Genomic Surveillance of a Canadian Airport Wastewater Samples Allows Early Detection of Emerging SARS-CoV-2 Lineages (preprint)

The SARS-CoV-2 pandemic has shown that wastewater (WW) surveillance is an effective means of tracking the emergence of viral lineages in communities, arriving by many routes including via transportation hubs. In Ontario, numerous municipal WWTPs participate in WW surveillance of infectious disease targets such as SARS-CoV-2 by qPCR and whole genome sequencing (WGS). The Greater Toronto Airports Authority (GTAA), operator of Toronto Pearson International Airport (Toronto Pearson), has been participating in WW surveillance since January 2022. As a major international airport in Canada and the largest national hub, this airport is an ideal location for tracking globally emerging SARS-CoV-2 variants of concern (VOCs). In this study, WW collected from Toronto Pearson’s two terminals and pooled aircraft sewage was processed for WGS using a tiled-amplicon approach targeting the SARS-CoV-2 virus. Data generated was analyzed to monitor trends SARS-CoV-2 lineage frequencies. Initial detections of emerging lineages were compared between Toronto Pearson WW samples, municipal WW samples collected from the surrounding regions, and Ontario clinical data as published by Public Health Ontario. Results enabled the early detection of VOCs and individual mutations emerging in Ontario. On average, emergence of novel lineages at the airport ahead of clinical detections was 1–4 weeks, and up to 16 weeks. This project illustrates the efficacy of WW surveillance at transitory transportation hubs and sets an example that could be applied to other viruses as part of a pandemic preparedness strategy and to provide monitoring on a mass scale.

Learning novel SARS-CoV-2 lineages from wastewater sequencing data (preprint)

Background:Wastewater surveillance of SARS-CoV-2 has emerged as a critical tool for tracking the spread of COVID-19.In addition to estimating the relative case numbers using quantitative PCR, SARS-CoV-2 genomic RNA can be extracted from wastewater and sequenced.There are many existing techniques for using the sequenced RNA to determine the relative abundance of known lineages in a sample.However, it is very challenging to predict novel lineages from wastewater data due to its mixed composition and unreliable genomic coverage.Results:In this work, we present a novel technique based on non-negative matrix factorization which is able to extract novel lineage definitions by analyzing data from across different samples.We test the method both on synthetic and real wastewater sequencing data.We show that the technique is able to determine major lineages such as Omicron and Delta as well as sub-lineages such as BA.5.2.1.Conclusions:We provide a method for determining emerging lineages in wastewater without the need for genomic data from clinical samples. This could be used for routine monitoring of SARS-CoV-2 as well as other emerging viral pathogens in wastewater. Additionally, it may be used to derive more sequences for viruses with fewer complete genomes.

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data (preprint)

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic "novel" lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances, and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1% frequency, results were more reliable above a 5% threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of noise or bias in wastewater sequencing data and to appreciate the commonalities and differences across methods.

A map of direct SARS-CoV-2 protein interactions implicates specific human host processes (preprint)

Key steps in viral propagation, immune suppression and pathology are mediated by direct, binary physical interactions between viral and host proteins. To understand the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we generated an unbiased systematic map of binary physical interactions between viral and host interactions, complementing previous co-complex association maps by conveying more direct mechanistic understanding and enabling targeted disruption of direct interactions. To this end, we deployed two parallel strategies, identifying 205 virus-host and 27 intraviral binary interactions amongst 171 host and 19 viral proteins, with orthogonal validation by an internally benchmarked NanoLuc two-hybrid system to ensure high data quality. Host proteins interacting with SARS-CoV-2 proteins were enriched in various cellular processes, including immune signaling and inflammation, protein ubiquitination, and membrane trafficking. Specific subnetworks provide new hypotheses related to viral modulation of host protein homeostasis and T-cell regulation. The direct virus-host protein interactions we identified can now be prioritized as targets for therapeutic intervention. More generally, we provide a resource of systematic maps describing which SARS-CoV-2 and human proteins interact directly.

A SARS-CoV-2 - host proximity interactome (preprint)

Viral replication is dependent on interactions between viral polypeptides and host proteins. Identifying virus-host protein interactions can thus uncover unique opportunities for interfering with the virus life cycle via novel drug compounds or drug repurposing. Importantly, many viral-host protein interactions take place at intracellular membranes and poorly soluble organelles, which are difficult to profile using classical biochemical purification approaches. Applying proximity-dependent biotinylation (BioID) with the fast-acting miniTurbo enzyme to 27 SARS-CoV-2 proteins in a lung adenocarcinoma cell line (A549), we detected 7810 proximity interactions (7382 of which are new for SARS-CoV-2) with 2242 host proteins (results available at covid19interactome.org). These results complement and dramatically expand upon recent affinity purification-based studies identifying stable host-virus protein complexes, and offer an unparalleled view of membrane-associated processes critical for viral production. Host cell organellar markers were also subjected to BioID in parallel, allowing us to propose modes of action for several viral proteins in the context of host proteome remodelling. In summary, our dataset identifies numerous high confidence proximity partners for SARS-CoV-2 viral proteins, and describes potential mechanisms for their effects on specific host cell functions.

Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms (preprint)

The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.

Silicon Nitride Inactivates SARS-CoV-2 in vitro (preprint)

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the COVID-19 pandemic, remains viable and therefore potentially infectious on several materials. One strategy to discourage the fomite-mediated spread of COVID-19 is the development of materials whose surface chemistry can spontaneously inactivate SARS-CoV-2. Silicon nitride (Si3N4), a material used in spine fusion surgery, is one such candidate because it has been shown to inactivate several bacterial species and viral strains. This study hypothesized that contact with Si3N4 would inactivate SARS-CoV-2, while mammalian cells would remain unaffected. MaterialsSARS-CoV-2 virions (2x104 PFU/mL diluted in growth media) were exposed to 5, 10, 15, and 20% (w/v) of an aqueous suspension of sintered Si3N4 particles for durations of 1, 5, and 10 minutes, respectively. Before exposure to the virus, cytotoxicity testing of Si3N4 alone was assessed in Vero cells at 24 and 48 hour post-exposure times. Following each exposure to Si3N4, the remaining infectious virus was quantitated by plaque assay. ResultsVero cell viability increased at 5% and 10% (w/v) concentrations of Si3N4 at exposure times up to 10 minutes, and there was only minimal impact on cell health and viability up to 20% (w/v). However, the SARS-CoV-2 titers were markedly reduced when exposed to all concentrations of Si3N4; the reduction in viral titers was between 85% - 99.6%, depending on the dose and duration of exposure. ConclusionsSi3N4 was non-toxic to the Vero cells while showing strong antiviral activity against SARS-CoV-2. The viricidal effect increased with increasing concentrations of Si3N4 and longer duration of exposure. Surface treatment strategies based on Si3N4 may offer novel methods to discourage SARS-CoV-2 persistence and infectivity on surfaces and discourage the spread of COVID-19.

A SARS-CoV-2 BioID-based virus-host membrane protein interactome and virus peptide compendium: new proteomics resources for COVID-19 research (preprint)

Key steps of viral replication take place at host cell membranes, but the detection of membrane-associated protein-protein interactions using standard affinity-based approaches (e.g. immunoprecipitation coupled with mass spectrometry, IP-MS) is challenging. To learn more about SARS-CoV-2 - host protein interactions that take place at membranes, we utilized a complementary technique, proximity-dependent biotin labeling (BioID). This approach uncovered a virus-host topology network comprising 3566 proximity interactions amongst 1010 host proteins, highlighting extensive virus protein crosstalk with: (i) host protein folding and modification machinery; (ii) membrane-bound vesicles and organelles, and; (iii) lipid trafficking pathways and ER-organelle membrane contact sites. The design and implementation of sensitive mass spectrometric approaches for the analysis of complex biological samples is also important for both clinical and basic research proteomics focused on the study of COVID-19. To this end, we conducted a mass spectrometry-based characterization of the SARS-CoV-2 virion and infected cell lysates, identifying 189 unique high-confidence virus tryptic peptides derived from 17 different virus proteins, to create a high quality resource for use in targeted proteomics approaches. Together, these datasets comprise a valuable resource for MS-based SARS-CoV-2 research, and identify novel virus-host protein interactions that could be targeted in COVID-19 therapeutics.

Elucidation of remdesivir cytotoxicity pathways through genome-wide CRISPR-Cas9 screening and transcriptomics (preprint)

The adenosine analogue remdesivir has emerged as a frontline antiviral treatment for SARS-CoV-2, with preliminary evidence that it reduces the duration and severity of illness1. Prior clinical studies have identified adverse events1,2, and remdesivir has been shown to inhibit mitochondrial RNA polymerase in biochemical experiments7, yet little is known about the specific genetic pathways involved in cellular remdesivir metabolism and cytotoxicity. Through genome-wide CRISPR-Cas9 screening and RNA sequencing, we show that remdesivir treatment leads to a repression of mitochondrial respiratory activity, and we identify five genes whose loss significantly reduces remdesivir cytotoxicity. In particular, we show that loss of the mitochondrial nucleoside transporter SLC29A3 mitigates remdesivir toxicity without a commensurate decrease in SARS-CoV-2 antiviral potency and that the mitochondrial adenylate kinase AK2 is a remdesivir kinase required for remdesivir efficacy and toxicity. This work elucidates the cellular mechanisms of remdesivir metabolism and provides a candidate gene target to reduce remdesivir cytotoxicity.

A Flexible Genome-Scale SARS-CoV-2 Clone Resource (preprint)

The world is facing a major health crisis, the global pandemic of COVID-19 caused by the SARS-CoV-2 coronavirus, for which no approved antiviral agents or vaccines are currently available. Here we describe a collection of codon-optimized coding sequences for SARS-CoV-2 cloned into Gateway-compatible entry vectors, which enable rapid transfer into a variety of expression and tagging vectors. The collection is freely available via Addgene. We hope that widespread availability of this SARS-CoV-2 resource will enable many subsequent molecular studies to better understand the viral life cycle and how to block it.