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Human induced pluripotent stem cells (iPSCs) provide a valuable model for regenerative medicine and human disease research. To date, however, the reprogramming efficiency of human adult cells is still low. Recent studies have revealed that cell cycle is a key parameter driving epigenetic reprogramming to pluripotency. As is well known, retroviruses such as the Moloney murine leukemia virus (MoMLV) require cell division to integrate into the host genome and replicate, whereas the target primary cells for reprogramming are a mixture of several cell types with different cell cycle rhythms. Whether cell cycle synchronization has potential effect on retrovirus induced reprogramming has not been detailed. In this study, utilizing transient serum starvation induced synchronization, we demonstrated that starvation generated a reversible cell cycle arrest and synchronously progressed through G2/M phase after release, substantially improving retroviral infection efficiency. Interestingly, synchronized human dermal fibroblasts (HDF) and adipose stem cells (ASC) exhibited more homogenous epithelial morphology than normal FBS control after infection, and the expression of epithelial markers such as E-cadherin and Epcam were strongly activated. Futhermore, synchronization treatment ultimately improved Nanog positive clones, achieved a 15–20 fold increase. These results suggested that cell cycle synchronization promotes the mesenchymal to epithelial transition (MET) and facilitates retrovirus mediated reprogramming. Our study, utilization of serum starvation rather than additional chemicals, provide a new insight into cell cycle regulation and induced reprogramming of human cells.

The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model to Sus scrofa, which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies in Arabidopsis thaliana and Oryza sativa. Additionally, to facilitate the use of the model, we present ISwine (http://iswine.iomics.pro/), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future.

Rice is an important staple crop throughout the world, but environmental stress like low-light conditions can negatively impact crop yield and quality. Using pot experiments and field experiments, we studied the effects of shading on starch pasting viscosity and starch content with six rice varieties for three years, using the Rapid Visco Analyser to measure starch pasting viscosity. Shading at different growth stages and in different rice varieties all affected the starch pasting characteristics of rice. The effects of shading on starch pasting viscosity at middle and later growth stages were greater than those at earlier stages. Shading enhanced breakdown but reduced hold viscosity and setback at tillering-elongation stage. Most pasting parameters changed significantly with shading after elongation stage. Furthermore, the responses of different varieties to shading differed markedly. The change scope of starch pasting viscosity in Dexiang 4103 was rather small after heading, while that in IIyou 498 and Gangyou 906 was small before heading. We observed clear tendencies in peak viscosity, breakdown, and pasting temperature of the five rice varieties with shading in 2010 and 2011. Correlation analysis indicated that the rice amylose content was negatively correlated with breakdown, but was positively correlated with setback. Based on our results, IIyou 498, Gangyou 906, and Dexiang 4103 had higher shade endurance, making these varieties most suitable for high-quality rice cultivation in low-light regions.

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