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Biology subjects

Zhai, M.

Publications and source records attributed to Zhai, M..

2 recordsLinked to original sources
bioRxiv2021

DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.

Hepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. We trained and tested DeepHBV using the HBV integration sites data from dsVIS database. Initially, DeepHBV showed AUROC of 0.6363 and AUPR of 0.5471 on the dataset. Adding repeat peaks and TCGA Pan Cancer peaks can significantly improve the model performance, with an AUROC of 0.8378 and 0.9430 and an AUPR of 0.7535 and 0.9310, respectively. On independent validation dataset of HBV integration sites from VISDB, DeepHBV with HBV integration sequences plus TCGA Pan Cancer (AUROC of 0.7603 and AUPR of 0.6189) performed better than HBV integration sequences plus repeat peaks (AUROC of 0.6657 and AUPR of 0.5737). Next, we found the transcriptional factor binding sites (TFBS) were significantly enriched near genomic positions that were paid attention to by convolution neural network. The binding sites of AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra and Foxo3 were highlighted by DeepHBV attention mechanism in both dsVIS dataset and VISDB dataset, revealing the HBV integration preference. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for further mechanism study of HBV induced cancer. Author summaryHepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. The performance of DeepHBV model significantly improves after adding genomic features, with an AUROC of 0.9430 and an AUPR of 0.9310. Furthermore, we enriched the transcriptional factor binding sites of proteins by convolution neural network. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for the further study of HBV integration mechanism.

bioinformatics↗
bioRxiv2020

Pulsed Electromagnetic Fields Alleviates Hepatic Oxidative Stress and Lipids Accumulation in db/db mice

PurposeNonalcoholic fatty liver disease (NAFLD), affected more than 70 % of patients with type 2 diabetes (T2DM), has become a common metabolic liver disease worldwide. However, the specifically treatments targeting NAFLD have not been found until now. Pulsed electromagnetic fields have positive effects on multiple diseases. However, the effects of PEMF on NAFLD in T2DM require further investigation. The present study assessed the effects of pulsed electromagnetic fields on the liver oxidative stress and lipid accumulation of db/db mice. Patients and methodsAnimals were exposed to 2 h of pulsed electromagnetic fields (15.38 Hz, 2 mT) or sham stimulated, and thereafter sacrificed at 8 weeks later. The biomarkers of oxidative stress, such as MDA, GSSG and GSH levels, were analysed with commercial kits. The activity of liver antioxidant enzymes as CAT, SOD and GSH-Px was detected. Hepatic expressions of CAT, GR, GSH-Px, SOD1, SOD2 and SREBP-1c at protein levels were determined with Western blotting. Hepatic weight was measured and triglyceride accumulation were visualized by Oil Red O staining. ResultsPEMF exposure could protect the liver from oxidative stress injury by decreasing MDA and GSSG level, promoting reduced GSH level, and increasing GSH-Px activity and expression in comparison with sham group. But CAT and SOD activity have no statistic difference as same as CAT, GR, SOD1 and SOD2 expression. Furthermore, PEMF exposure reduced liver weight and triglyceride content. Meanwhile, PEMF exposure ameliorated hepatic steatosis through reducing the expression of SREBP-1c to regulate the lipid synthesis. ConclusionThe present study provides evidence that PEMF could increase antioxidant enzymes activity and alleviate lipid accumulation in fatty liver. This implies that PEMF exposure has beneficial effects for the treatment of NAFLD in accompany with T2DM.

bioengineering↗