This program offers internal funds to support and grow collaborative One Health research projects across the University of Tennessee system. The goal of the program is to create transdisciplinary synergies among faculty, staff, students, and external collaborators that embrace a One Health approach to investigations.
In 2022, the One Health Initiative offered several small awards (up to $40,000), including one exploring healthy, living shorelines on the Tennessee River (*in partnership with Tennessee RiverLine) and one globally-focused award addressing one or more of the United Nations Sustainable Development Goals (**in partnership with UT’s Smith Center for International Sustainable Agriculture and the Center for Global Engagement).
2022 OHI Seed Grant Recipients
- PI: Dr. Madhu Dhar (College of Veterinary Medicine, Large Animal Clinical Sciences)
- Co-PI: Dr. Dayakar Penumadu (Tickle College of Engineering, Department of Civil and Environmental Engineering)
- PI: Dr. Shigetoshi Eda (Herbert College of Agriculture, Department of Forestry, Wildlife, and Fisheries)
- Co-PI: Dr. Doris D’Souza (Herbert College of Agriculture, Department of Food Science)
- Co-PI: Dr. Jayne Wu (Tickle College of Engineering, Department of Electrical Engineering and Computer Science)
- PI: Dr. Michael McKinney (College of Arts and Sciences, Department of Earth and Planetary Sciences)
- Co-PI: Dr. Andrea Ludwig (Herbert College of Agriculture, Department of Biosystems Engineering and Soil Sciences)
- Co-PI: Dr. John Schwartz (Tickle College of Engineering, Department of Civil and Environmental Engineering)
- Co-PI: Dr. Michael Ross (Herbert College of Agriculture, Department of Plant Sciences)
- Co-PI: Garrett Ferry (Facilities Services)
- PI: Dr. Jennifer Retherford (Tickle College of Engineering, Department of Civil and Environmental Engineering)
- Co-PI: Dr. Nan Gaylord (College of Nursing)
- Co-PI: Dr. Sara Mulville (Smith Center for International Sustainable Agriculture)
- Co-PI: Dr. David Ader (Smith Center for International Sustainable Agriculture)
- PI: Dr. Debasish Saha (Herbert College of Agriculture, Department of Biosystems Engineering and Soil Sciences)
- Co-PI: Dr. Subhadeep Chakraborty (Tickle College of Engineering, Department of Mechanical, Aerospace, and Biomedical Engineering)
- PI: Dr. Timothy Truster (Tickle College of Engineering, Department of Civil and Environmental Engineering)
- Co-PI: Dr. Pierre-Yves Mulon (College of Veterinary Medicine, Large Animal Clinical Sciences)
- Co-PI: Dr. David Anderson (College of Veterinary Medicine, Large Animal Clinical Sciences)
Abstract: Many challenges are faced by clinicians and researchers when making decisions regarding bone injury and the ability to restore normal form and function to the bone. Translational research focused on orthopedic applications such as biomedical implants carries substantial value, as numbers of individuals requiring orthopedic implants are continuously increasing. This study will develop and combine physics-based and machine-learning models for assessing the effect of bone defects using the goat tibia as a model. The finite element phase field fracture model provides a high-fidelity virtual laboratory to map 3D specimen scans into bone strength and microcrack profiles, and the Gaussian Process Regression (GPR) model provides a low-fidelity fast-sampling platform for optimizing the defect placement within a new specimen to maximize strength and minimize deleterious effect on bone health. Combining high- and low-fidelity models enables a nearly unlimited number of variations to be analyzed prior to undertaking a research study, aiding in the design of novel implants or in refinements of orthopedic surgery. Outcomes of this study would improve in vivo studies in animals, achieving the goal of reduce, refine and replace in animal research. Computational models also will enhance investigations of surgical techniques to improve the healing of defects in injured patients. Our long-term goals are to develop methods for computational estimation of the effect of risk factors, modifications to surgical model designs, and development of new surgical implants and approaches while reducing the reliance on first ex vivo and second in vivo pilot study modeling, thereby conserving resources.
2020 OHI Seed Grant Recipients
- PI: Dr. Michelle Dennis (College of Veterinary Medicine, Biomedical and Diagnostic Sciences)
- Co-PI: Dr. Nina Fefferman (College of Arts and Sciences, Department of Ecology and Evolutionary Biology, Department of Mathematics)
- Co-PI: Gerald Dinkins (McClung Museum of Natural History and Culture)
Abstract: Freshwater mussels are key species for riverine ecosystem health because of their indispensable roles in water purification and nutrient cycling. However, over the last several decades, mussel populations have been severely impacted across North America by poorly-understood mortality epidemics. Since 2016, seasonal mortality events have decimated mussels of the upper Clinch River, a location treasured for unique mussel biodiversity, including 20 federally endangered species. This study aims to determine likely causes of mortality in the Clinch River with an in-situ experiment that measures seasonal changes in the health of sentinel mussels maintained in silos at impacted sites. Observations of growth and survivorship, clinical signs of disease, hemolymph indices, histopathology, and bacterial microbiome shifts during the experiment will be paired with population dynamic data from previous mortality events. These data will help build predictive models considering observed spatiotemporal patterns and likely causative agents to draw conclusions about causes of mussel mass-moralities.
