A Murine Model of Post-acute Neurological Sequelae Following SARS-CoV-2 Variant Infection (preprint)

Viral variant is one known risk factor associated with post-acute sequelae of COVID-19 (PASC), yet the pathogenesis is largely unknown. Here, we studied SARS-CoV-2 Delta variant-induced PASC in K18-hACE2 mice. The virus replicated productively, induced robust inflammatory responses in lung and brain tissues, and caused weight loss and mortality during the acute infection. Longitudinal behavior studies in surviving mice up to 4 months post-acute infection revealed persistent abnormalities in neuropsychiatric state and motor behaviors, while reflex and sensory functions recovered over time. Surviving mice showed no detectable viral RNA in the brain and minimal neuroinflammation post-acute infection. Transcriptome analysis revealed persistent activation of immune pathways, including humoral responses, complement, and phagocytosis, and reduced levels of genes associated with ataxia telangiectasia, impaired cognitive function and memory recall, and neuronal dysfunction and degeneration. Furthermore, surviving mice maintained potent T helper 1 prone cellular immune responses and high neutralizing antibodies against Delta and Omicron variants in the periphery for months post-acute infection. Overall, infection in K18-hACE2 mice recapitulates the persistent clinical symptoms reported in long COVID patients and may be useful for future assessment of the efficacy of vaccines and therapeutics against SARS-CoV-2 variants.

AT2 Cell-Derived IgA Trapped by the Extracellular Matrix and Promoted Pulmonary Fibrosis (preprint)

Pulmonary fibrosis is an interstitial lung disease caused by various factors such as exposure to workplace environmental contaminants, drugs, or X-rays. Epithelial cells are among the driving factors of pulmonary fibrosis. Immunoglobulin A (IgA), traditionally thought to be secreted by B cells, is an important immune factor involved in COVID-19 infection and vaccination. In current study, we found lung epithelial cells were involved in IgA secretion which, in turn, promoted pulmonary fibrosis. The spatial transcriptomics and single-cell sequencing suggests that Igha transcripts were highly expressed in the fibrotic lesion areas of lungs from silica-treated mice. Reconstruction of B-cell receptor (BCR) sequences revealed a new cluster of AT2-like epithelial cells with a shared BCR and high expression of genes related to IgA production. Furthermore, the secretion of IgA by AT2-like cells were trapped by extracellular matrix and aggravated pulmonary fibrosis by activating fibroblasts. Targeted blockade of IgA secretion by pulmonary epithelial cells may be a potential strategy for treating pulmonary fibrosis.

Optimizing COVID-19 vaccine allocation considering the target population

Vaccine allocation strategy for COVID-19 is an emerging and important issue that affects the efficiency and control of virus spread. In order to improve the fairness and efficiency of vaccine distribution, this paper studies the optimization of vaccine distribution under the condition of limited number of vaccines. We pay attention to the target population before distributing vaccines, including attitude toward the vaccination, priority groups for vaccination, and vaccination priority policy. Furthermore, we consider inventory and budget indexes to maximize the precise scheduling of vaccine resources. A mixed-integer programming model is developed for vaccine distribution considering the target population from the viewpoint of fairness and efficiency. Finally, a case study is provided to verify the model and provide insights for vaccine distribution.

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).

Specific Pupylation as IDEntity Reporter (SPIDER) for the identification of Protein-Biomolecule interactions (preprint)

Protein-biomolecule interactions play pivotal roles in almost all biological processes, the identification of the interacting protein is essential. By combining a substrate-based proximity labelling activity from the pupylation pathway of Mycobacterium tuberculosis , and the streptavidin (SA)-biotin system, we developed S pecific P upylation as IDE ntity R eporter (SPIDER) for identifying protein-biomolecular interactions. As a proof of principle, SPIDER was successfully applied for global identification of interacting proteins, including substrates for enzyme (CobB), the readers of m 6 A, the protein interactome of mRNA, and the target proteins of drug (lenalidomide). In addition, by SPIDER, we identified SARS-CoV-2 Omicron variant specific receptors on cell membrane and performed in-depth analysis for one candidate, Protein-g. These potential receptors could explain the differences between the Omicron variant and the Prototype strain, and further serve as target for combating the Omicron variant. Overall, we provide a robust technology which is applicable for a wide-range of protein-biomolecular interaction studies.

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

Comparison of ARIMA, ES, GRNN and ARIMA–GRNN hybrid models to forecast the second wave of COVID-19 in India and the United States

As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)14 model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.

COVID-ONE-humoral immune: The One-stop Database for COVID-19-specific Antibody Responses and Clinical Parameters (preprint)

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, varies with regard to symptoms and mortality rates among populations. Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19. However, differences in immune responses and clinical features among COVID-19 patients remain largely unknown. Here, we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters (COVID-ONE humoral immune). COVID-ONE humoral immunity is based on a dataset that contains the IgG/IgM responses to 21 of 28 known SARS-CoV-2 proteins and 197 spike protein peptides against 2,360 COVID-19 samples collected from 783 patients. In addition, 96 clinical parameters for the 2,360 samples and information for the 783 patients are integrated into the database. Furthermore, COVID-ONE humoral immune provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups. A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters. After the "START" button is clicked, one can readily obtain a comprehensive analysis report for further interpretation. COVID-ONE-humoral immune is freely available at www.COVID-ONE.cn.

