Targeting G9a translational mechanism of SARS-CoV-2 pathogenesis for multifaceted therapeutics of COVID-19 and its sequalae (preprint)

By largely unknown mechanism(s), SARS-CoV-2 hijacks the host translation apparatus to promote COVID-19 pathogenesis. We report that the histone methyltransferase G9a noncanonically regulates viral hijacking of the translation machinery to bring about COVID-19 symptoms of hyperinflammation, lymphopenia, and blood coagulation. Chemoproteomic analysis of COVID-19 patient peripheral mononuclear blood cells (PBMC) identified enhanced interactions between SARS-CoV-2-upregulated G9a and distinct translation regulators, particularly the N6-methyladenosine (m6A) RNA methylase METTL3. These interactions with translation regulators implicated G9a in translational regulation of COVID-19. Inhibition of G9a activity suppressed SARS-CoV-2 replication in human alveolar epithelial cells. Accordingly, multi-omics analysis of the same alveolar cells identified SARS-CoV-2-induced changes at the transcriptional, m6A-epitranscriptional, translational, and post-translational (phosphorylation or secretion) levels that were reversed by inhibitor treatment. As suggested by the aforesaid chemoproteomic analysis, these multi-omics-correlated changes revealed a G9a-regulated translational mechanism of COVID-19 pathogenesis in which G9a directs translation of viral and host proteins associated with SARS-CoV-2 replication and with dysregulation of host response. Comparison of proteomic analyses of G9a inhibitor-treated, SARS-CoV-2 infected cells, or ex vivo culture of patient PBMCs, with COVID-19 patient data revealed that G9a inhibition reversed the patient proteomic landscape that correlated with COVID-19 pathology/symptoms. These data also indicated that the G9a-regulated, inhibitor-reversed, translational mechanism outperformed G9a-transcriptional suppression to ultimately determine COVID-19 pathogenesis and to define the inhibitor action, from which biomarkers of serve symptom vulnerability were mechanistically derived. This cell line-to-patient conservation of G9a-translated, COVID-19 proteome suggests that G9a inhibitors can be used to treat patients with COVID-19, particularly patients with long-lasting COVID-19 sequelae.

Development of a nomogram model for predicting the risk of insomnia in nurses who underwent the Long- COVID (preprint)

Purpose To investigate the prevalence of insomnia among nurses diagnosed with Long-COVID, analyze the potential risk factors, and establish a nomogram prediction model.Methods General demographic information was obtained, and assessments of sleep quality, burnout, and stress were performed in a single center in May 2023. Three hundred and ninety-eight nurses were recruited. The Lasso regression technique was employed to screen for potential factors contributing to insomnia. A prognostic nomogram was constructed and evaluated by receiver operating characteristic curves and calibration curves.Results Fifty-four percent of nurses complained of insomnia in this study. Eleven variables were independently associated with sleep patterns, including family, years of work, relaxion time, sequela of respiratory system, sequela of nervous system, others sequela, attitudes towards COVID-19, sleep duration, previous sleep problems, stress, and job burnout. The R-squared value was 0.4642 and the area under curve was 0.8661. The derived nomogram showed that neurological sequela, stress, job burnout, sleep time before infection, and previous sleep problems also made the most substantial contributions to predicting sleep patterns. The calibration curves for predicting insomnia showed significant agreement between the nomogram models and actual observations.Conclusion The present study established a nomogram prediction model of insomnia for nurses diagnosed with Long-COVID, which is helpful for the early clinical identification of high-risk individuals with insomnia.

The Whistleblower’s Dilemma: An Evolutionary Game Analysis of the Public Health Early Warning System

Although the ability to manage public health emergencies in China has improved significantly, there are still many challenges to the existing information transmission mechanism in pandemic early warning systems. In this context, a tripartite evolutionary game model composed of the local government, the whistleblower, and the public is formulated. By using Matlab, the dynamic evolution path of the game model is stimulated under different conditions. Stable strategies for an early warning system for public health emergencies are also explored. The results indicate that the cost of whistleblowing, the cost of response, and the benefit of attention significantly influence strategic decisions among three parties. This study highlights the importance of whistleblowing in managing public health emergencies. Yet, our findings provide theoretical support for policy recommendations for promoting public health emergency preparedness.

