Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA sequences and histone protein modifications. However, the prediction of enhancers based on the locally conserved DNA sequence and similar histone modification features is still unknown. Here, the present study proposed a convolutional neural network (CNN) model to predict enhancers that can regulate gene expression during spermatogenesis. RESULTS: we have obtained a positive set of enhancers using the P300 locus, verified by experiments, while a negative set was constructed using the promoter as a non-enhancer locus. The model was trained on all types of specific cells during spermatogenesis independently, and the transfer learning strategy was used to fine-tune the model based on which the model can be trained and adapted to other cells quickly. We visualized the convolution layer of the trained model and aligned the predicted enhancer with the JASPAR database. The results showed that the model was highly matched with some important transcription factors during spermatogenesis, signifying the reliability of the model. Finally, we compared the CNN algorithm with the gkmSVM algorithm (Support Vector Machine). It is well known that CNN has better performance than the gkmSVM algorithm, especially in the generalization ability. Our work demonstrated their strong learning ability and the low CPU requirements for the experiment, with a small number of convolution layers and simple network structure, while avoiding overfitting the training data. At the end of the experiment, we used the trained model to build an enhancer recognition website for further research and communication.

Lycium barbarum polysaccharide alleviates IL-1ß-evoked chondrogenic ATDC5 cell inflammatory injury through mediation of microRNA-124.

Background: Lycium barbarum polysaccharide (LBP) is a promising therapeutic drug in inflammation-related injuries, nevertheless the mechanism of LBP's action is still elusive. The study is to explore the effect of LBP on IL-1ß-evoked ATDC5 cell inflammatory injury. Methods: ATDC5 cells were administrated with 10 ng/mL interleukin (IL)-1ß to establish an in vitro model of cartilage damage. After management, cell viability was tested through CCK8 assay. The pro-inflammatory cytokines and cyclooxygenase (Cox)-2 were assessed through ELISA, western blot and qRT-PCR. MiR-124 expression in ATDC5 cells was silenced by transfecting with miR-124 inhibitor, and the pro-inflammatory cytokines and Cox-2 were re-assessed. NF-κB and JNK pathways were measured through western blot. Results: IL-1ß stimulation accelerated the release of IL-1ß, IL-6 and TNF-α, elevated Cox-2 expression. LBP significantly eased IL-1ß-induced inflammation. MiR-124 expression was observed to enhance by LBP, and the impacts of LBP on ATDC5 cells were lightened via transfection with miR-124 inhibitor. NF-κB and JNK pathways were activated after IL-1ß stimulation, nevertheless were inactivated by LBP administration. Besides, LBP-evoked the repression of NF-κB and JNK pathways were overturned by miR-124 inhibitor. Conclusions: Our study suggests that LBP protects ATDC5 cells from IL-1ß-evoked injury through up-regulating miR-124 via blocking NF-κB and JNK pathways.