Recent studies have demonstrated that specific miRNAs, such as miR-221/222, may be responsible for tamoxifen resistance in breast cancer. Secreted miRNAs enclosed in exosomes can act as intercellular bio-messengers. Our objective is to investigate the role of secreted miR-221/222 in tamoxifen resistance of ER-positive breast cancer cells. Transmission electron microscopy analysis and nanoparticle tracking analysis were performed to determine the exosomes difference between MCF-7(TamR) (tamoxifen resistant) and MCF-7(wt) (tamoxifen sensitive) cells. PKH67 fluorescent labeling assay was used to detect exosomes derived from MCF-7(TamR) cells entering into MCF-7(wt) cells. The potential function of exosomes on tamoxifen resistance transmission was analyzed with cell viability, apoptosis ,and colony formation. MiRNA microarrays and qPCR were used to detect and compare the miRNAs expression levels in the two cells and exosomes. As the targets of miR-221/222, p27 and ERα were analyzed with western blot and qPCR. Compared with the MCF-7(wt) exosomes, there were significant differences in the concentration and size distribution of MCF-7(TamR) exosomes. MCF-7(wt) cells had an increased amount of exosomal RNA and proteins compared with MCF-7(TamR) cells. MCF-7(TamR) exosomes could enter into MCF-7(wt) cells, and then released miR-221/222. And the elevated miR-221/222 effectively reduced the target genes expression of P27 and ERα, which enhanced tamoxifen resistance in recipient cells. Our results are the first to show that secreted miR-221/222 serves as signaling molecules to mediate communication of tamoxifen resistance.

The novel coronavirus (SARS-CoV-2/ 2019-nCoV) identified in Wuhan, China, in December 2019 has caused great damage to public health and economy worldwide with over 140,000 infected cases up to date. Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus. Here, we examine the relationships of meteorological variables with the severity of the outbreak on a worldwide scale. The confirmed case counts, which indicates the severity of COVID-19 spread, and four meteorological variables, i.e., air temperature, relative humidity, wind speed, and visibility, were : medRxiv preprint all over China, 21 cities/ provinces in Italy, 21 cities/ provinces in Japan, and 51 other countries around the world. Four different time delays of weather (on the day, 3 days ago, 7 days ago, and 14 days ago) as to the epidemic situation were taken for modeling and we finally chose the weather two weeks ago to model against the daily epidemic situation as its correlated with the outbreak best. Taken Chinese cities as a discovery dataset, it was suggested that temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r 2 >0.6) with the real data. Using this model, we further predicted the possible epidemic situation in the future 12 days in several high-latitude cities with potential outbreak. This model could provide more information for government's future decisions on COVID-19 outbreak control.

The Notch pathway is functionally important in breast cancer. Notch-1 has been reported to maintain an estrogen-independent phenotype in estrogen receptor α (ERα)+ breast cancer cells. Notch-4 expression correlates with Ki67. Notch-4 also plays a key role in breast cancer stem-like cells. Estrogen-independent breast cancer cell lines have higher Notch activity than estrogen-dependent lines. Protein kinase Cα (PKCα) overexpression is common in endocrine-resistant breast cancers and promotes tamoxifen (TAM)-resistant growth in breast cancer cell lines. We tested whether PKCα overexpression affects Notch activity and whether Notch signaling contributes to endocrine resistance in PKCα-overexpressing breast cancer cells.Analysis of published microarray data from ERα+ breast carcinomas shows that PKCα expression correlates strongly with Notch-4. Real-time reverse transcription PCR and immunohistochemistry on archival specimens confirmed this finding. In a PKCα-overexpressing, TAM-resistant T47D model, PKCα selectively increases Notch-4, but not Notch-1, expression in vitro and in vivo. This effect is mediated by activator protein-1 (AP-1) occupancy of the Notch-4 promoter. Notch-4 knockdown inhibits estrogen-independent growth of PKCα-overexpressing T47D cells, whereas Notch-4IC expression stimulates it. Gene expression profiling shows that multiple genes and pathways associated with endocrine resistance are induced in Notch-4IC- and PKCα-expressing T47D cells. In PKCα-overexpressing T47D xenografts, an orally active γ-secretase inhibitor at clinically relevant doses significantly decreased estrogen-independent tumor growth, alone and in combination with TAM. In conclusion, PKCα overexpression induces Notch-4 through AP-1. Notch-4 promotes estrogen-independent, TAM-resistant growth and activates multiple pathways connected with endocrine resistance and chemoresistance. Notch inhibitors should be clinically evaluated in PKCα- and Notch-4-overexpressing, endocrine-resistant breast cancers.

