The oral drug nitazoxanide restricts SARS-CoV-2 infection and attenuates disease pathogenesis in Syrian hamsters

A well-tolerated and cost-effective oral drug that blocks SARS-CoV-2 growth and dissemination would be a major advance in the global effort to reduce COVID-19 morbidity and mortality. Here, we show that the oral FDA-approved drug nitazoxanide (NTZ) significantly inhibits SARS-CoV-2 viral replication and infection in different primate and human cell models including stem cell-derived human alveolar epithelial type 2 cells. Furthermore, NTZ synergizes with remdesivir, and it broadly inhibits growth of SARS-CoV-2 variants B.1.351 (beta), P.1 (gamma), and B.1617.2 (delta) and viral syncytia formation driven by their spike proteins. Strikingly, oral NTZ treatment of Syrian hamsters significantly inhibits SARS-CoV-2-driven weight loss, inflammation, and viral dissemination and syncytia formation in the lungs. These studies show that NTZ is a novel host-directed therapeutic that broadly inhibits SARS-CoV-2 dissemination and pathogenesis in human and hamster physiological models, which supports further testing and optimization of NTZ-based therapy for SARS-CoV-2 infection alone and in combination with antiviral drugs.

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajimas D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

Human airway lineages derived from pluripotent stem cells reveal the epithelial responses to SARS-CoV-2 infection

There is an urgent need to understand how SARS-CoV-2 infects the airway epithelium and in a subset of individuals leads to severe illness or death. Induced pluripotent stem cells (iPSCs) provide a near limitless supply of human cells that can be differentiated into cell types of interest, including airway epithelium, for disease modeling. We present a human iPSC-derived airway epithelial platform, composed of the major airway epithelial cell types, that is permissive to SARS-CoV-2 infection. Subsets of iPSC-airway cells express the SARS-CoV-2 entry factors ACE2 and TMPRSS2. Multiciliated cells are the primary initial target of SARS-CoV-2 infection. Upon infection with SARS-CoV-2, iPSC-airway cells generate robust interferon and inflammatory responses and treatment with remdesivir or camostat methylate causes a decrease in viral propagation and entry, respectively. In conclusion, iPSC-derived airway cells provide a physiologically relevant in vitro model system to interrogate the pathogenesis of, and develop treatment strategies for, COVID-19 pneumonia. Highlights and eTOC blurbO_LISubsets of human iPSC-airway epithelial cells express SARS-Co-V entry factors ACE2 and TMPRSS2. C_LIO_LIiPSC-airway cells are permissive to SARS-CoV-2 infection via multiciliated cells. C_LIO_LISARS-CoV-2 infection of iPSC-airway leads to a robust interferon and inflammatory response. C_LIO_LIiPSC-airway is a physiologically relevant model to study SARS-CoV-2 infection. C_LI

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

1The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFN{gamma}+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

SARS-CoV-2 Infection of Pluripotent Stem Cell-derived Human Lung Alveolar Type 2 Cells Elicits a Rapid Epithelial-Intrinsic Inflammatory Response

The most severe and fatal infections with SARS-CoV-2 result in the acute respiratory distress syndrome, a clinical phenotype of coronavirus disease 2019 (COVID-19) that is associated with virions targeting the epithelium of the distal lung, particularly the facultative progenitors of this tissue, alveolar epithelial type 2 cells (AT2s). Little is known about the initial responses of human lung alveoli to SARS-CoV-2 infection due in part to inability to access these cells from patients, particularly at early stages of disease. Here we present an in vitro human model that simulates the initial apical infection of the distal lung epithelium with SARS-CoV-2, using AT2s that have been adapted to air-liquid interface culture after their derivation from induced pluripotent stem cells (iAT2s). We find that SARS-CoV-2 induces a rapid global transcriptomic change in infected iAT2s characterized by a shift to an inflammatory phenotype predominated by the secretion of cytokines encoded by NF-kB target genes, delayed epithelial interferon responses, and rapid loss of the mature lung alveolar epithelial program. Over time, infected iAT2s exhibit cellular toxicity that can result in the death of these key alveolar facultative progenitors, as is observed in vivo in COVID-19 lung autopsies. Importantly, drug testing using iAT2s confirmed an antiviral dose-response to remdesivir and demonstrated the efficacy of TMPRSS2 protease inhibition, validating a putative mechanism used for viral entry in human alveolar cells. Our model system reveals the cell-intrinsic responses of a key lung target cell to infection, providing a physiologically relevant platform for further drug development and facilitating a deeper understanding of COVID-19 pathogenesis.

Human iPSC-derived alveolar and airway epithelial cells can be cultured at air-liquid interface and express SARS-CoV-2 host factors

Development of an anti-SARS-CoV-2 therapeutic is hindered by the lack of physiologically relevant model systems that can recapitulate host-viral interactions in human cell types, specifically the epithelium of the lung. Here, we compare induced pluripotent stem cell (iPSC)-derived alveolar and airway epithelial cells to primary lung epithelial cell controls, focusing on expression levels of genes relevant for COVID-19 disease modeling. iPSC-derived alveolar epithelial type II-like cells (iAT2s) and iPSC-derived airway epithelial lineages express key transcripts associated with lung identity in the majority of cells produced in culture. They express ACE2 and TMPRSS2, transcripts encoding essential host factors required for SARS-CoV-2 infection, in a minor subset of each cell sub-lineage, similar to frequencies observed in primary cells. In order to prepare human culture systems that are amenable to modeling viral infection of both the proximal and distal lung epithelium, we adapt iPSC-derived alveolar and airway epithelial cells to two-dimensional air-liquid interface cultures. These engineered human lung cell systems represent sharable, physiologically relevant platforms for SARS-CoV-2 infection modeling and may therefore expedite the development of an effective pharmacologic intervention for COVID-19.

Reconstructing SARS-CoV-2 response signaling and regulatory networks

Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s). Software and interactive visualizationhttps://github.com/phoenixding/sdremsc

Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly identified and translated to clinical care. Traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious agents against SARS-CoV-2. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 17 dose-responsive compounds with in vitro antiviral efficacy in human liver Huh7 cells and confirmed antiviral efficacy in human colon carcinoma Caco-2, human prostate adenocarcinoma LNCaP, and in a physiologic relevant model of alveolar epithelial type 2 cells (iAEC2s). Additionally, we found that inhibitors of the Ras/Raf/MEK/ERK signaling pathway exacerbate SARS-CoV-2 infection in vitro. Notably, we discovered that lactoferrin, a glycoprotein classically found in secretory fluids, including mammalian milk, inhibits SARS-CoV-2 infection in the nanomolar range in all cell models with multiple modes of action, including blockage of virus attachment to cellular heparan sulfate and enhancement of interferon responses. Given its safety profile, lactoferrin is a readily translatable therapeutic option for the management of COVID-19. IMPORTANCESince its emergence in China in December 2019, SARS-CoV-2 has caused a global pandemic. Repurposing of FDA-approved drugs is a promising strategy for identifying rapidly deployable treatments for COVID-19. Herein, we developed a pipeline for quantitative high-throughput image-based screening of SARS-CoV-2 infection in human cells that led to the identification of several FDA-approved drugs and clinical candidates with in vitro antiviral activity.