Inhibitors of Phosphatidylinositol 3′-Kinases Promote Mitotic Cell Death in HeLa Cells

The phosphatidylinositol 3-kinase (PI3K) pathway plays an important role in many biological processes, including cell cycle progression, cell growth, survival, actin rearrangement and migration, and intracellular vesicular transport. However, the involvement of the PI3K pathway in the regulation of mitotic cell death remains unclear. In this study, we treated HeLa cells with the PI3K inhibitors, 3-methyladenine (3-MA, as well as a widely used autophagy inhibitor) and wortmannin to examine their effects on cell fates using live cell imaging. Treatment with 3-MA decreased cell viability in a time- and dose-dependent manner and was associated with caspase-3 activation. Interestingly, 3-MA-induced cell death was not affected by RNA interference-mediated knockdown (KD) of beclin1 (an essential protein for autophagy) in HeLa cells, or by deletion of atg5 (an essential autophagy gene) in mouse embryonic fibroblasts (MEFs). These data indicate that cell death induced by 3-MA occurs independently of its ability to inhibit autophagy. The results from live cell imaging studies showed that the inhibition of PI3Ks increased the occurrence of lagging chromosomes and cell cycle arrest and cell death in prometaphase. Furthermore, PI3K inhibitors promoted nocodazole-induced mitotic cell death and reduced mitotic slippage. Overexpression of Akt (the downstream target of PI3K) antagonized PI3K inhibitor-induced mitotic cell death and promoted nocodazole-induced mitotic slippage. These results suggest a novel role for the PI3K pathway in regulating mitotic progression and preventing mitotic cell death and provide justification for the use of PI3K inhibitors in combination with anti-mitotic drugs to combat cancer

Tetraploid cells from cytokinesis failure induce aneuploidy and spontaneous transformation of mouse ovarian surface epithelial cells

Most ovarian cancers originate from the ovarian surface epithelium and are characterized by aneuploid karyotypes. Aneuploidy, a consequence of chromosome instability, is an early event during the development of ovarian cancers. However, how aneuploid cells are evolved from normal diploid cells in ovarian cancers remains unknown. In the present study, cytogenetic analyses of a mouse syngeneic ovarian cancer model revealed that diploid mouse ovarian surface epithelial cells (MOSECs) experienced an intermediate tetraploid cell stage, before evolving to aneuploid (mainly near-tetraploid) cells. Using long-term live-cell imaging followed by fluorescence in situ hybridization (FISH), we demonstrated that tetraploid cells originally arose from cytokinesis failure of bipolar mitosis in diploid cells, and gave rise to aneuploid cells through chromosome mis-segregation during both bipolar and multipolar mitoses. Injection of the late passage aneuploid MOSECs resulted in tumor formation in C57BL/6 mice. Therefore, we reveal a pathway for the evolution of diploid to aneuploid MOSECs and elucidate a mechanism for the development of near-tetraploid ovarian cancer cells

SpermatogenesisOnline 1.0:a resource for spermatogenesis based on manual literature curation and genome-wide data mining

Human infertility affects 10–15% of couples, half of which is attributed to the male partner. Abnormal spermatogenesis is a major cause of male infertility. Characterizing the genes involved in spermatogenesis is fundamental to understand the mechanisms underlying this biological process and in developing treatments for male infertility. Although many genes have been implicated in spermatogenesis, no dedicated bioinformatic resource for spermatogenesis is available. We have developed such a database, SpermatogenesisOnline 1.0 (http://mcg.ustc.edu.cn/sdap1/spermgenes/), using manual curation from 30 233 articles published before 1 May 2012. It provides detailed information for 1666 genes reported to participate in spermatogenesis in 37 organisms. Based on the analysis of these genes, we developed an algorithm, Greed AUC Stepwise (GAS) model, which predicted 762 genes to participate in spermatogenesis (GAS probability >0.5) based on genome-wide transcriptional data in Mus musculus testis from the ArrayExpress database. These predicted and experimentally verified genes were annotated, with several identical spermatogenesis-related GO terms being enriched for both classes. Furthermore, protein–protein interaction analysis indicates direct interactions of predicted genes with the experimentally verified ones, which supports the reliability of GAS. The strategy (manual curation and data mining) used to develop SpermatogenesisOnline 1.0 can be easily extended to other biological processes

