Biology subjects

Okuno, Y.

Publications and source records attributed to Okuno, Y..

22 records · Page 2Linked to original sources

bioRxiv2020

Direct evidence of abortive lytic infection-mediated establishment of Epstein-Barr virus latency during B-cell infection

Viral infection induces dynamic changes in transcriptional profiles. Virus-induced and anti-viral responses are intertwined during the infection. Epstein-Barr virus (EBV) is a human gammaherpesvirus that provides a model of herpesvirus latency. To measure the transcriptome changes during the establishment of EBV latency, EBV-negative Akata cells were infected with EBV-EGFP and observed by transcriptome sequencing (RNA-seq) at 0, 2, 4, 7, 10, and 14 days after infection. We found transient downregulation of mitotic division-related genes, reflecting reprograming of cell growth by EBV. Moreover, a burst of viral lytic gene expression was detected in the early phase of infection. Experimental and mathematical investigations demonstrated that infectious virions were not produced in the pre-latent phase, suggesting the presence of an abortive lytic infection. Finally, we conducted fate mapping using recombinant EBV, enabling the noninvasive, continuous observation of infected cells during EBV infection. Our tracking analysis provided direct evidence that the abortive lytic infection in the pre-latent phase converges to latent infection during EBV infection of B-cells, shedding light on novel roles of viral lytic gene(s) in establishing latency. Author summaryViral infection is a complex process that activates both virus-triggered and host anti-viral responses. This process has classically been studied by snapshot analysis such as microarray and RNA-seq at discrete time points as population averages. Snapshot data lead to invaluable findings in host-pathogen interactions. However, these "snapshot" omics, even from a single cell, lack temporal resolution. Because the behavior of infected cells is highly dynamic and heterogenous, continuous analysis is required for deciphering the fate of infected cells during viral infection. Here, we exploited fate mapping techniques with recombinant Epstein-Barr virus (EBV) to track the infected cells and recorded a log of lytic gene expression during EBV infection. Our continuous observation of infected cells revealed that EBV established latency in B-cells via an abortive lytic infection in the pre-latent phase.

microbiology↗

bioRxiv2020

Extraction of Protein Dynamics Information Hidden in Cryo-EM Map Using Deep Learning

Technical breakthroughs in cryogenic electron microscopy (cryo-EM)-based single-particle analysis have enabled the structures of numerous proteins to be solved at atomic or near-atomic resolutions, including extremely large macromolecules whose structures could not be solved by conventional techniques. Determining the dynamics properties of these macromolecules, based on their solved structures, can further improve our understanding of their functional mechanisms. However, such analysis is often hampered by the large molecular size and complex structural assembly, making both experimental and computational approaches to determine dynamics properties challenging. Here, we report a deep learning-based approach, DEFMap, to extract the dynamics information "hidden" in cryo-EM density maps. By relying only on cryo-EM maps, DEFMap successfully provided dynamics information equivalent to that determined from molecular dynamics (MD) simulations and experimental approaches at the atomic and residue levels. Additionally, DEFMap could detect dynamics changes associated with molecular recognition and the accompanying allosteric conformational stabilizations, which trigger various biological events such as signal transduction and enzyme catalysis. This approach will provide new insights into the functional mechanisms of biological molecules, accelerating modern molecular biology researches. Furthermore, this advanced strategy combining experimental data, deep learning approaches, and MD simulations would open a new multidisciplinary science area

molecular biology↗

bioRxiv2018

Prospective randomized controlled study directly comparing tadalafil and tamsulosin for male patients with lower urinary tract symptoms.

Lower urinary tract symptoms are widespread in elderly men and often suggestive of benign prostatic hyperplasia (LUTS/BPH). A randomized, prospective, and open-labeled trial directly comparing the effects of tadalafil (a phosphodiesterase 5 inhibitor) 5 mg once daily and tamsulosin (an 1-blocker) 0.2 mg once daily for 12 weeks in LUTS/BPH patients was conducted. Data were recorded before randomization as well as at 4, 8, and 12 weeks after medication. Fifteen patients allocated tadalafil and 20 allocated tamsulosin completed 12 weeks of medication. Total IPSS, IPSS voiding, and IPSS-QOL scores declined with medication, but there was no difference between drugs. IPSS storage scores reduced more in the tamsulosin group than tadalafil group. OABSS did not decline significantly with medication. IIEF5 was maintained in the tadalafil group, but declined in the tamsulosin group. The maximum flow rate and post-void residual urine volume did not significantly change with medication. Daytime, night-time, and 24-hour urinary frequencies as well as the mean and largest daytime, night-time, and 24-hour voiding volumes per void did not significantly change with medication. In conclusion, tamsulosin was more effective to reduce storage symptoms than tadalafil. Tadalafil had the advantage of maintaining the erectile function.

clinical trials↗

bioRxiv2018

Prediction of prostate cancer by deep learning with multilayer artificial neural network.

ObjectivesTo predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning utilizing a multilayer artificial neural network was investigated.\n\nMaterials and methodsA total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography-guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis were input into the constructed multilayer artificial neural network (ANN) programs. 232 patients were used as training cases of ANN programs, and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.\n\nResultsWith any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and 9 explanatory variables from 22, respectively. In common between them, age at biopsy, findings on digital rectal examination, findings in the peripheral zone on MRI diffusion-weighted imaging, and body mass index were positively influential variables, while numbers of previous prostatic biopsy and prostate volume were negatively influential. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared with that with logistic regression analysis (LR). The AUCs with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise logistic regression compared with the AUC with LR. The ANN had a higher net-benefit than LR between prostate cancer probability cut-off values of 0.38 and 0.6.\n\nConclusionANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

bioinformatics↗