Impact of SARS-CoV-2 variants on inpatient clinical outcome (preprint)

Background: Prior observation has shown differences in COVID-19 hospitalization rates between SARS-CoV-2 variants, but limited information describes differences in hospitalization outcomes. Methods: Patients admitted to 5 hospitals with COVID-19 were included if they had hypoxia, tachypnea, tachycardia, or fever, and data to describe SARS-CoV-2 variant, either from whole genome sequencing, or inference when local surveillance showed [≥]95% dominance of a single variant. The average effect of SARS-CoV-2 variant on 14-day risk of severe disease, defined by need for advanced respiratory support, or death was evaluated using models weighted on propensity scores derived from baseline clinical features. Results: Severe disease or death within 14 days occurred for 950 of 3,365 (28%) unvaccinated patients and 178 of 808 (22%) patients with history of vaccination or prior COVID-19. Among unvaccinated patients, the relative risk of 14-day severe disease or death for Delta variant compared to ancestral lineages was 1.34 (95% confidence interval [CI] 1.13-1.55). Compared to Delta variant, this risk for Omicron patients was 0.78 (95% CI 0.62-0.97) and compared to ancestral lineages was 1.04 (95% CI 0.84-1.24). Among Omicron and Delta infections, patients with history of vaccination or prior COVID-19 had one-half the 14-day risk of severe disease or death (adjusted hazard ratio 0.46, IQR 0.34-0.62) but no significant outcome difference between Delta and Omicron infections. Conclusions: Although the risk of severe disease or death for unvaccinated patients with Omicron was lower than Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated patients, but there was no difference between Delta and Omicron infections.

Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring (preprint)

COVID-19 has challenged health systems to learn how to learn. This paper describes the context, methods and challenges for learning to improve COVID-19 care at one academic health center. Challenges to learning include: (1) choosing a right clinical target; (2) designing methods for accurate predictions by borrowing strength from prior patients' experiences; (3) communicating the methodology to clinicians so they understand and trust it; (4) communicating the predictions to the patient at the moment of clinical decision; and (5) continuously evaluating and revising the methods so they adapt to changing patients and clinical demands. To illustrate these challenges, this paper contrasts two statistical modeling approaches - prospective longitudinal models in common use and retrospective analogues complementary in the COVID-19 context - for predicting future biomarker trajectories and major clinical events. The methods are applied to and validated on a cohort of 1,678 patients who were hospitalized with COVID-19 during the early months of the pandemic. We emphasize graphical tools to promote physician learning and inform clinical decision making.

Effectiveness of remdesivir with and without dexamethasone in hospitalized patients with COVID-19 (preprint)

RationaleRemdesivir and dexamethasone reduced the severity of COVID-19 in clinical trials. However, their individual or combined effectiveness in clinical practice remains unknown. ObjectivesTo examine the effectiveness of remdesivir with or without dexamethasone. MethodsWe conducted a multicenter, retrospective cohort study between March 4 and August 29, 2020. Eligible COVID cases were hospitalized patients treated with remdesivir with or without dexamethasone. We applied a Cox proportional hazards model with propensity score matching to estimate the effect of these treatments on clinical improvement by 28 days (discharge or a 2-point decrease in WHO severity score) and 28-day mortality. Measurements and Main ResultsOf 2485 COVID-19 patients admitted between March 4 and August 29, 2020, 342 received remdesivir and 157 received remdesivir plus dexamethasone. Median age was 60 years; 45% were female; 81% were non-white. Remdesivir recipients on room air or nasal cannula oxygen had a faster time to clinical improvement (median 5.0 days [IQR 4.0, 8.0], remdesivir vs. 7.0 days [IQR 5.0, 12.0], control; adjusted hazard ratio (aHR) 1.55 [1.28; 1.87]), yet those requiring higher levels of respiratory support did not benefit. Remdesivir recipients had lower, but statistically insignificant, 28-day mortality (7.6% [23 deaths], remdesivir vs. 14.9% [45 deaths], control). Adding dexamethasone trended toward lower 28-day mortality compared to remdesivir alone (5.1% [8 deaths] vs. 9.2% [17 deaths]; aHR 0.14 [0.02; 1.03]). ConclusionsRemdesivir offered a significantly faster time to clinical improvement among a cohort of predominantly non-white patients hospitalized with COVID-19, particularly with mild-moderate disease. Remdesivir plus dexamethasone may reduce mortality.