Safety and immunogenicity of a broad-spectrum mosaic vaccine as a booster dose against SARS-CoV-2 Omicron and other circulating variants (preprint)

BACKGROUNDThe rising breakthrough infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, especially Omicron and its sub-lineages, have raised an urgent need to develop broad-spectrum vaccines against coronavirus disease 2019 (COVID-19). We have developed a mosaic-type recombinant vaccine candidate, named NVSI-06-09, having immune potentials against a broad range of SARS-CoV-2 variants. METHODSAn ongoing randomized, double-blind, controlled phase 2 trial was conducted to evaluate the safety and immunogenicity of NVSI-06-09 as a booster dose in subjects aged 18 years and older from the United Arab Emirates (UAE), who had completed two or three doses of BBIBP-CorV vaccinations at least 6 months prior to the enrollment. The participants were randomly assigned with 1:1 to receive a booster dose of NVSI-06-09 or BBIBP-CorV. The primary outcomes were immunogenicity and safety against SARS-CoV-2 Omicron variant, and the exploratory outcome was cross-immunogenicity against other circulating strains. RESULTSA total of 516 participants received booster vaccination. Interim results showed a similar safety profile between NVSI-06-09 and BBIBP-CorV booster groups, with low incidence of adverse reactions of grade 1 or 2. For immunogenicity, by day 14 after the booster vaccination, the fold rises in neutralizing antibody geometric mean titers (GMTs) from baseline level elicited by NVSI-06-09 were remarkably higher than those by BBIBP-CorV against the prototype strain (19.67 vs 4.47-fold), Omicron BA.1.1 (42.35 vs 3.78-fold), BA.2 (25.09 vs 2.91-fold), BA.4 (22.42 vs 2.69-fold), and BA.5 variants (27.06 vs 4.73-fold). Similarly, the neutralizing GMTs boosted by NVSI-06-09 against Beta and Delta variants were also 6.60-fold and 7.17-fold higher than those boosted by BBIBP-CorV. CONCLUSIONSA booster dose of NVSI-06-09 was well-tolerated and elicited broad-spectrum neutralizing responses against SARS-CoV-2 prototype strain and immune-evasive variants, including Omicron and its sub-lineages. The immunogenicity of NVSI-06-09 as a booster vaccine was superior to that of BBIBP-CorV. (Funded by LIBP and BIBP of Sinopharm; ClinicalTrials.gov number, NCT05293548).

Behaviour in public open spaces: A systematic review of studies with quantitative research methods

Public open spaces are important assets that play a significant role in city lives, based on which a great number of behaviour-based studies are being conducted. These studies often use one or more case studies to observe people's preferences and usage habits and to investigate their influencing factors such as outdoor thermal comfort, environmental conditions, urban configuration, and local settings. Because the subject is complex and falls within the purview of multiple academic disciplines, it is a challenging task to understand the current status and development trends of existing studies. To fill this gap, this article presents a systematic review of quantitative evidence-based behaviour studies in public open spaces. Following the PRISMA method and searching using eight academic search engines, full texts of 116 research articles have been included for this review. The main contributions of this review are that: (1) it proposed a relatively complete system that categorizes people's behaviour in public open spaces;(2) it introduced outdoor subjective influencing procedure including behaviour, feeling and health impacts;(3) the review illustrated the distribution of existing research as well as research trends;and finally (4) the article also timely discussed the influence of the COVID-19 on people's behaviour in public open spaces. The authors consider this article to be useful as it can facilitate further behaviour-based studies in public open spaces. With a robust classification and future trend discussion of factors associated, fellow researchers, urban designers, city managers, and policymakers are easier to integrate and use the knowledge learned.

Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Clustered Regularly Interspaced Short Palindromic Repeat/Cas12a Mediated Multiplexable and Portable Detection Platform for GII Genotype Porcine Epidemic Diarrhoea Virus Rapid Diagnosis

Porcine epidemic diarrhoea virus (PEDV) is a member of the genus Alphacoronavirus in the family Coronaviridae. It causes acute watery diarrhoea and vomiting in piglets with high a mortality rate. Currently, the GII genotype, PEDV, possesses a high separation rate in wild strains and is usually reported in immunity failure cases, which indicates a need for a portable and sensitive detection method. Here, reverse transcription–recombinase aided amplification (RT-RAA) was combined with the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas12a system to establish a multiplexable, rapid and portable detection platform for PEDV. The CRISPR RNA (crRNA) against Spike (S) gene of GII PEDV specifically were added into the protocol. This system is suitable for different experimental conditions, including ultra-sensitive fluorescence, visual, UV light, or flow strip detection. Moreover, it exhibits high sensitivity and specificity and can detect at least 100 copies of the target gene in each reaction. The CRISPR/Cas12a detection platform requires less time and represents a rapid, reliable and practical tool for the rapid diagnosis of GII genotype PEDV.

The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic

The purpose of this article is to investigate whether various uncertainty measures provide incremental information for the prediction the volatility of crude oil futures under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, by considering the information overlap among various uncertainty measures and fully using of the information in various uncertainty measures, this paper uses two prevailing shrinkage methods, the least absolute shrinkage and selection operator (lasso) and elastic nets, to select uncertainty variables during the entire sampling period, before the COVID-19 pandemic and during the COVID-19 pandemic and then uses the HAR model to predict crude oil volatility. The results show that (i) uncertainty measures can be utilized to predict crude oil volatility under the high-frequency framework in both in-sample and out-of-sample analyses. (ii) Because of the information overlap between various uncertainty measures, adding a large number of uncertain variables to the HAR model may not significantly improve the volatility prediction. (iii) Before and during the COVID-19 pandemic, Chicago Board Options Exchange (CBOE) crude oil volatility (OVX) has the greatest impact on crude oil volatility, infectious disease equity market volatility (EMV) exerts a significant influence on crude oil futures volatility forecasts during the COVID-19 period, and CBOE implied volatility (VIX) and the financial stress index (FSI) have substantial impacts on crude oil futures volatility forecasts before COVID-19.

A mosaic-type trimeric RBD-based COVID-19 vaccine candidate induces potent neutralization against Omicron and other SARS-CoV-2 variants (preprint)

Large-scale populations in the world have been vaccinated with COVID-19 vaccines, however, breakthrough infections of SARS-CoV-2 are still growing rapidly due to the emergence of immune-evasive variants, especially Omicron. It is urgent to develop effective broad-spectrum vaccines to better control the pandemic of these variants. Here, we present a mosaic-type trimeric form of spike receptor-binding domain (mos-tri-RBD) as a broad-spectrum vaccine candidate, which carries the key mutations from Omicron and other circulating variants. Tests in rats showed that the designed mos-tri-RBD, whether used alone or as a booster shot, elicited potent cross-neutralizing antibodies against not only Omicron but also other immune-evasive variants. Neutralizing antibody titers induced by mos-tri-RBD were substantially higher than those elicited by homo-tri-RBD (containing homologous RBDs from prototype strain) or the inactivated vaccine BBIBP-CorV. Our study indicates that mos-tri-RBD is highly immunogenic, which may serve as a broad-spectrum vaccine candidate in combating SARS-CoV-2 variants including Omicron.

