Analysis of neutralizing antibody to COVID-19 vaccine in hospitalized patients with liver dysfunction (preprint)

Background: To evaluate the level of neutralizing antibodies(NAbs) after COVID-19 vaccination in patients suffered from liver dysfunction.Methods: In this cross-sectional study with longitudinal follow-up , a total of 137 patients and 134 healthy volunteers with full-course COVID-19 vaccination for more than 1 month were enrolled. Data of clinical characteristics , dates after vaccination and the types of vaccine were collected. Blood samples were collected for NAbs test when the patient was hospitalized, and some collected second time after treatment. Results: Both seropositivity and mean titer of NAbs in patients was significantly lower than that in healthy control(65.7% vs 80.60%; 3.954 vs 4.944 log2AU/ml ). The decrease of antibody titer in patients was significantly faster than that in healthy controls（-2.642 vs -0.9654 AU/ml per day). In multiple logistic regression analysis, more severe liver damage (odds ratio (OR): 0.30; 95% confidence interval (95%CI): 0.12–0.71; p=0.0067) and male (OR: 0.17; 95%CI: 0.06–0.46; p=0.0005)were significantly associated with reduction of the probability of NAbs seropositivity. Chronic liver status(β =−1.45; 95% CI: −2.13 to −0.76; p<0.0001) and male sex (β =−1.18; 95% CI: −1.73 to −0.64; p<0.0001) were significantly associated with higher NAbs titers. In 26 patients with liver failure, both the antibody seropositivity and titer did not decrease, but increased(before 53.8% vs after 84.6%; 3.553 vs 4.321 log2AU/ml)after artificial liver plasmapheresis.Conclusions: Compared with healthy people, the seropositivity and titer of NAbs after COVID-19 vaccination in patients with liver dysfunction were lower and the titer decreased faster. Male, severe liver injury and chronic liver status have increased risk of poor antibody responses, and artificial liver treatment does not reduce NAbs titers in patients with liver failure. 

Lyophilized mRNA-lipid nanoparticles vaccine with long-term stability and high antigenicity against SARS-CoV-2 (preprint)

Advanced mRNA vaccines play vital roles against SARS-CoV-2. However, due to the poor stability, most current mRNA delivery platforms need to be stored at -20 {degrees}C or -70 {degrees}C. Here we present lyophilized thermostable mRNA loaded lipid nanoparticles, which could be stored at room temperature with long-term stability. We demonstrate the applicability of lyophilization techniques to different mRNA sequences and lipid components. Three lyophilized vaccines targeting wild-type, Delta and Omicron SARS-CoV-2 variant were prepared and demonstrated to be able induce high-level of IgG titer and neutralization response. In the Delta challenge in vivo experiment, the lyophilized mRNA vaccine successfully protected the mice from infection and clear the virus. This lyophilization platform could significantly improve the accessibility of mRNA vaccine or therapeutics, particularly in remote regions.

Equity Promotion in Online Resource Allocation (preprint)

We consider online resource allocation under a typical non-profit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government. We focus on the internal-equity by assuming that arriving requesters are homogeneous in terms of their external factors like demands but heterogeneous for their internal attributes like demographics. Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio. We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. Both theoretical and numerical results show that our LP-based sampling strategies can effectively promote equity, especially when the arrival population is disproportionately represented, as observed in the early stage of the COVID-19 vaccine rollout.

Inferring SARS-CoV-2 functional genomics from viral transcriptome with identification of potential antiviral drugs and therapeutic targets.

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has posed a serious threat to global health. Here, we systematically characterized the transcription levels of the SARS-CoV-2 genes and identified the responsive human genes associated with virus infection. We inferred the possible biological functions of each viral gene and depicted the functional landscape based on guilt-by-association and functional enrichment analyses. Subsequently, the transcription factor regulatory network, protein-protein interaction network, and non-coding RNA regulatory network were constructed to discover more potential antiviral targets. In addition, several potential drugs for COVID-19 treatment and prevention were recognized, including known cell proliferation-related, immune-related, and antiviral drugs, in which proteasome inhibitors (bortezomib, carfilzomib, and ixazomib citrate) may play an important role in the treatment of COVID-19. These results provided novel insights into the understanding of SARS-CoV-2 functional genomics and host-targeting antiviral strategies for SARS-CoV-2 infection.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. f

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (preprint)

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

The N-terminal domain of spike glycoprotein mediates SARS-CoV-2 infection by associating with L-SIGN and DC-SIGN (preprint)

The widespread occurrence of SARS-CoV-2 has had a profound effect on society and a vaccine is currently being developed. Angiotensin-converting enzyme 2 (ACE2) is the primary host cell receptor that interacts with the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Although pneumonia is the main symptom in severe cases of SARS-CoV-2 infection, the expression levels of ACE2 in the lung is low, suggesting the presence of another receptor for the spike protein. In order to identify the additional receptors for the spike protein, we screened a receptor for the SARS-CoV-2 spike protein from the lung cDNA library. We cloned L-SIGN as a specific receptor for the N-terminal domain (NTD) of the SARS-CoV-2 spike protein. The RBD of the spike protein did not bind to L-SIGN. In addition, not only L-SIGN but also DC-SIGN, a closely related C-type lectin receptor to L-SIGN, bound to the NTD of the SARS-CoV-2 spike protein. Importantly, cells expressing L-SIGN and DC-SIGN were both infected by SARS-CoV-2. Furthermore, L-SIGN and DC-SIGN induced membrane fusion by associating with the SARS-CoV-2 spike protein. Serum antibodies from infected patients and a patient-derived monoclonal antibody against NTD inhibited SARS-CoV-2 infection of L-SIGN or DC-SIGN expressing cells. Our results highlight the important role of NTD in SARS-CoV-2 dissemination through L-SIGN and DC-SIGN and the significance of having anti-NTD neutralizing antibodies in antibody-based therapeutics.

