Main Areas of Focus

• Provide high performance computing infrastructure & training to researchers across Africa
• Empower researchers and their students to utilize computing resources for advanced biomedical data analysis
• Build better technical capacity for research and foster data sharing to improve the quality of infectious disease science within the African continent
• Partner with other sponsors to build a network of self-sustaining centers that can tap into other sources of funding

To support the generation of data for the SIG, NIAID has created the SARS-CoV-2 Assessment of Viral Evolution (SAVE) risk-assessment pipeline for SARS-CoV-2 variant viruses. SAVE provides a comprehensive real-time risk assessment of emerging mutations in SARS-CoV-2 that could impact transmissibility, virulence, and susceptibility to infection- or vaccine-induced immunity. The goals of the SAVE program are to understand emerging changes in the virus that could impact transmissibility, virulence, susceptibility to convalescent and vaccine-induced immunity and diagnostic testing. SAVE is one of the data streams that provides important information for variant characterization and complements other USG and NIH efforts like the ACTIV Tracking Resistance and Coronavirus Evolution (TRACE) initiative.   

The NIAID SAVE program is composed of an international team of scientists with expertise in virology, immunology, vaccinology, structural biology, bioinformatics, viral genetics, and evolution. Researchers from NIAID Intramural, the NIAID Vaccine Research Center, other HHS and Department of Defense laboratories, and the extramural academic community work collaboratively within and across multiple sub-groups to accelerate the pace of variant research and discovery through rapid and open sharing. Researchers are developing high quality data sets that are then vetted by SIG working groups comprised of various experts. These data help inform public health recommendations.

More information on the SAVE program can be found in the publication, "Defining the risk of SARS-CoV-2 variants on immune protection" (Nature, 2022).

Credit: NIAID

NIAID’s activities are divided into the following data-generating components:

Early Detection and Variant Analysis

Lead: Elodie Ghedin, Ph.D., NIAID and Derek Smith, Ph.D., Cambridge University

This team is evaluating surveillance/epidemiological data, information from observational studies on the efficacy of COVID-19 vaccines, variant information from documented vaccine breakthrough cases and accompanying sequence data from these efforts to identify variants that should be evaluated further in vitro and in vivo. They will also perform antibody escape mapping in the lab using monoclonal antibodies and polyclonal sera in pseudoviral assays to identify proactively substitutions of concern.

In vitro Characterization

Lead: Florian Krammer, Ph.D., Icahn School of Medicine at Mount Sinai and Mehul Suthar, Ph. D., Emory University

This team characterizes variants identified by the early detection group comprehensively in vitro to determine how they may differ from the original SARS-CoV-2 virus. Variant viruses, pseudoviruses and various convalescent and vaccinee sera samples are used to determine if variants are able to evade infection- or vaccine-induced immunity. Groups are also performing antigenic landscape analyses to determine the relatedness of each emerging variant to the original strain to which vaccines were made. Researchers also analyze characteristics such as in vitro replication kinetics to determine how variant viruses may have different characteristics that affect their ability to replicate or transmit between humans. Groups are also exploring if any variants can evade cellular immune responses generated by memory B-cells and T-cells. The data generated by this groups helps determine which variants are of most concern for our existing countermeasures.

In vivo Characterization

Lead: Michael Diamond, M.D., Ph.D., and Jacco Boon, Ph.D., Washington University in St. Louis

This team is using animal models to estimate virulence changes in circulating SARS-CoV-2 viruses and evaluate vaccine efficacy against variant viruses. Researchers use various animal models to examine if any variants cause different levels of disease in models. Researchers also vaccinate or infect animals to determine if animals remain protected from infection with variant viruses over different periods of time.  Finally, researchers are working to study ability of countermeasures such as vaccines to prevent transmission in animal models.