An in vitro workflow to create and modify infectious clones using replication cycle reaction.

Reverse genetics systems are critical tools in combating emerging viruses which enable a better understanding of the genetic mechanisms by which viruses cause disease. Traditional cloning approaches using bacteria are fraught with difficulties due to the bacterial toxicity of many viral sequences, resulting in unwanted mutations within the viral genome. Here, we describe a novel in vitro workflow that leverages gene synthesis and replication cycle reaction to produce a supercoiled infectious clone plasmid that is easy to distribute and manipulate. We developed two infectious clones as proof of concept: a low passage dengue virus serotype 2 isolate (PUO-218) and the USA-WA1/2020 strain of SARS-CoV-2, which replicated similarly to their respective parental viruses. Furthermore, we generated a medically relevant mutant of SARS-CoV-2, Spike D614G. Results indicate that our workflow is a viable method to generate and manipulate infectious clones for viruses that are notoriously difficult for traditional bacterial-based cloning methods.

Vaccine Effectiveness during Outbreak of COVID-19 Alpha (B.1.1.7) Variant in Men's Correctional Facility, United States.

In April 2021, a COVID-19 outbreak occurred at a correctional facility in rural Virginia, USA. Eighty-four infections were identified among 854 incarcerated persons by facilitywide testing with reverse transcription quantitative PCR (qRT-PCR). We used whole-genome sequencing to link all infections to 2 employees infected with the B.1.1.7α (UK) variant. The relative risk comparing unvaccinated to fully vaccinated persons (mRNA-1273 [Moderna, https://www.modernatx.com]) was 7.8 (95% CI 4.8-12.7), corresponding to a vaccine effectiveness of 87.1% (95% CI 79.0%-92.1%). Average qRT-PCR cycle threshold values were lower, suggesting higher viral loads, among unvaccinated infected than vaccinated cases for the nucleocapsid, envelope, and spike genes. Vaccination was highly effective at preventing SARS-CoV-2 infection in this high-risk setting. This approach can be applied to similar settings to estimate vaccine effectiveness as variants emerge to guide public health strategies during the ongoing pandemic.

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively;n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively;n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N;IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N;IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales. Wastewater samples, population estimates, and case outcome data from individual buildings can be used to monitor and predict COVID-19 trends.

The Pro-Inflammatory Chemokines CXCL9, CXCL10 and CXCL11 Are Upregulated Following SARS-CoV-2 Infection in an AKT-Dependent Manner.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible RNA virus that is the causative agent of the Coronavirus disease 2019 (COVID-19) pandemic. Patients with severe COVID-19 may develop acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) and require mechanical ventilation. Key features of SARS-CoV-2 induced pulmonary complications include an overexpression of pro-inflammatory chemokines and cytokines that contribute to a 'cytokine storm.' In the current study an inflammatory state in Calu-3 human lung epithelial cells was characterized in which significantly elevated transcripts of the immunostimulatory chemokines CXCL9, CXCL10, and CXCL11 were present. Additionally, an increase in gene expression of the cytokines IL-6, TNFα, and IFN-γ was observed. The transcription of CXCL9, CXCL10, IL-6, and IFN-γ was also induced in the lungs of human transgenic angiotensin converting enzyme 2 (ACE2) mice infected with SARS-CoV-2. To elucidate cell signaling pathways responsible for chemokine upregulation in SARS-CoV-2 infected cells, small molecule inhibitors targeting key signaling kinases were used. The induction of CXCL9, CXCL10, and CXCL11 gene expression in response to SARS-CoV-2 infection was markedly reduced by treatment with the AKT inhibitor GSK690693. Samples from COVID-19 positive individuals also displayed marked increases in CXCL9, CXCL10, and CXCL11 transcripts as well as transcripts in the AKT pathway. The current study elucidates potential pathway specific targets for reducing the induction of chemokines that may be contributing to SARS-CoV-2 pathogenesis via hyperinflammation.