A multiplexed Cas13-based assay with point-of-care attributes for simultaneous COVID-19 diagnosis and variant surveillance (preprint)

Point-of-care (POC) nucleic acid detection technologies are poised to aid gold-standard technologies in controlling the COVID-19 pandemic, yet shortcomings in the capability to perform critically needed complex detection, such as multiplexed detection for viral variant surveillance, may limit their widespread adoption. Herein, we developed a robust multiplexed CRISPR-based detection using LwaCas13a and PsmCas13b to simultaneously diagnose SARS-CoV-2 infection and pinpoint the causative SARS-CoV-2 variant of concern (VOC)-including globally dominant VOCs Delta (B.1.617.2) and Omicron (B.1.1.529)-all while maintaining high levels of accuracy upon the detection of multiple SARS-CoV-2 gene targets. The platform has several attributes suitable for POC use: premixed, freeze-dried reagents for easy use and storage; convenient direct-to-eye or smartphone-based readouts; and a one-pot variant of the multiplexed detection. To reduce reliance on proprietary reagents and enable sustainable use of such a technology in low- and middle-income countries, we locally produced and formulated our own recombinase polymerase amplification reaction and demonstrated its equivalent efficiency to commercial counterparts. Our tool, CRISPR-based detection for simultaneous COVID-19 diagnosis and variant surveillance which can be locally manufactured, may enable sustainable use of CRISPR diagnostics technologies for COVID-19 and other diseases in POC settings.

Kinetics of IgM and IgG antibody specific to SARS-CoV-2 nucleocapsid and spike proteins

：Objective To study the kinetics of IgM and IgG antibodies based on nucleocapsid（N） and spike（S） protein of SARS-Co V2-in COVID-19 patients. Methods Immunofluorescent kits were used to detect N and S protein specific IgM and IgG antibodies from Jan.21 to Feb.11, 2020 for the 60 hospitalized COVID-19 patients（48 mild, 12 severe cases） with a total of 290 plasma samples collected 9 weeks after the onset of the disease. Results The level of antibodies specific for S protein varied significantly with the course of disease（Ig M from 27.32 to 110.10 TU/ml, IgG from 56.85 to 135.00 TU/ml）, but not for N protein.Higher level of Ig M/Ig G antibodies specific to S protein was observed during the 2-7 week than that to N protein.The seropositive rate of antibodies gradually increased during the early stage of disease.IgM/IgG antibodies specific to N protein changed from 12.50% at the first week to peak level（51.72% and 86.21% respectively） at the 4 th week and those for S protein from 25.00% and 14.58% to 100.00%, and then declined.The seropositive rate of Ig M antibody specific to S protein was higher than that for N protein during 2-8 th week and that for Ig G antibody at 2, 3, 4, 6 and 7 th week.The seropositive rate of Ig G antibody specific to N protein in severe patients at the third week was higher than that in mild patients（100.00% vs 59.52%,χ2=9.67, P=0.001 9）, and the same as to Ig G antibody for S protein at the second week after disease onset（80.00% vs 46.58%, χ2=5.57, P=0.018 2）. Conclusions SARS-Co V2-S protein can induc stronger antibody response than N protein, and the antibody level was related to the severity of the disease.

Prediction of Road Network Traffic State Using the NARX Neural Network

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

Proof of concept continuous event logging in living cells (preprint)

Cells must detect and respond to molecular events such as the presence or absence of specific small molecules. To accomplish this, cells have evolved methods to measure the presence and concentration of these small molecules in their environment and enact changes in gene expression or behavior. However, cells don't usually change their DNA in response to outside stimuli. In this work, we have engineered a genetic circuit that can enact specific and controlled genetic changes in response to small molecule stimuli. Known DNA sequences can be repeatedly integrated in a genomic array such that their identity and order encodes information about past small molecule concentrations that the cell has experienced. To accomplish this, we use catalytically inactive CRISPR-Cas9 (dCas9) to bind to and block attachment sites for the integrase Bxb1. Therefore, through the co-expression of dCas9 and guide RNA, Bxb1 can be directed to integrate one of two engineered plasmids, which correspond to two orthogonal small molecule inducers that can be recorded with this system. We identified the optimal location of guide RNA binding to the Bxb1 attP integrase attachment site, and characterized the detection limits of the system by measuring the minimal small molecule concentration and shortest induction time necessary to produce measurable differences in array composition as read out by Oxford Nanopore sequencing technology.