Comparison of SARS-CoV-2 infection in two non-human primate species: rhesus and cynomolgus macaques (preprint)

SARS-CoV-2 is a coronavirus that sparked the current COVID-19 pandemic. To stop the shattering effect of COVID-19, effective and safe vaccines, and antiviral therapies are urgently needed. To facilitate the preclinical evaluation of intervention approaches, relevant animal models need to be developed and validated. Rhesus macaques (Macaca mulatta) and cynomolgus macaques (Macaca fascicularis) are widely used in biomedical research and serve as models for SARS-CoV-2 infection. However, differences in study design make it difficult to compare and understand potential species-related differences. Here, we directly compared the course of SARS-CoV-2 infection in the two genetically closely-related macaque species. After inoculation with a low passage SARS-CoV-2 isolate, clinical, virological, and immunological characteristics were monitored. Both species showed slightly elevated body temperatures in the first days after exposure while a decrease in physical activity was only observed in the rhesus macaques and not in cynomolgus macaques. The virus was quantified in tracheal, nasal, and anal swabs, and in blood samples by qRT-PCR, and showed high similarity between the two species. Immunoglobulins were detected by various enzyme-linked immunosorbent assays (ELISAs) and showed seroconversion in all animals by day 10 post-infection. The cytokine responses were highly comparable between species and computed tomography (CT) imaging revealed pulmonary lesions in all animals. Consequently, we concluded that both rhesus and cynomolgus macaques represent valid models for evaluation of COVID-19 vaccine and antiviral candidates in a preclinical setting.

Human Identical Sequences of SARS-CoV-2 Promote Clinical Progression of COVID-19 by Upregulating Hyaluronan via NamiRNA-Enhancer Network (preprint)

The COVID-19 pandemic is a widespread and deadly public health crisis. The pathogen SARS-CoV-2 replicates in the lower respiratory tract and causes fatal pneumonia. Although tremendous efforts have been put into investigating the pathogeny of SARS-CoV-2, the underlying mechanism of how SARS-CoV-2 interacts with its host is largely unexplored. Here, by comparing the genomic sequences of SARS-CoV-2 and human, we identified five fully conserved elements in SARS-CoV-2 genome, which were termed as "human identical sequences (HIS)". HIS are also recognized in both SARS-CoV and MERS-CoV genome. Meanwhile, HIS-SARS-CoV-2 are highly conserved in the primate. Mechanically, HIS-SARS-CoV-2 RNA directly binds to the targeted loci in human genome and further interacts with host enhancers to activate the expression of adjacent and distant genes, including cytokines gene and angiotensin converting enzyme II (ACE2), a well-known cell entry receptor of SARS-CoV-2, and hyaluronan synthase 2 (HAS2), which further increases hyaluronan formation. Noteworthily, hyaluronan level in plasma of COVID-19 patients is tightly correlated with severity and high risk for acute respiratory distress syndrome (ARDS) and may act as a predictor for the progression of COVID-19. HIS antagomirs, which downregulate hyaluronan level effectively, and 4-Methylumbelliferone (MU), an inhibitor of hyaluronan synthesis, are potential drugs to relieve the ARDS related ground-glass pattern in lung for COVID-19 treatment. Our results revealed that unprecedented HIS elements of SARS-CoV-2 contribute to the cytokine storm and ARDS in COVID-19 patients. Thus, blocking HIS-involved activating processes or hyaluronan synthesis directly by 4-MU may be effective strategies to alleviate COVID-19 progression.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research (preprint)

Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availability: https://neo4covid19.ncats.io . Keywords: SARS-CoV-2, COVID-19, network pharmacology, graph database, Neo4j, data integration, drug repositioning

The experience of treating patients with pneumonia secondary to the coronavirus disease 2019 using the Guizhi method of traditional Chinese medicine

This paper is a discussion of Professor Tang Nong's approach to the diagnosis and treatment of the coronavirus disease 2019 (COVID-19) while providing a case report at the end. Professor Tang Nong considered that the main etiologies of the disease are 'cold, wet, and poisonous.' He suggested resolving the body's dampness by balancing internal organ functions, detoxifying the lungs, and providing heat. However, the treatment of cold with herbs and cleansing heat must not be performed too early to prevent the spread of the disease. Using principles from the basic theory of Fuyang Pai from traditional Chinese medicine (TCM), this project used the Huashi Qingfei immune formula (modified Guizhi Erchen decoction), which has been shown to be effective, to treat patients diagnosed with COVID-19. At present, the participation of TCM in our hospital is over 96% with a cure rate of approximately 90%.

Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States (preprint)

We propose a new epidemic model (SuEIR) for forecasting the spread of COVID-19, including numbers of confirmed and fatality cases at national and state levels in the United States. Specifically, the SuEIR model is a variant of the SEIR model by taking into account the untested/unreported cases of COVID-19, and trained by machine learning algorithms based on the reported historical data. Besides providing basic projections for confirmed and fatality cases, the proposed SuEIR model is also able to predict the peak date of active cases, and estimate the basic reproduction number (R0). In particular, the forecasts based on our model suggest that the peak date of the US, New York state, and California state are 06/01/2020, 05/10/2020, and 07/01/2020 respectively. In addition, the estimated R0 of the US, New York state, and California state are 2.5, 3.6 and 2.2 respectively. The prediction results for all states in the US can be found on our project website: https://covid19.uclaml.org, which are updated on a weekly basis, and have been adopted by the Centers for Disease Control and Prevention (CDC) for COVID-19 death forecasts (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html).