COVID-ONE-humoral immune: The One-stop Database for COVID-19-specific Antibody Responses and Clinical Parameters (preprint)

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, varies with regard to symptoms and mortality rates among populations. Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19. However, differences in immune responses and clinical features among COVID-19 patients remain largely unknown. Here, we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters (COVID-ONE humoral immune). COVID-ONE humoral immunity is based on a dataset that contains the IgG/IgM responses to 21 of 28 known SARS-CoV-2 proteins and 197 spike protein peptides against 2,360 COVID-19 samples collected from 783 patients. In addition, 96 clinical parameters for the 2,360 samples and information for the 783 patients are integrated into the database. Furthermore, COVID-ONE humoral immune provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups. A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters. After the START button is clicked, one can readily obtain a comprehensive analysis report for further interpretation. COVID-ONE-humoral immune is freely available at www.COVID-ONE.cn.

Predicting Progression to Severe COVID-19 Using the PAINT Score Based on Natural Killer Cells (preprint)

Objectives: One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. Natural killer (NK) cells are innate lymphocytes that respond to viral infection and might relate to COVID-19 disease severity. Therefore, we aimed to develop a new predictive score for progression from mild/moderate to severe COVID-19 based on NK cells information. Method: In total, 239 hospitalized patients with COVID-19 from two medical center in China were retrospectively included. The prognostic effects of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analyzed using Cox proportional hazards model and Kaplan-Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19. Results: Among the 239 patients, 216 (90.38%) patients had mild/moderate disease and 23 (9.62%) progressed to severe disease. After adjusting multiple confounding factors, pulmonary disease, age >75, IgM, CD16+/CD56+ NK cells and aspartate aminotransferase were independently predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p<0.001). The PAINT score was validated using nomogram, bootstrap internal validations, calibration curves, decision curves and clinical impact curve, all of which confirmed its high predictive value. Conclusions: The PAINT score for progression from mild/moderate to severe COVID-19 based on NK cell information may be helpful to identify patients at high risk of progression. Trial registration: None

Simultaneous Dual‐Gene Diagnosis of SARS‐CoV‐2 Based on CRISPR/Cas9‐Mediated Lateral Flow Assay

Few methods for the detection of SARS‐CoV‐2 currently have the capability to simultaneously detect two genes in a single test, which is a key measure to improve detection accuracy, as adopted by the gold standard RT‐qPCR method. Developed here is a CRISPR/Cas9‐mediated triple‐line lateral flow assay (TL‐LFA) combined with multiplex reverse transcription‐recombinase polymerase amplification (RT‐RPA) for rapid and simultaneous dual‐gene detection of SARS‐CoV‐2 in a single strip test. This assay is characterized by the detection of envelope (E) and open reading frame 1ab (Orf1ab) genes from cell‐cultured SARS‐CoV‐2 and SARS‐CoV‐2 viral RNA standards, showing a sensitivity of 100 RNA copies per reaction (25 μL). Furthermore, dual‐gene analysis of 64 nasopharyngeal swab samples showed 100 % negative predictive agreement and 97.14 % positive predictive agreement. This platform will provide a more accurate and convenient pathway for diagnosis of COVID‐19 or other infectious diseases in low‐resource regions.

SARS-CoV-2 Antibody Signatures for Predicting the Outcome of COVID-19 (preprint)

Background: The COIVD-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies remain elevated with disease progression and severity in COIVD-19 patients. We assumed that antibodies may serve as biomarkers for predicting disease outcome.Method: By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgM/IgG responses against 20 SARS-CoV-2 proteins in 1,034 hospitalized COVID-19 patients on admission, who were followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality.Results: We found that high level of IgM against ORF7b at the time of hospitalization is an independent predictor of patient survival ( p  trend = 0.002), while levels of IgG responses to 6 non-structural proteins and 1 accessory protein, i. e., NSP4, NSP7, NSP9, NSP10, RdRp (NSP12), NSP14, and ORF3b, possess significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory markers for disease severity (all with p trend < 0.05). Spline regression analysis indicated that the correlation between ORF7b IgM, NSP9 IgG, and NSP10 IgG and the risk of COVID-19 mortality shows linear ( p = 0.0013, 0.0073 and 0.0003, respectively). Their AUCs for predictions, determined by computational cross-validations (validation1), were 0.74 (cut-off = 7.59), 0.66 (cut-off = 9.13), and 0.68 (cut-off = 6.29), respectively. Further validations were conducted in the second and third serial samples of these cases (validation2A, n = 633, validation2B, n = 382), with high accuracy of prediction for outcome.Conclusion: These findings have important implications for improving clinical management, and especially for developing medical interventions and vaccines.Funding Statement: This work was supported by grants from the Fundamental Research Funds for the Central Universities (HUST COVID-19 Rapid Response Call No. 2020kfyXGYJ040) and Wuhan Bureau of Science and Technology (No. 2020020601012218).Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: The study was approved by the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (IRB ID:TJ-C20200128).

