Fisetin is a natural flavonol present in edible vegetables, fruits and wine at 2-160 microg/g concentrations and an ingredient in nutritional supplements with much higher concentrations. The compound has been reported to exert anticarcinogenic effects as well as antioxidant and anti-inflammatory activity via its ability to act as an inhibitor of cell proliferation and free radical scavenger, respectively. Our cell-based high-throughput screen for small molecules that override chemically induced mitotic arrest identified fisetin as an antimitotic compound. Fisetin rapidly compromised microtubule drug-induced mitotic block in a proteasome-dependent manner in several human cell lines. Moreover, in unperturbed human cancer cells fisetin caused premature initiation of chromosome segregation and exit from mitosis without normal cytokinesis. To understand the molecular mechanism behind these mitotic errors, we analyzed the consequences of fisetin treatment on the localization and phoshorylation of several mitotic proteins. Aurora B, Bub1, BubR1 and Cenp-F rapidly lost their kinetochore/centromere localization and others became dephosphorylated upon addition of fisetin to the culture medium. Finally, we identified Aurora B kinase as a novel direct target of fisetin. The activity of Aurora B was significantly reduced by fisetin in vitro and in cells, an effect that can explain the observed forced mitotic exit, failure of cytokinesis and decreased cell viability. In conclusion, our data propose that fisetin perturbs spindle checkpoint signaling, which may contribute to the antiproliferative effects of the compound.
The let-7 microRNA (miRNA) family has been implicated in the regulation of diverse cellular processes and disease pathogenesis. In cancer, loss-of-function of let-7 miRNAs has been linked to tumorigenesis via increased expression of target oncogenes. Excessive proliferation rate of tumor cells is often associated with deregulation of mitotic proteins.Here, we show that let-7b contributes to the maintenance of genomic balance via targeting Aurora B kinase, a key regulator of the spindle assembly checkpoint (SAC). Our results indicate that let-7b binds to Aurora B kinase 3 0 UTR reducing mRNA and protein expression of the kinase. In cells, excess let-7b induced mitotic defects characteristic to Aurora B perturbation including increased rate of polyploidy and multipolarity, and premature SAC inactivation that leads to forced exit from chemically induced mitotic arrest. Moreover, the frequency of aneuploid HCT-116 cells was significantly increased upon let-7b overexpression compared to controls. Interestingly, together with a chemical Aurora B inhibitor, let-7b had an additive effect on polyploidy induction in HeLa cells. In breast cancer patients, reduced let-7b expression was found to be associated with increased Aurora B expression in grade 3 tumors. Furthermore, let-7b was found downregulated in the most aggressive forms of breast cancer determined by clinicopathological parameters. Together, our findings suggest that let-7b contributes to the fidelity of cell division via regulation of Aurora B. Moreover, the loss of let-7b in aggressive tumors may drive tumorigenesis by Abbreviations: CIN, chromosomal instability; CPC, chromosomal passenger complex; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; HTS, high-throughput screen; miRNA, microRNA; SAC, spindle assembly checkpoint; TSB, target site blocker; UTR, untranslated region.
We explore cross-lingual transfer of register classification for web documents. Registers, that is, text varieties such as blogs or news are one of the primary predictors of linguistic variation and thus affect the automatic processing of language. We introduce two new registerannotated corpora, FreCORE and SweCORE, for French and Swedish. We demonstrate that deep pre-trained language models perform strongly in these languages and outperform previous state-of-the-art in English and Finnish. Specifically, we show 1) that zeroshot cross-lingual transfer from the large English CORE corpus can match or surpass previously published monolingual models, and 2) that lightweight monolingual classification requiring very little training data can reach or surpass our zero-shot performance. We further analyse classification results finding that certain registers continue to pose challenges in particular for cross-lingual transfer.
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