An observatory for infection genomics
2021 Strategic Research Initiatives
Nguyen (CEE), Irudayaraj (Bioengineering), Beck and Kesavadas (IESE), Basar (ECE), King (MechSE), Koyejo (CS), Whitaker (IGB), Smith (Vetmed), Keefer (PRI)
We propose an observatory for infection genomics for the Midwestern region of the US. This observatory will consist of a sensor network created based on understanding the evolution of pathogens and antimicrobial resistance (AMR) at the gene level and the factors controlling pathogen and AMR evolution and transmission in livestock, wildlife, human, and the surrounding environments. Pathogen surveillance exists but is inadequate because of the lack of understanding on what to target, what temporal and spatial scale should be applied, and what population or environments are at risk. The undetected jump of SARS-CoV-2 from animal reservoirs to humans leading to COVID-19 pandemic is a timely example of the gap between current needs and state-of-the-art diagnostic and pathogen surveillance technologies. We propose that future national policies on biothreats should be formulated to preempt and mitigate pandemics by better understanding the long-term evolution and transmission of pathogens to design and interpret surveillance programs. We propose to address this by building a large-scale observatory for infection genomics and microbial hosts. The proposed observatory will provide access to sequencing, computational power, instruction, and technology to satellite labs that apply to collaborate at the University of Illinois and explore infectious genomics in their own research systems. We envision that U of I will serve as a science and technology hub for an observatory of infection genomics (Fig. 1). This hub is built upon the convergence of the science that enables the development of sensor and sensor network technology to track the evolution of infectious genomics, expertise on environmental monitoring, and computational framework to predict the transmission of the microbial hosts. Only at this scale, with this breath of comparative data analyzed in a unified way, and with convergent science approaches that cross taxonomic and disciplinary lines, will we be able to establish generalized evolutionary rules of infection genomics that apply across all domains of life and a new evolutionary framework guiding monitoring and modeling of infectious diseases.
We propose a work plan that will show NSF or other funding agencies that we have the expertise and have already conducted preliminary framework for this observatory. We will track transmission of AMR genes and pathogens from swine farms through meatpacking plants to workers, their living environment, and surrounding communities. This task will be accomplished by analyzing sewage samples from the following sewer collection points for Rantoul Foods (site 1), a mobile home park where Rantoul Foods workers live (site 2), a neighborhood in Rantoul without the workers (site 3), Campustown in Champaign (where undergraduate students live, site 4), Prairie school neighborhood in Urbana (site 5), Centennial school neighborhood in Champaign (site 6), and Garden Hill elementary school neighborhood in Champaign (site 7). These locations were selected based on the high infection rate of SARS-CoV2, suggesting that residents are susceptible to infection and communities that are likely favorable for other microbial pathogen transmissions. Campustown represents a location with a mobile population (U of I undergraduate students) without connection to the meatpacking plant. This task will be built upon the Labor Health Equity Action Project on identifying social factors on COVID-19 infection in marginalized communities in CUPHD.
Convergence of expertise will be discussed by a virtual workshop for center-level proposal preparation. The workshop and the following up communication will design to build teams - the initial nucleus for a national center focusing on developing an evolution-guided network of on-site on-demand sensors for infection genomics at the neighborhood levels and the next generation of epidemic modeling tools.