<i>In-silico</i>analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Periwal, Neha; Sarma, Sankritya; Arora, Pooja; Sood, Vikas

doi:10.1101/2020.03.31.018499

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Besides the proteins, the genome of the coronavirus also encodes non-coding RNAs (ncRNAs). An N gene small ncRNA inhibition has been associated with reduced inflammation (40). These data are in line with the hypothesis stating that SARS-CoV-2 may potentially cross the placenta (Figure 3).…”

Section: Hormonal and Immune Protection Of The Placentasupporting

confidence: 90%

Investigation of SARS-CoV-2 Ability to Pass Through the Placenta

et al. 2021

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Context: Recently, a new coronavirus (SARS-CoV-2) as the cause of COVID-19 has made a global health crisis and a great challenge. Pregnant women and fetuses are among the high-risk groups for COVID-19. In this review, we summarize studies regarding SARS-CoV-2 virus-placenta interactions at the maternal-fetal interface by demonstrating the pathogenicity of the virus and defense methods of the placenta. Evidence Acquisition: In the present study, a search was done in domestic and international databases including Google Scholar, Web of Science, PubMed, and Scopus using specific keywords (“Coronavirus” OR “COVID-19” OR “SARS-CoV-2”) AND (“Fetus” OR “Placenta”) AND (“Pregnancy”), limited until August 2020. Finally, we reviewed 250 articles. Results: Generally, the pathogenicity and the life cycle of SARS-CoV-2 and virus entry and replication methods allow the virus to pass through the placenta, although there are hormonal and immune barriers in the placenta against SARS-CoV-2, such as placental type interferons. Conclusions: The SARS-CoV-2 can pass through the placenta, but there are defense methods against it.

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Section: Hormonal and Immune Protection Of The Placentasupporting

confidence: 90%

Investigation of SARS-CoV-2 Ability to Pass Through the Placenta

et al. 2021

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“…Treatment of SARS-CoV-infected mice with small viral RNA inhibitors resulted in reduced infection severity, lower levels of inflammation, and higher mRNA expression of antiviral proteins. To date, there have been no confirmed SARS-CoV-2-encoded miRNAs, although several studies predict the existence of such virally-encoded miRNAs [ 82 , 83 ]. It will be important to determine in future studies whether SARS-CoV-2 encodes miRNAs capable of altering viral infectivity and whether they are candidates for therapeutic intervention.…”

Section: Discussionmentioning

confidence: 99%

Computational Analysis of Targeting SARS-CoV-2, Viral Entry Proteins ACE2 and TMPRSS2, and Interferon Genes by Host MicroRNAs

Pierce

Simion

Icli

et al. 2020

Genes

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Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has led to a global pandemic, failures of local health care systems, and global economic recession. MicroRNAs (miRNAs) have recently emerged as important regulators of viral pathogenesis, particularly among RNA viruses, but the impact of host miRNAs on SARS-CoV-2 infectivity remains unknown. In this study, we utilize the combination of powerful bioinformatic prediction algorithms and miRNA profiling to predict endogenous host miRNAs that may play important roles in regulating SARS-CoV-2 infectivity. We provide a collection of high-probability miRNA binding sites within the SARS-CoV-2 genome as well as within mRNA transcripts of critical viral entry proteins ACE2 and TMPRSS2 and their upstream modulators, the interferons (IFN). By utilizing miRNA profiling datasets of SARS-CoV-2-resistant and -susceptible cell lines, we verify the biological plausibility of the predicted miRNA–target RNA interactions. Finally, we utilize miRNA profiling of SARS-CoV-2-infected cells to identify predicted miRNAs that are differentially regulated in infected cells. In particular, we identify predicted miRNA binders to SARS-CoV-2 ORFs (miR-23a (1ab), miR-29a, -29c (1ab, N), miR-151a, -151b (S), miR-4707-3p (S), miR-298 (5′-UTR), miR-7851-3p (5′-UTR), miR-8075 (5′-UTR)), ACE2 3′-UTR (miR-9-5p, miR-218-5p), TMPRSS2 3′-UTR (let-7d-5p, -7e-5p, miR-494-3p, miR-382-3p, miR-181c-5p), and IFN-α 3′-UTR (miR-361-5p, miR-410-3p). Overall, this study provides insight into potential novel regulatory mechanisms of SARS-CoV-2 by host miRNAs and lays the foundation for future investigation of these miRNAs as potential therapeutic targets or biomarkers.

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“…In these countries, the counter-measures provide a certain breath to governments in order to allow the health systems of each country to meet local needs, or to be able to wait for other solutions, such as the development of vaccines (not yet existing so far for the SARS-CoV-2 virus). The medical solutions to minimize or stop the pandemic include in-silico analysis of the SARS-CoV-2 genome [ 8 ], aiming to study its weaknesses in an attempt to better understanding it and develop treatments and vaccines.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

Pereira

Guérin

Júnior

et al. 2020

IJERPH

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The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world’s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country’s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.

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In-silicoanalysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Cited by 8 publications

References 14 publications

Investigation of SARS-CoV-2 Ability to Pass Through the Placenta

Investigation of SARS-CoV-2 Ability to Pass Through the Placenta

Computational Analysis of Targeting SARS-CoV-2, Viral Entry Proteins ACE2 and TMPRSS2, and Interferon Genes by Host MicroRNAs

Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

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