COVID-19 CoRE Projects

Lead: Paula Mouser

Abstract: The ongoing pandemic of the novel coronavirus (SARS-CoV-2) poses a unique challenge for epidemiologists. There is growing evidence that the disease is more widespread than initially thought, with a large portion of the infected population presenting as asymptomatic. Methods for tracking the spread of the disease and its potential routes of exposure have generally been limited to the testing of suspected infections, which severely underestimates infection rates and geographic distribution. Novel tools for environmental surveillance of SARS-CoV-2 could greatly expand our ability to track the spread of the disease and its routes of transmission. Recent work from Europe and Boston suggests that environmental surveillance SARS-CoV-2 occurrence in human sewage may represent a complementary approach to assess the prevalence and density of SARS-CoV-2 occurrence when clinical testing is unavailable or underrepresented. Here, we develop and deploy a robust diagnostic methodology based on digital droplet PCR (ddPCR) to assess the distribution of SARS-CoV-2 across a broad range of environmental media, including municipal wastewaters. Once optimized, our ddPCR approach will be capable of quantifying SARS-CoV-2 in many other environmental samples (e.g., water, air, solids, surfaces). For example, applications include assessing the efficacy of wastewater treatment processes on viral removal; assessing human exposure via seafood ingestion or direct contact in recreational waters; or validating methods for disinfection such as during ultraviolet treatment of personal protective equipment. Such data will inform risk analysis and decision-making processes as well as decrease the burden of clinical testing. Moreover, it could provide a tool for early detection of the disease during its expected reoccurrence in the coming year(s). The proposed approach is scalable, and could also be applied for the detection of other viral infections.

Lead: Jim Malley

Abstract: This research grew out of our group providing pro bono assistance to professionals on the frontlines of the public health battle against COVID-19. This assistance identified research gaps that call for experiments to develop better understanding of temperature, relatively humidity, aerosol droplet size and chemistry as well as how critical properties of contaminated surfaces such as hydrophobicity, roughness, and porosity impact UV effectiveness. Short term research outcomes will optimize UV device operating conditions using viral surrogate data to reinforce the assistance provided to first responders fighting COVID-19 currently using UV disinfection to allow reuse of PPEs. Longer term research outcomes will develop sufficient preliminary, fundamental, experimental data for use in computational fluid dynamics (CFD) and light irradiance distribution (LID) modeling. Modeling will be conducted by Dr. Joel Ducoste’s group at North Carolina State University (NCSU). The data and modeling results will support follow-on proposals to NSF, NIH and/or other appropriate funding sources. Our research schedule is designed to provide immediate refinement of UV disinfection guidance for protection of frontline workers before the fall cold and flu season returns. The work will also inform applications of UV disinfection for surfaces and rooms in facilities such as classrooms allowed to reopen after COVID-19 metrics subside. Longer term, benefits from follow-on proposals will lead to refined models and improved approaches (e.g., novel UV devices) that can have transformational impacts on the selection, design and operation of UV technology for public health protection.

Lead: Harish Vashisth

Abstract: The proposed project aims to develop inhibitory approaches to block the activity of the main protease enzyme from highly pathogenic coronavirus (SARS-CoV2) causing COVID-19. To this end, we propose to apply an interdisciplinary computational and experimental investigation by combining methods rooted in atomistic modeling/simulations, Nuclear Magnetic Resonance (NMR) spectroscopy, and small-molecule synthesis that will be used to probe the conformational dynamics of the key protease enzyme with and without inhibitory small-molecules. The main driving question pertains to resolving the binding pose and interactions of inhibitors within the deeply buried binding pocket of the enzyme. Studying this question will be of profound significance for developing potent small-molecule drugs as novel therapeutic modalities to address the wide-spread public health threat originating from COVID-19 pandemic.

Lead: Sherine Elsawa

Abstract: The emergence of Coronavirus Disease 2019 (COVID-19), caused by the Betacoronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic that has infected ~3 million people and caused ~206,000 deaths as of April 26, 2020. Despite ongoing research efforts to develop COVID-19 therapeutics, no vaccine or therapy has been approved yet. One path to developing a therapy for critically ill patients is to engineer antibodies that neutralize the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) to bind SARS-CoV-2. Three antibodies, M396, S230 and 80R, that bind to the receptor binding domain (RBD) of SARS-CoV have been shown to have no experimentally measurable binding to SARS-CoV-2. Given the structural similarity between the viruses’ RBDs, it is likely that the antibodies have shapes that are complementary to binding SARS-CoV-2 but lack the hotspot interactions necessary to do so. Using a computational analysis of the predicted complexes of the antibodies with the RBDs revealed that SARS-CoV-2 mutations had disrupted two to three hotspot interactions in each neutralizing antibody. Further analysis identified antibody interface residues that can be mutated to introduce new hotspots with SARS-CoV-2, resulting in multiple mutation designs predicted to strongly bind SARS-CoV-2 for all three neutralizing antibodies. This data suggests that structure-guided engineering of SARS-CoV antibodies is a viable path to developing therapeutics for treating COVID-19. Here, we propose to validate the computational data using biological experiments in the laboratory. We will clone the M396 antibody region that binds to RBD of SARS-CoV-2 and perform the predicted mutations using site-directed mutagenesis technique. We will also clone, express and purify the RBD. We will test the binding of the RBD to the mutated antibodies to determine the mutations that results in enhanced binding. If successful, these results have the potential to be used to develop novel neutralizing antibody-based therapies for COVID-19 patients.

Leads: Bethany Silva and Alecia Magnifico

Abstract: To participate in contemporary life, students must learn to effectively navigate and create online information. In response to that need, even before COVID-19, New Hampshire had written state standards addressing the importance of teaching digital literacies. The onset of the pandemic, however, forced districts to quickly implement remote learning. This rapid shift to digital tools and teaching methods has revealed significant inequities in access to, resources for, and knowledge of digital literacies in our state — factors that will limit educational opportunity for many students. In response, the TILDE project will look across K16 education to investigate how K12 and college educators transitioned to remote learning. A team of university researchers and K16 educators will examine relationships among digital tools, remote learning, digital literacy curricula, and systemic inequities by co-designing and administering surveys and focus groups. We will pilot these instruments and collaboratively review and analyze these data to develop initial practical, actionable recommendations for state policy and teachers’ professional learning to enact more equitable implementation. 

Lead: Semra Aytur

Abstract: The purpose of this project is to convene UNH researchers and partners to identify populations at greatest risk for COVID-19 and its stress-related impacts, and to develop data-driven, place-based capacities that enable communities to adapt to a changing world. Social distancing and limiting public activity are the most effective actions that can be taken to prevent viral transmission, but these actions can place further strain on vulnerable individuals and families. We will utilize a complex socio-ecological systems (SES) approach that frames vulnerability in terms of three key drivers: (1) Exposure; (2) Sensitivity (susceptibility); and (3) Adaptive Capacity. Adaptive capacity includes individual, community, and system-level responses to a crisis that can increase resilience over time. Although the COVID-19 pandemic has catalyzed research around the first two components, there is a paucity of research that integrates all three components using interdisciplinary perspectives. This research will assess the dynamics underlying disparities in risk and resilience among different population sub-groups, as well as the socioeconomic and environmental factors driving those dynamics. The results of this project will inform data-driven insights to help protect public health, including the most vulnerable members of our communities.