Expansion of profibrotic monocyte-derived alveolar macrophages in patients with persistent respiratory symptoms and radiographic abnormalities after COVID-19 (preprint)

As many as 10-30% of the over 760 million survivors of COVID-19 develop persistent symptoms, of which respiratory symptoms are among the most common. To understand the cellular and molecular basis for respiratory PASC, we combined a machine learning-based analysis of lung computed tomography (CT) with flow cytometry, single-cell RNA-sequencing analysis of bronchoalveolar lavage fluid and nasal curettage samples, and alveolar cytokine profiling in a cohort of thirty-five patients with respiratory symptoms and radiographic abnormalities more than 90 days after infection with COVID-19. CT images from patients with PASC revealed abnormalities involving 73% of the lung, which improved on subsequent imaging. Interstitial abnormalities suggestive of fibrosis on CT were associated with the increased numbers of neutrophils and presence of profibrotic monocyte-derived alveolar macrophages in BAL fluid, reflecting unresolved epithelial injury. Persistent infection with SARS-CoV-2 was identified in six patients and secondary bacterial or viral infections in two others. These findings suggest that despite its heterogenous clinical presentations, respiratory PASC with radiographic abnormalities results from a common pathobiology characterized by the ongoing recruitment of neutrophils and profibrotic monocyte-derived alveolar macrophages driving lung fibrosis with implications for diagnosis and therapy.

Immune response to SARS-CoV-2 in the nasal mucosa in children and adults (preprint)

Rationale: Despite similar viral load and infectivity rates between children and adults infected with SARS-CoV-2, children rarely develop severe illness. Differences in the host response to the virus at the primary infection site are among the proposed mechanisms. Objectives: To investigate the host response to SARS-CoV-2, respiratory syncytial virus (RSV), and influenza virus (IV) in the nasal mucosa in children and adults. Methods: Clinical outcomes and gene expression in the nasal mucosa were analyzed in 36 children hospitalized with SARS-CoV-2 infection, 24 children with RSV infection, 9 children with IV infection, 16 adults with mild to moderate SARS-CoV-2 infection, and 7 healthy pediatric and 13 healthy adult controls. Results: In both children and adults, infection with SARS-CoV-2 leads to an interferon response in the nasal mucosa. The magnitude of the interferon response correlated with the abundance of viral reads and was comparable between symptomatic children and adults infected with SARS-CoV-2 and symptomatic children infected with RSV and IV. Cell type deconvolution identified an increased abundance of immune cells in the samples from children and adults with a viral infection. Expression of ACE2 and TMPRSS2 - key entry factors for SARS-CoV-2 - did not correlate with age or presence or absence of viral infection. Conclusions: Our findings support the hypothesis that differences in the immune response to SARS-CoV-2 determine disease severity, independent of viral load and interferon response at the primary infection primary site. Keywords: COVID-19, pneumonia, viral infections, interferons

Coronavirus associated molecular mimicry common to SARS-CoV-2 peptide (preprint)

Relationship of COVID-19 and immunity is complex and can involve autoimmune reactions through molecular mimicry. We investigated autoimmunity related pathological mechanisms involving molecular mimicry that are common to certain coronaviruses, including SARS-CoV-2, by means of a selected peptide sequence (CFLGYFCTCYFGLFC). Accordingly, coronavirus-associated sequences that are homologous to that 15mer sequence in the SARS-CoV-2 proteome are attained first. Then, homologous human and coronavirus sequences are obtained, wherein the coronavirus sequences are homologous to the 15mer SARS-CoV-2 peptide. All the identified query-subject sequences contained at least 7 residue matches in the aligned regions. Finally, parts of those coronavirus and host sequences, which are predicted to have high affinity to the same human leukocyte antigen (HLA) alleles as that of the SARS-CoV-2 sequence, are selected among the query and subject epitope-pairs that were both (predicted to be) strongly binding to the same HLA alleles. The proteins or the protein regions with those predicted epitopes include, but not limited to, immunoglobulin heavy chain junction regions, phospholipid phosphatase-related protein type 2, slit homolog 2 protein, and CRB1 isoform I precursor. These proteins are potentially associated with certain pathologies, but especially the possible CRB1 related coronavirus pathogenicity could be furthered by autoimmunity risk in HLA*A24:02 serotypes. Overall, results imply autoimmunity risk in COVID-19 patients with HLA*A02:01 and HLA*A24:02 serotypes in general, through molecular mimicry. This is also common to other coronaviruses than SARS-CoV-2. These results are indicative at the current stage, they need to be validated. Yet, they can pave the way to autoimmunity treatment options to be used in COVID-19 and its associated diseases.