Impact of SARS-CoV-2 Vaccines on Covid-19 Incidence and Mortality in the United States (preprint)

Background: Despite safe and effective vaccines to prevent Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infections and disease, a substantial minority of the US remains resistant to getting vaccinated. It is imperative to know if expanding vaccination rates could reduce community-wide Coronavirus 2019 (COVID-19) disease, not just among those vaccinated. Methods: Negative binomial models were used to estimate associations between U.S. county-level vaccination rates and county-wide COVID-19 incidence and mortality between April 23rd and September 30th, 2021. A two-week lag and a four-week lag were introduced to assess vaccination rate impact on incidence and mortality, respectively. Stratified analyses were performed for county vaccination rates >40%, and before and after Delta became the dominant variant. Findings: Among 3,070 counties, each percentage increase in population vaccination rates reduced county-wide COVID-19 incidence by 0.9% (relative risk (RR) 0.9910 (95% CI: 0.9869, 0.9952)) and mortality by 1.9% (RR 0.9807 (95% CI: 0.9745, 0.9823)). Among counties with vaccination coverage >40%, each percentage increase in vaccination rates reduced COVID-19 disease by 1.5%, RR 0.9850 (95% CI: 0.9793, 0.9952) and mortality by 2.7% (RR 0.9727 (95% CI: 0.9632, 0.9823)). These associations were not observed among counties with <40% vaccination rates. Increasing vaccination rates from 40% to 80% would have reduced COVID-19 cases by 45.4% (RR 0.5458 (95% CI: 0.4335, 0.6873)) and deaths by 67.0% (RR 0.3305 (95% CI: 0.2230, 0.4898)). An estimated 5,989,952 COVID-19 cases could have been prevented and 127,596 lives saved had US population vaccination rates increased from 40% to 80%. Interpretations: Increasing U.S. SARS-CoV-2 vaccination rates results in population-wide reductions in COVID-19 incidence and mortality. Furthermore, increasing vaccination rates above 40% has protective effects among non-vaccinated persons. Given ongoing vaccine hesitancy in the U.S., increasing vaccination rates could better protect the entire community and potentially reach herd immunity. Funding: National Cancer Institute

Functional Multi-Omics Reveals Noncanonical Function of G9a in Translational Regulation of Chronic Inflammation (preprint)

By largely unknown mechanisms, dysregulated gene-specific translation directly contributes to chronic inflammation-associated diseases such as sepsis and ARDS. Here, we report that G9a, a histone methyltransferase and well-regarded transcriptional repressor, non-canonically or non-epigenetically activates translation of select antimicrobial genes to promote proliferation of cytokine producing macrophages and to impair T cell function; all hallmarks of endotoxin-tolerance related complications including sepsis, ARDS and COVID19. Mechanistically, G9a interacts with translation regulators including METTL3, an N6-methyladenosine or m6A RNA methyltransferase, and methylates it to cooperatively upregulate the translation of certain m6A-modified mRNAs that encode immune checkpoint and anti-inflammatory proteins. Further, translatome proteomic analysis of ET macrophages progressively treated by a G9a inhibitor identified proteins showing G9a-dependent translation that unite the networks associated with hyperinflammation and T cell dysfunction. Overall, we identified a previously unrecognized function of G9a in gene-specific translation that can be leveraged to treat ET-related chronic inflammatory diseases.

Epistasis at the SARS-CoV-2 RBD Interface and the Propitiously Boring Implications for Vaccine Escape (preprint)

Emergence of vaccine escape variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a grave global concern. The interface between the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein and the host receptor (ACE2) overlaps the binding site of principal neutralizing antibodies (NAb), limiting the repertoire of viable mutations. Epistasis among RBD mutations can increase the risk of vaccine escape. We compare the effects of all single mutants at the RBD-NAb and RBD-ACE2 interfaces for wild type (WT), Gamma and Delta variants using Rosetta. Epistasis at the RBD surface appears to be limited and the effects of most multiple mutations are additive. For Delta variant, epistasis weakly stabilizes NAb interaction relative to ACE2, whereas in Gamma, epistasis more substantially destabilizes NAb interaction. Thus, the repertoire of potential escape mutations for Delta is not substantially different from that of WT, whereas Gamma poses a moderately greater risk of vaccine escape.Funding: Intramural Research Program of the National Institutes of Health of the USA (National Library of Medicine), to E. V. KooninDeclaration of Interests: The declare they have no competing interests.