Multiplexed proteomics and imaging of resolving and lethal SARS-CoV-2 infection in the lung (preprint)

Normal tissue physiology and repair depends on communication with the immune system. Understanding this communication at the molecular level in intact tissue requires new methods. The consequences of SARS-CoV-2 infection, which can result in acute respiratory distress, thrombosis and death, has been studied primarily in accessible liquid specimens such as blood, sputum and bronchoalveolar lavage, all of which are peripheral to the primary site of infection in the lung. Here, we describe the combined use of multiplexed deep proteomics with multiplexed imaging to profile infection and its sequelae directly in fixed lung tissue specimens obtained from necropsy of infected animals and autopsy of human decedents. We characterize multiple steps in disease response from cytokine accumulation and protein phosphorylation to activation of receptors, changes in signaling pathways, and crosslinking of fibrin to form clots. Our data reveal significant differences between naturally resolving SARS-CoV-2 infection in rhesus macaques and lethal COVID-19 in humans. The approach we describe is broadly applicable to other tissues and diseases.

Evidence of antigenic imprinting in sequential Sarbecovirus immunization (preprint)

Antigenic imprinting, which describes the bias of antibody response due to previous immune history, can influence vaccine effectiveness and has been reported in different viruses. Give that COVID-19 vaccine development is currently a major focus of the world, there is a lack of understanding of how background immunity influence antibody response to SARS-CoV-2. This study provides evidence for antigenic imprinting in Sarbecovirus, which is the subgenus that SARS-CoV-2 belongs to. Specifically, we sequentially immunized mice with two antigenically distinct Sarbecovirus strains, namely SARS-CoV and SARS-CoV-2. We found that the neutralizing antibodies triggered by the sequentially immunization are dominantly against the one that is used for priming. Given that the impact of the background immunity on COVID-19 is still unclear, our results will provide important insights into the pathogenesis of this disease as well as COVID-19 vaccination strategy.

IgM autoantibodies recognizing ACE2 are associated with severe COVID-19 (preprint)

SARS-CoV-2 infection induces severe disease in a subpopulation of patients, but the underlying mechanisms remain unclear. We demonstrate robust IgM autoantibodies that recognize angiotensin converting enzyme-2 (ACE2) in 18/66 (27%) patients with severe COVID-19, which are rare (2/52; 3.8%) in hospitalized patients who are not ventilated. The antibodies do not undergo class-switching to IgG, suggesting a T-independent antibody response. Purified IgM from anti-ACE2 patients activates complement. Pathological analysis of lung obtained at autopsy shows endothelial cell staining for IgM in blood vessels in some patients. We propose that vascular endothelial ACE2 expression focuses the pathogenic effects of these autoantibodies on blood vessels, and contributes to the angiocentric pathology observed in some severe COVID-19 patients. These findings may have predictive and therapeutic implications.

A Common Methodological Phylogenomics Framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones (preprint)

We present a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer patient. The commonality is in the phylogenetic retrodiction based on the genomic reads in both scenarios. While there is evidence of heteroplasmy, i.e., multiple lineages of SARS-CoV-2 in the same COVID-19 patient; to date, there is no evidence of sublineages recombining within the same patient. The heterogeneity in a patient's tumor is analogous to intra-patient heteroplasmy and the absence of recombination in the cells of tumor is a widely accepted assumption. Just as the different frequencies of the genomic variants in a tumor presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that so do the different variant frequencies in the viral reads, offering the means to infer the multiple co-infecting sublineages. We describe the Concerti computational framework for inferring phylogenies in each of the two scenarios. To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. We also make some additional discoveries. We uncovered new potential parallel mutation in the evolution of the SARS-CoV-2 virus. In the context of cancer, we uncovered new clones harboring resistant mutations to therapy from clinically plausible phylogenetic tree in a patient.

The Strand-biased Transcription of SARS-CoV-2 and Unbalanced Inhibition by Remdesivir (preprint)

SARS-CoV-2, a positive single-stranded RNA virus, caused the COVID-19 pandemic. Although its sense-mRNA architecture was reported, its anti-sense strand was unexplored. Here, we deeply sequenced both strands of RNA and found SARS-CoV-2 transcription is strongly biased to form the sense strand. During negative strand synthesis, apart from canonical sub-genomic ORFs, numerous non-canonical fusion transcripts are formed, driven by 3-15 nt sequence homology scattered along the genome but more prone to be inhibited by SARS-CoV-2 RNA polymerase inhibitor Remdesivir. The drug also represses more of the negative than the positive strand synthesis as supported by a mathematic simulation model and experimental quantifications. Overall, this study opens new sights into SARS-CoV-2 biogenesis and may facilitate the anti-viral drug design.