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HER2-overexpressing breast cancers often show hyperactivation of the HER2/AKT/mTOR signaling pathway. Lapatinib is an oral dual tyrosine kinase inhibitor (TKI) that targets both EGFR and HER2 to inhibit the proliferation of breast cancer cells. However, it is obscure whether and how lapatinib could induce autophagy in breast cancer cells, an important cell response with drug treatment. In this study, we investigated the apoptosis and the autophagy in the HER2-overexpressing breast cancer cells BT474 and AU565 treated with lapatinib, and further examined their relationship. Lapatinib inhibited the proliferation and the rate of DNA synthesis in HER2-positive cells, as observed by MTT, colony formation and EDU assays. Lapatinib not only induced apoptosis accompanied by an increased expression of cleaved Caspase-3 and cleaved PARP, but it also induced autophagy in vitro, as confirmed by electron microscopy (EM), acridine orange (AO) staining and LC3-II expression. Meanwhile, lapatinib inhibited the phosphorylation of HER2, AKT, mTOR, and p70S6K, whereas that of AMPK was activated. When the cells were pre-incubated with 3-Methyladenine (3-MA), the specific autophagy inhibitor, the growth inhibitory ratio and apoptosis rate were frustrated, whereas colony formation and DNA synthesis ability were encouraged. In addition, 3-MA application could up-regulate Caspase-3 and PARP expression, compared with the treatment with lapatinib alone. The addition of 3-MA could attenuate the inhibitory role on HER2/AKT/mTOR pathway and the active role on AMPK that was raised by lapatinib. Therefore, lapatinib simultaneously induced both apoptosis and autophagy in the BT474 and AU565 cells, and in these settings, autophagy facilitates apoptosis.

ObjectivesThis study aims to investigate the relationship between daily weather and transmission rate of SARS-CoV-2, and to develop a generalised model for future prediction of the COVID-19 spreading rate for a certain area with meteorological factors.DesignA retrospective, qualitative study.Methods and analysisWe collected 382 596 records of weather data with four meteorological factors, namely, average temperature, relative humidity, wind speed, and air visibility, and 15 192 records of epidemic data with daily new confirmed case counts (1 587 209 confirmed cases in total) in nearly 500 areas worldwide from 20 January 2020 to 9 April 2020. Epidemic data were modelled against weather data to find a model that could best predict the future outbreak.ResultsSignificant correlation of the daily new confirmed case count with the weather 3 to 7 days ago were found. SARS-CoV-2 is easy to spread under weather conditions of average temperature at 5 to 15°C, relative humidity at 70% to 80%, wind speed at 1.5 to 4.5 m/s and air visibility less than 10 statute miles. A short-term model with these four meteorological variables was derived to predict the daily increase in COVID-19 cases; and a long-term model using temperature to predict the pandemic in the next week to month was derived. Taken China as a discovery dataset, it was well validated with worldwide data. According to this model, there are five viral transmission patterns, ‘restricted’, ‘controlled’, ‘natural’, ‘tropical’ and ‘southern’. This model’s prediction performance correlates with actual observations best (over 0.9 correlation coefficient) under natural spread mode of SARS-CoV-2 when there is not much human interference such as epidemic control.ConclusionsThis model can be used for prediction of the future outbreak, and illustrating the effect of epidemic control for a certain area.

High mtDNA content is a potential effective factor of poor prognosis in patients with advanced stage colon cancer.