RESUMO
SARS-CoV-2 infection has caused a major global burden. Despite intensive research, the mechanism and dynamics of early viral replication are not completely understood including the kinetics of formation of plus stranded genomic and subgenomic RNAs (gRNA and sgRNA) starting from the RNA from the first virus that enters the cell. We employed single-molecule RNA-fluorescence in situ hybridization (smRNA-FISH) to simultaneously detect viral gRNA and sgRNA in infected cells and carried out a time course analysis to determine the kinetics of their replication. We visualized the single molecules of gRNA within the cytoplasm of infected cells 30 minutes post-infection and detected the co-expression of gRNA and sgRNA within two hours post-infection. Furthermore, we observed the formation of a replication organelle (RO) from a single RNA, which led to the formation of multiple ROs within the same cells. Single molecule analysis indicated that while gRNA resided in the center of these ROs, the sgRNAs were found to radiate and migrate out of these structures. Our results also indicated that after the initial delay, there was a rapid but asynchronous replication, and the gRNA and sgRNAs dispersed throughout the cell within 4-5 hours post-infection forming multiple ROs that filled the entire cytoplasm. These results provide insight into the kinetics of early post-entry events of SARS-CoV-2 and the formation of RO, which will help to understand the molecular events associated with viral infection and facilitate the identification of new therapeutic targets that can curb the virus at a very early stage of replication to combat COVID-19. Author SummarySARS-CoV-2 infection continues to be a global burden. Soon after the entry, SARS-CoV-2 replicates by an elaborate process, producing genomic and subgenomic RNAs (gRNA and sgRNAs) within specialized structures called replication organelles (RO). Many questions including the timing of multiplication of gRNA and sgRNA, the generation, subcellular localization, and function of the ROs, and the mechanism of vRNA synthesis within ROs is not completely understood. Here, we have developed probes and methods to simultaneously detect the viral gRNA and a sgRNA at single cell single molecule resolution and have employed a method to scan thousands of cells to visualize the early kinetics of gRNA and sgRNA synthesis soon after the viral entry into the cell. Our results reveal that the replication is asynchronous and ROs are rapidly formed from a single RNA that enters the cell within 2 hours, which multiply to fill the entire cell cytoplasm within ~4 hours after infection. Furthermore, our studies provide a first glimpse of the gRNA and sgRNA synthesis within ROs at single molecule resolution. Our studies may facilitate the development of drugs that inhibit the virus at the earliest possible stages of replication to minimize the pathogenic impact of viral infection.
RESUMO
Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust prediction of cloud services based on the multi-faceted Quality of Service (QoS) attributes is a challenging task due to the complicated and non-linear relationships between the QoS values and the corresponding trust result. Recent research works reveal the significance of Artificial Neural Network (ANN) and its variants in providing a reasonable degree of success in trust prediction problems. However, the challenges with respect to weight assignment, training time and kernel functions make ANN and its variants under continuous advancements. Hence, this work presents a novel multi-level Hypergraph Coarsening based Robust Heteroscedastic Probabilistic Neural Network (HC-RHRPNN) to predict trustworthiness of cloud services to build high-quality service applications. HC-RHRPNN employs hypergraph coarsening to identify the informative samples, which were then used to train HRPNN to improve its prediction accuracy and minimize the runtime. The performance of HC-RHRPNN was evaluated using Quality of Web Service (QWS) dataset, a public QoS dataset in terms of classifier accuracy, precision, recall, and F-Score.