Antibody Landscape Against SARS-CoV-2 Proteome Revealed Significant Differences between Non-Structural/ Accessory Proteins and Structural Proteins (preprint)

The immunogenicity of SARS-CoV-2 proteome is largely unknown, especially for non-structural proteins and accessory proteins. Here we collected 2,360 COVID-19 sera and 601 control sera. We analyzed these sera on a protein microarray with 20 proteins of SARS-CoV-2, built an antibody response landscape for IgG and IgM. We found that non-structural proteins and accessory proteins NSP1, NSP7, NSP8, RdRp, ORF3b and ORF9b elicit prevalent IgG responses. The IgG patterns and dynamic of non-structural/ accessory proteins are different from that of S and N protein. The IgG responses against these 6 proteins are associated with disease severity and clinical outcome and declined sharply about 20 days after symptom onset. In non-survivors, sharp decrease of IgG antibodies against S1 and N protein before death was observed. The global antibody responses to non-structural/ accessory proteins revealed here may facilitate deeper understanding of SARS-CoV-2 immunology.Funding: This work was partially supported by the National Key Research and Development Program of China Grant (No.2016YFA0500600), National Natural Science Foundation of China (No. 31970130, 31600672, 31670831, 31370813, 31900112 and 21907065).Conflict of Interest: The authors declare no competing interests.Ethical Approval: The study was approved by the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (ITJ-C20200128). Written informed consent was obtained from all participants enrolled in this study.

What Explains Differences in Finance Research Productivity During the Pandemic? (preprint)

Based on a survey of AFA members, we analyze how demographics, time allocation, production mechanisms, and institutional factors affect research production during the pandemic. Consistent with the literature, research productivity falls more for women and faculty with young children. Independently, and novel, extra time spent teaching (much more likely for women) negatively affects research productivity. Also novel, concerns about feedback, isolation, and health have large negative research effects, which disproportionately affect junior faculty and PhD students. Finally, faculty who express greater concerns about employers’ finances report larger negative research effects and more concerns about feedback, isolation, and health.

Genetic determinants of COVID-19 drug efficacy revealed by genome-wide CRISPR screens (preprint)

Immunomodulatory agents dexamethasone and colchicine, antiviral drugs remdesivir, favipiravir and ribavirin, as well as antimalarial drugs chloroquine phosphate and hydroxychloroquine are currently used in the combat against COVID-19. However, whether some of these drugs have clinical efficacy for COVID-19 is under debate. Moreover, these drugs are applied in COVID-19 patients with little knowledge of genetic biomarkers, which will hurt patient outcome. To answer these questions, we designed a screen approach that could employ genome-wide sgRNA libraries to systematically uncover genes crucial for these drugs' action. Here we present our findings, including genes crucial for the import, export, metabolic activation and inactivation of remdesivir, as well as genes that regulate colchicine and dexamethasone's immunosuppressive effects. Our findings provide preliminary information for developing urgently needed genetic biomarkers for these drugs. Such biomarkers will help better interpret COVID-19 clinical trial data and point to how to stratify COVID-19 patients for proper treatment with these drugs.

The Volatile and Heterogenous Gut Microbiota Shifts of COVID-19 Patients Over The Course of A Probiotics-Assisted Therapy (preprint)

Background Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic, and little is known regarding the gut microbiota dynamics of the disease that often features a drastic and swift progression. Here we employed analyses of 16S rRNA gene sequencing and metatranscriptome to investigate the gut microbiome characteristics of a group of COVID-19 patients over the course of a probiotics-assisted therapy. Results The COVID-19 patients exhibited apparent microbiota alterations characterized by prominent compositional and functional shifts, which included taxonomic changes (e.g., increased relative abundance of Enterococcus and Rhodococcus, and decreased relative abundance of Faecalibacterium and Clostridium XlVa) and transcriptional changes (e.g., increased transcriptional activities of Escherichia coli and Klebsiella pneumoniae, virulence factors, and antibiotic resistance genes, and decreased activities of Faecalibacterium prausnitzii). Importantly, there were great interpersonal heterogeneity and intertimepoint fluctuations, as the most abundant or transcriptionally active taxa often greatly differed among individual patients and timepoints. Coincided with the resolution of respiratory symptoms, after the therapy some patients showed signs of recovery in the gut microbiome abnormalities. Associations were identified between gut and airway taxa and serum factors. Conclusion Our findings suggested that there is a lack of gut microbiota stability in COVID-19 patients and that measures are needed to ameliorate the gut microbiome perturbations in the patients to improve the prognosis. In addition, inclusion of probiotics is safe for treating COVID-19 patients and may improve their prognosis. Trial registration ISRCTN, ChiCTR2000029999. Registered 19 February 2020, http://www.chictr.org.cn/showprojen.aspx?proj=49717