Asymptomatic SARS-CoV-2 infection in children: A clinical analysis of 20 cases

To study the clinical and epidemiological features of children with asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods The clinical data of 20 children who were diagnosed with asymptomatic SARS-CoV-2 infection from January 20 to March 4, 2020 were analyzed. Results Among the 20 children, there were 7 boys (35%) and 13 girls (65%), aged 8 months to 14 years (mean 8±5 years). All these children had no clinical manifestations and attended the hospital for an epidemiological history of SARS-CoV-2. Nineteen children were shown with family aggregation of SARS-CoV-2 infection. Nasopharyngeal swabs were PCRpositive for SARS-CoV-2 in all 20 children. There were 4 children (20%) of mild type, 16 children (80%) of common type, and no children of severe type or critical type. The mean peripheral blood leukocyte count was (6.8±3.5)×109/L, and 7 children had an abnormal peripheral blood leukocyte count, with an increase in 5 children and a reduction in 2 children. One child had a decreased absolute value of lymphocytes (0.87×109/L), 3 children had an increased erythrocyte sedimentation rate (20-42 mm/h), 7 children had an increased lactate dehydrogenase level (>400 U/L), and 4 children had an increased blood lactate level (>1.6 mmol/L). Chest CT showed single or multiple small nodule shadows, patchy shadows, and ground-glass shadows in the middle or lateral lobe of lungs or under the pleura in 13 children. Conclusions Pediatric cases of asymptomatic SARS-CoV-2 infection mostly occur with family aggregation. Most of the children with asymptomatic infection have no obvious abnormalities in blood routine and other laboratory tests. Changes in chest CT scan can be used as an aid for early diagnosis of asymptomatic infection in children.

Epistasis at the SARS-CoV-2 RBD Interface and the Propitiously Boring Implications for Vaccine Escape (preprint)

At the time of this writing, August 2021, potential emergence of vaccine escape variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a grave global concern. The interface between the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein and the host receptor (ACE2) overlap with the binding site of principal neutralizing antibodies (NAb), limiting the repertoire of viable mutations. Nonetheless, variants with multiple mutations in the RBD have rose to dominance. Non-additive, epistatic relationships among RBD mutations are apparent, and assessing the impact of such epistasis on the mutational landscape is crucial. Epistasis can substantially increase the risk of vaccine escape and cannot be completely characterized through the study of the wild type (WT) alone. We employed protein structure modeling using Rosetta to compare the effects of all single mutants at the RBD-NAb and RBD-ACE2 interfaces for the WT, Gamma (417T, 484K, 501Y), and Delta variants (452R, 478K). Overall, epistasis at the RBD surface appears to be limited and the effects of most multiple mutations are additive. Epistasis at the Delta variant interface weakly stabilizes NAb interaction relative to ACE2, whereas in the Gamma variant, epistasis more substantially destabilizes NAb interaction. These results suggest that the repertoire of potential escape mutations for the Delta variant is not substantially different from that of the WT, whereas Gamma poses a moderately greater risk for enhanced vaccine escape. Thus, the modest ensemble of mutations relative to the WT shown to reduce vaccine efficacy might constitute the majority of all possible escape mutations. SignificancePotential emergence of vaccine escape variants of SARS-CoV-2 is arguably the most pressing problem during the COVID-19 pandemic as vaccines are distributed worldwide. We employed a computational approach to assess the risk of antibody escape resulting from mutations in the receptor-binding domain of the spike protein of the wild type SARS-CoV-2 virus as well as the Gamma and Delta variants. The results indicate that emergence of escape mutants is somewhat less likely for the Delta variant than for the wild type and moderately more likely for the Gamma variant. We conclude that the small set of escape-enhancing mutations already identified for the wild type is likely to include the majority of all possible mutations with this effect, a welcome finding.