Regulation of Asymmetrical Cytokinesis by cAMP during Meiosis I in Mouse Oocytes

Mammalian oocytes undergo an asymmetrical first meiotic division, extruding half of their chromosomes in a small polar body to preserve maternal resources for embryonic development. To divide asymmetrically, mammalian oocytes relocate chromosomes from the center of the cell to the cortex, but little is known about the underlying mechanisms. Here, we show that upon the elevation of intracellular cAMP level, mouse oocytes produced two daughter cells with similar sizes. This symmetrical cell division could be rescued by the inhibition of PKA, a cAMP-dependent protein kinase. Live cell imaging revealed that a symmetrically localized cleavage furrow resulted in symmetrical cell division. Detailed analyses demonstrated that symmetrically localized cleavage furrows were caused by the inappropriate central positioning of chromosome clusters at anaphase onset, indicating that chromosome cluster migration was impaired. Notably, high intracellular cAMP reduced myosin II activity, and the microinjection of phospho-myosin II antibody into the oocytes impeded chromosome migration and promoted symmetrical cell division. Our results support the hypothesis that cAMP plays a role in regulating asymmetrical cell division by modulating myosin II activity during mouse oocyte meiosis I, providing a novel insight into the regulation of female gamete formation in mammals

New principle of busbar protection based on a fundamental frequency polarity comparison.

To overcome the contradiction between speed and reliability in existing busbar protection schemes, a new busbar protection algorithm based on a polarity comparison of fundamental frequency currents is proposed. The algorithm extracts the fundamental frequency components of the fault reference current and the virtual current through a wavelet transform. The angle between the two currents is used to characterize the polarity relationship. The polarities of the virtual current and the reference current are the same when an internal fault occurs, and the angle will be small. The polarities of the two currents are opposite for an external fault, in which case the angle is larger. By analysing the variation characteristics of the angle between faults inside and outside busbar, a protection criterion is established, and the fault area is determined. In simulation results based on PSCAD/EMTDC, the algorithm can quickly and reliably identify the faults inside and outside the busbar area, and its performance is not affected by the initial fault angle, fault resistance, fault type or capacitor voltage transformer (CVT) transmission characteristics

A new method for identifying a fault in T-connected lines based on multiscale S-transform energy entropy and an extreme learning machine.

Due to the characteristics of T-connection transmission lines, a new method for T-connection transmission lines fault identification based on current reverse travelling wave multi-scale S-transformation energy entropy and limit learning machine is proposed. S-transform are implemented on the faulty reverse traveling waves measured by each traveling wave protection unit of the T-connection transmission line, the reverse travelling wave energy entropies under eight different frequencies are respectively calculated, and a T-connection transmission line fault characteristic vector sample set are thus formed. Establish an intelligent fault identification model of extreme learning machines, and use the sample set for training and testing to identify the specific faulty branch of the T-connection transmission line. The simulation results show that the proposed algorithm can accurately and quickly identify the branch where the fault is located on the T-connection transmission line under various operation conditions

A novel intelligent fault identification method based on random forests for HVDC transmission lines.

In order to remedy the current problem of having been buffeted by competing requirements for both protection sensitivity and quick reaction of High Voltage Direct Current (HVDC) transmission lines simultaneously, a new intelligent fault identification method based on Random Forests (RF) for HVDC transmission lines is proposed. S transform is implemented to extract fault current traveling wave of 8 frequencies and calculate the fluctuation index and energy sum ratio, in which the wave index is used to identify internal and external faults, and energy sum ratio is used to identify the positive and negative pole faults occurred on the transmission line. The intelligent fault identification model of RF is established, and the fault characteristic sample set of HVDC transmission lines is constructed by using multi-scale S transform fluctuation index and multi-scale S-transform energy sum ratio. Training and testing have been carried out to identify HVDC transmission line faults. According to theoretical researches and a large number of results of simulation experiments, the proposed intelligent fault identification method based on RF for HVDC transmission lines can effectively solve the problem of protection failure caused by inaccurate identification of traditional traveling wave wavefront or wavefront data loss. It can accurately and quickly realize the identification of internal and external faults and the selection of fault poles under different fault distances and transitional resistances, and has a strong ability to withstand transitional resistance and a strong ability to resist interference

Different network algorithm fault diagnosis experiment results.

Different network algorithm fault diagnosis experiment results.</p

Model recognition accuracy comparison.

Model recognition accuracy comparison.</p

Comparison chart of classification results.

Comparison chart of classification results.</p