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2 (preprint)

Biochemical phenotypes are major indexes for protein structure and function characterization. They are determined, at least in part, by the intrinsic physicochemical properties of amino acids and may be reflected in the protein three-dimensional structure. Modeling mutational effects on biochemical phenotypes is a critical step for understanding protein function and disease mechanism as well as enabling drug discovery. Deep Mutational Scanning (DMS) experiments have been performed on SARS-CoV-2's spike receptor binding domain and the human ACE2 zinc-binding peptidase domain - both central players in viral infection and evolution and antibody evasion - quantifying how mutations impact binding affinity and protein expression. Here, we modeled biochemical phenotypes from massively parallel assays, using convolutional neural networks trained on protein sequence mutations in the virus and human host. We found that neural networks are significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the intrinsic physicochemical properties of amino acids, including hydrophobicity, solvent-accessible surface area, and long-range non-bonded energy per atom, significantly improved prediction (empirical p<0.01) though there was such a strong dependence on the sequence data alone to yield reasonably good prediction. We observed concordance of the DMS data and our neural network predictions with an independent study on intermolecular interactions from molecular dynamics (multiple 500 ns or 1 s all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms. The mutation- or genetically- determined component of a biochemical phenotype estimated from the neural networks has improved causal inference properties relative to the original phenotype and can facilitate crucial insights into disease pathophysiology and therapeutic design.

Early therapy with remdesivir and antibody combinations improves COVID-19 disease in mice (preprint)

Improving the standard of clinical care for coronavirus disease 2019 (COVID-19) is a global health priority. Small molecule antivirals like remdesivir (RDV) and biologics such as human monoclonal antibodies (mAb) have demonstrated therapeutic efficacy against SARS-CoV-2, the causative agent of COVID-19. However, the efficacy of single agent therapies has not been comprehensively defined over the time course of infection and it is not known if combination RDV/mAb will improve outcomes over single agent therapies. In kinetic studies in a mouse-adapted SARS-CoV-2 pathogenesis model, we show that single-agent therapies exert potent antiviral effects even when initiated relatively late after infection, but their efficacy is diminished as a function of time. RDV and a cocktail of two mAbs in combination provided improved outcomes compared to single agents alone extending the therapeutic window of intervention with less weight loss, decreased virus lung titers, reduced acute lung injury, and improved pulmonary function. Overall, we demonstrate that direct-acting antivirals combined with potent mAb can improve outcomes over single agents alone in animal models of COVID-19 thus providing a rationale for the coupling of therapies with disparate modalities to extend the therapeutic window of treatment.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets (preprint)

Tremendous progress has been made to control the COVID-19 pandemic, including the development and approval of vaccines as well as the drug remdesivir, which inhibits the SARS-CoV-2 virus that causes COVID-19. However, remdesivir confers only mild benefits to a subset of patients, and additional effective therapeutic options are needed. Drug repurposing and drug combinations may represent practical strategies to address these urgent unmet medical needs. Viruses, including coronaviruses, are known to hijack the host metabolism to facilitate their own proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM). We find that SARS-CoV-2 infection can induce recurrent and complicated metabolic reprogramming spanning a wide range of metabolic pathways. We next applied the GEM-based metabolic transformation algorithm (MTA) to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. These predictions are enriched for validated targets from various published experimental drug and genetic screens. Further analyzing the RNA-sequencing data of remdesivir-treated Vero E6 cell samples that we generated, we predicted metabolic targets that act in combination with remdesivir. These predictions are enriched for previously reported synergistic drugs with remdesivir. Since our predictions are based in part on human patient data, they are likely to be clinically relevant. We provide our top high-confidence candidate targets for their evaluation in further studies, demonstrating host metabolism-targeting as a promising antiviral strategy.

SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral Spike vaccines (preprint)

The SARS-CoV-2 Spike glycoprotein mediates virus entry and is a major target for neutralizing antibodies. All current vaccines are based on the ancestral Spike with the goal of generating protective neutralizing antibodies. Several novel SARS-CoV-2 variants with multiple Spike mutations have emerged, and their rapid spread and potential for immune escape have raised concerns. One of these variants, first identified in the United Kingdom, B.1.1.7 (also called VUI202012/01), contains eight Spike mutations with potential to impact antibody therapy, vaccine efficacy and risk of reinfection. Here we employed a lentivirus-based pseudovirus assay to show that variant B.1.1.7 remains sensitive to neutralization, albeit at moderately reduced levels (~2-fold), by serum samples from convalescent individuals and recipients of two different vaccines based on ancestral Spike (mRNA-1273, Moderna, and protein nanoparticle (NVX-CoV2373, Novavax). Some monoclonal antibodies to the receptor binding domain (RBD) of Spike were less effective against the variant while others were largely unaffected. These findings indicate that B.1.1.7 is not a neutralization escape variant that would be a major concern for current vaccines, or for risk of reinfection.