Inhibition of SARS-CoV-2 viral entry in vitro upon blocking N- and O-glycan elaboration (preprint)

The Spike protein of SARS-CoV-2, its receptor binding domain (RBD), and its primary receptor ACE2 are extensively glycosylated. The impact of this post-translational modification on viral entry is yet unestablished. We expressed different glycoforms of the Spike-protein and ACE2 in CRISPR-Cas9 glycoengineered cells, and developed corresponding SARS-CoV-2 pseudovirus. We observed that N- and O-glycans had only minor contribution to Spike-ACE2 binding. However, these carbohydrates played a major role in regulating viral entry. Blocking N-glycan biosynthesis at the oligomannose stage using both genetic approaches and the small molecule kifunensine dramatically reduced viral entry into ACE2 expressing HEK293T cells. Blocking O-glycan elaboration also partially blocked viral entry. Mechanistic studies suggest multiple roles for glycans during viral entry. Among them, inhibition of N-glycan biosynthesis enhanced Spike-protein proteolysis. This could reduce RBD presentation on virus, lowering binding to host ACE2 and decreasing viral entry. Overall, chemical inhibitors of glycosylation may be evaluated for COVID-19.

Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs (preprint)

The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poses a severe threat to humanity. Rapid and comprehensive analysis of both pathogen and host sequencing data is critical to track infection and inform therapies. In this study, we performed unbiased metatranscriptomic analysis of clinical samples from COVID-19 patients using a newly-developed RNA-seq library construction method (TRACE-seq), which utilizes tagmentation activity of Tn5 on RNA/DNA hybrids. This approach avoids the laborious and time-consuming steps in traditional RNA-seq procedure, and hence is fast, sensitive and convenient. We demonstrated that TRACE-seq allowed integrated characterization of full genome information of SARS-CoV-2, putative pathogens causing coinfection, antibiotic resistance and host response from single throat swabs. We believe that the integrated information will deepen our understanding of pathogenesis and improve diagnostic accuracy for infectious diseases.

Quantitative Assays Reveal Cell Fusion at Minimal Levels of SARS-CoV-2 Spike Protein and Fusion-from-Without (preprint)

Cell entry of the pandemic virus SARS-CoV-2 is mediated by its spike protein S. As main antigenic determinant, S protein is in focus of antibody-based prophylactic and therapeutic strategies. Besides particle-cell fusion, S mediates fusion between infected and uninfected cells resulting in syncytia formation. Here we present quantitative assay systems covering not only particle-cell and cell-cell fusion, but also demonstrating fusion-from-without (FFWO), the formation of syncytia induced by S-containing viral particles in absence of newly synthesized S protein. Based on complementation of split {beta}-galactosidase and virus-like-particles (VLPs) displaying S protein, this assay can be performed at BSL-1. All three assays provided readouts with a high dynamic range and signal-to-noise ratios covering several orders of magnitude. The data obtained confirm the enhancing effect of trypsin and overexpression of angiotensin-converting enzyme 2 (ACE2) on membrane fusion. Neutralizing antibodies as well as sera from convalescent patients inhibited particle-cell fusion with high efficiency. Cell-cell fusion, in contrast, was only moderately inhibited despite requiring much lower levels of S protein, which were below the detection limit of flow cytometry and Western blot. The data indicate that syncytia formation as a pathological consequence in tissues of Covid-19 patients can proceed at low levels of S protein and may not be effectively prevented by antibodies.

Patient trajectories and risk factors for severe outcomes among persons hospitalized for COVID-19 in the Maryland/DC region (preprint)

BackgroundRisk factors for poor outcomes from COVID-19 are emerging among US cohorts, but patient trajectories during hospitalization ranging from mild-moderate, severe, and death and the factors associated with these outcomes have been underexplored. MethodsWe performed a cohort analysis of consecutive COVID-19 hospital admissions at 5 Johns Hopkins hospitals in the Baltimore/DC area between March 4 and April 24, 2020. Disease severity and outcomes were classified using the WHO COVID-19 disease severity ordinal scale. Cox proportional-hazards regressions were performed to assess relationships between demographics, clinical features and progression to severe disease or death. Results832 COVID-19 patients were hospitalized; 633 (76.1%) were discharged, 113 (13.6%) died, and 85 (10.2%) remained hospitalized. Among those discharged, 518 (82%) had mild/moderate and 116 (18%) had severe illness. Mortality was statistically significantly associated with increasing age per 10 years (adjusted hazard ratio (aHR) 1.54; 95%CI 1.28-1.84), nursing home residence (aHR 2.13, 95%CI 1.41-3.23), Charlson comorbidity index (1.13; 95% CI 1.02-1.26), respiratory rate (aHR 1.13; 95%CI 1.09-1.17), D-dimer greater than 1mg/dL (aHR 2.79; 95% 1.53-5.09), and detectable troponin (aHR 2.79; 95%CI 1.53-5.09). In patients under 60, only male sex (aHR 1.7;95%CI 1.11-2.58), increasing body mass index (BMI) (aHR1.25 1.14-1.37), Charlson score (aHR 1.27; 1.1-1.46) and respiratory rate (aHR 1.16; 95%CI 1.13-1.2) were associated with severe illness or death. ConclusionsA combination of demographic and clinical features on admission is strongly associated with progression to severe disease or death in a US cohort of COVID-19 patients. Younger patients have distinct risk factors for poor outcomes.