackgroundTo further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I.ResultsAs measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation.ConclusionsForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2561-z) contains supplementary material, which is available to authorized users.