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1. Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

   https://doi.org/10.1371/journal.pmed.1003660  

   Iwanami, Shoya ; Ejima, Keisuke ; Kim, Kwang Su ; Noshita, Koji ; Fujita, Yasuhisa ; Miyazaki, Taiga ; Kohno, Shigeru ; Miyazaki, Yoshitsugu ; Morimoto, Shimpei ; Nakaoka, Shinji ; et al ( July 2021 , PLOS Medicine)

   Kretzschmar, Mirjam E. (Ed.)

   Background Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. Methods and findings A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates ( p -value < 0.001). The mean decay rates were 1.17 d −1 (95% CI: 1.06 to 1.27 d −1 ), 0.777 d −1 (0.716 to 0.838 d −1 ), and 0.450 d −1 (0.378 to 0.522 d −1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. Conclusions In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model. 

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2. Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences

   https://doi.org/10.1128/msystems.00035-22  

   Sokhansanj, Bahrad A. ; Rosen, Gail L. ( April 2022 , mSystems)

   Gaglia, Marta M. (Ed.)

   ABSTRACT Next-generation sequencing has been essential to the global response to the COVID-19 pandemic. As of January 2022, nearly 7 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences are available to researchers in public databases. Sequence databases are an abundant resource from which to extract biologically relevant and clinically actionable information. As the pandemic has gone on, SARS-CoV-2 has rapidly evolved, involving complex genomic changes that challenge current approaches to classifying SARS-CoV-2 variants. Deep sequence learning could be a potentially powerful way to build complex sequence-to-phenotype models. Unfortunately, while they can be predictive, deep learning typically produces “black box” models that cannot directly provide biological and clinical insight. Researchers should therefore consider implementing emerging methods for visualizing and interpreting deep sequence models. Finally, researchers should address important data limitations, including (i) global sequencing disparities, (ii) insufficient sequence metadata, and (iii) screening artifacts due to poor sequence quality control. 

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3. The SARS-CoV-2 S1 Spike Protein Promotes MAPK and NF-kB Activation in Human Lung Cells and Inflammatory Cytokine Production in Human Lung and Intestinal Epithelial Cells

   https://doi.org/10.3390/microorganisms10101996  

   Forsyth, Christopher B. ; Zhang, Lijuan ; Bhushan, Abhinav ; Swanson, Barbara ; Zhang, Li ; Mamede, João I. ; Voigt, Robin M. ; Shaikh, Maliha ; Engen, Phillip A. ; Keshavarzian, Ali ( October 2022 , Microorganisms)

   The coronavirus disease 2019 (COVID-19) pandemic began in January 2020 in Wuhan, China, with a new coronavirus designated SARS-CoV-2. The principal cause of death from COVID-19 disease quickly emerged as acute respiratory distress syndrome (ARDS). A key ARDS pathogenic mechanism is the “Cytokine Storm”, which is a dramatic increase in inflammatory cytokines in the blood. In the last two years of the pandemic, a new pathology has emerged in some COVID-19 survivors, in which a variety of long-term symptoms occur, a condition called post-acute sequelae of COVID-19 (PASC) or “Long COVID”. Therefore, there is an urgent need to better understand the mechanisms of the virus. The spike protein on the surface of the virus is composed of joined S1–S2 subunits. Upon S1 binding to the ACE2 receptor on human cells, the S1 subunit is cleaved and the S2 subunit mediates the entry of the virus. The S1 protein is then released into the blood, which might be one of the pivotal triggers for the initiation and/or perpetuation of the cytokine storm. In this study, we tested the hypothesis that the S1 spike protein is sufficient to activate inflammatory signaling and cytokine production, independent of the virus. Our data support a possible role for the S1 spike protein in the activation of inflammatory signaling and cytokine production in human lung and intestinal epithelial cells in culture. These data support a potential role for the SARS-CoV-2 S1 spike protein in COVID-19 pathogenesis and PASC. 

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4. Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning

   https://doi.org/10.3390/genes13122264  

   Flores, Mario A. ; Paniagua, Karla ; Huang, Wenjian ; Ramirez, Ricardo ; Falcon, Leonardo ; Liu, Andy ; Chen, Yidong ; Huang, Yufei ; Jin, Yufang ( December 2022 , Genes)

   The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients. 

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5. Conserved Genomic Terminals of SARS-CoV-2 as Coevolving Functional Elements and Potential Therapeutic Targets

   https://doi.org/10.1128/mSphere.00754-20  

   Chan, Agnes P. ; Choi, Yongwook ; Schork, Nicholas J. ( December 2020 , mSphere)

   Lee, Benhur (Ed.)

   ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 40 million people worldwide, with over 1 million deaths as of October 2020 and with multiple efforts in the development and testing of antiviral drugs and vaccines under way. In order to gain insights into SARS-CoV-2 evolution and drug targets, we investigated how and to what extent the SARS-CoV-2 genome sequence differs from those of other well-characterized human and animal coronavirus genomes, as well as how polymorphic SARS-CoV-2 genomes are generally. We ultimately sought to identify features in the SARS-CoV-2 genome that may contribute to its viral replication, host pathogenicity, and vulnerabilities. Our analyses suggest the presence of unique sequence signatures in the 3′ untranslated region (3′-UTR) of betacoronavirus lineage B, which phylogenetically encompasses SARS-CoV-2 and SARS-CoV as well as multiple groups of bat and animal coronaviruses. In addition, we identified genome-wide patterns of variation across different SARS-CoV-2 strains that likely reflect the effects of selection. Finally, we provide evidence for a possible host-microRNA-mediated interaction between the 3′-UTR and human microRNA hsa-miR-1307-3p based on the results of multiple computational target prediction analyses and an assessment of similar interactions involving the influenza A H1N1 virus. This interaction also suggests a possible survival mechanism, whereby a mutation in the SARS-CoV-2 3′-UTR leads to a weakened host immune response. The potential roles of host microRNAs in SARS-CoV-2 replication and infection and the exploitation of conserved features in the 3′-UTR as therapeutic targets warrant further investigation. IMPORTANCE The coronavirus disease 2019 (COVID-19) outbreak is having a dramatic global effect on public health and the economy. As of October 2020, SARS-CoV-2 has been detected in over 189 countries, has infected over 40 million people, and is responsible for more than 1 million deaths. The genome of SARS-CoV-2 is small but complex, and its functions and interactions with human host factors are being studied extensively. The significance of our study is that, using extensive SARS-CoV-2 genome analysis techniques, we identified potential interacting human host microRNA targets that share similarity with those of influenza A virus H1N1. Our study results will allow the development of virus-host interaction models that will enhance our understanding of SARS-CoV-2 pathogenesis and motivate the exploitation of both the interacting viral and host factors as therapeutic targets. 

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