- PI: Dr. Matthew Gray (Herbert College of Agriculture, Dept. of Forestry, Wildlife, and Fisheries)
- Co-PI: Dr. Neelam Poudyal (Herbert College of Agriculture, Dept. of Forestry, Wildlife, and Fisheries)
- Co-PI: Dr. Nina Fefferman (College of Arts and Sciences, Dept. of Ecology and Evolutionary Biology, Dept. of Mathematics)
Abstract: The regional, national, and international trade of wildlife has facilitated the movement, spillover, and global emergence of numerous infectious diseases including SARS-CoV-2, viral hemorrhagic septicemia virus, and chytrid fungi. Wildlife pathogens and zoonoses have cost global economies trillions of dollars and substantial human life and biodiversity loss, leading to increasing calls to regulate or prohibit the trade of exotic wildlife. The wildlife trade industry exists as a network of nodes which can amplify or dampen pathogen dynamics and thus affect spillover risk. Each node in the network has their own economic and psychosocial values that affect their behaviors and practices, which in turn affect network behavior and subsequent pathogen dynamics. Further, consumers and government can affect network behavior via product demand, perceived values of biodiversity, and regulations or policies. This bidirectionally coupled system of spatially explicit disease processes and socioeconomic feedbacks in a hierarchical network presents unique challenges and novel opportunities to understand pathogen transmission and spillover risk. We hypothesize that socioeconomic incentives targeting industry and consumer behavior will alter emergent structural properties of a live animal trade network and influence patterns in pathogen dynamics. We will identify incentive structures that motivate nodes to alter behaviors and decrease pathogen spread in industry and spillover risk to wild populations. Understanding the social and economic incentives that shape the topology of disease networks requires a multi-scale approach that considers node and network factors driven by socioeconomic behavior, such as changes in consumer demand for particular species. Our overarching task is to characterize the amphibian trade network in the US by considering disease processes across two hierarchical scales with possible socioeconomic feedbacks.
- PI: Dr. Jun Lin (Herbert College of Agriculture, Department of Animal Science)
- Co-PI: Dr. Qiang He (Tickle College of Engineering, Department of Civil and Environmental Engineering)
Abstract: Escherichia albertii, often misidentified as Escherichia coli, has become an emerging human enteric pathogen. However, important animal and environmental reservoirs of this pathogen are still not clear. Based on recent preliminary studies, we hypothesize that chickens and raccoons are unique and significant players in the dynamic interactions among the enteric E. albertii pathogen, animals, humans, and their shared environment. To test this, we will perform a large scale study by pursuing following two aims: 1) examine the prevalence of E. albertii in US poultry production, wild raccoons, and surface waters; and 2) characterize the isolated E. albertii together with US human E. albertii strains using a panel of microbiological and genomics approaches. Completion of this project will provide insights into the ecology, evolution and transmission of E. albertii, and help us control the emerging E. albertii in a complex ecosystem using One Health approach.
- PI: Dr. Chunlei Su (College of Arts and Sciences, Department of Microbiology)
- Co-PI: Dr. Richard Gerhold (College of Veterinary Medicine, Biomedical and Diagnostic Sciences)
- Co-PI: Dr. Michelle Dennis (College of Veterinary Medicine, Biomedical and Diagnostic Sciences)
- Co-PI: Dr. Sree Rajeev (College of Veterinary Medicine, Biomedical and Diagnostic Sciences)
Abstract: Transmission of zoonotic diseases is affected by multiple transboundary factors including humans, animals, plants and the environment. Understanding the interplay of these factors is pivotal for the prevention and mitigation of these diseases. One essential step to reach this goal is to detect and identify zoonotic pathogens in clinical animals, reservoir hosts, and the environment, which provides critical information for surveillance. Currently, there is lack of an integrated system to investigate and conduct surveillance of multiple zoonotic pathogens simultaneously. Our One Health Initiative Research project is to address this problem by developing an integrated system to detect and identify zoonotic pathogens. This system aims to detect novel and known zoonotic pathogens from animals and the environment by metagenome sequencing and multiplex genotyping approaches. We expect this system will be highly scalable for surveillance of a large number of pathogens from animals, plants, humans and environment in the near future.
- PI: Dr. Brian Whitlock (College of Veterinary Medicine, Large Animal Clinical Sciences)
- Co-PI: Dr. Bhavya Sharma (College of Arts and Sciences, Department of Chemistry)
- Co-PI: Allison Renwick (College of Veterinary Medicine, Comparative and Experimental Medicine Program)
Abstract: Animals and humans frequently experience inflammation. Obesity has become a pandemic, causing chronic inflammation and impaired reproduction in humans. Lipopolysaccharide (LPS; endotoxin) is produced by bacteria and is responsible for the initiation of inflammation in obesity. The mechanism for impairment of reproduction in obese humans and animals likely involves reduction in neuropeptides in the hypothalamus . However, most research uses an acute model of inflammation; thus, there is a great need to develop a model of chronic inflammation. A valuable comparative model to evaluate the response to LPS in humans is sheep. They are similar in size, easy to handle, have large amounts of blood and tissue for collection and are physiologically similar. The focus of this study is to develop an animal model, using sheep, of chronic inflammation to determine its effects on hypothalamic neurons [KNDy (kisspeptin-neurokinin B-dynorphin) neurons] controlling reproduction.
- PI: Dr. Xinhua Yin (Herbert College of Agriculture, Department of Plant Sciences)
- Co-PI: Dr. Joshua Fu (Tickle College of Engineering, Department of Civil and Environmental Engineering)
- Co-PI: Dr. Sangeeta Bansal (Herbert College of Agriculture, Department of Plant Sciences)
Abstract: Influences of climate change on crop production are largely unknown in Tennessee, the USA, and the world. Cotton is a major crop in Tennessee and beyond. Forecasting of cotton yield under climate change is considered an advanced tool for future planning to assure global fiber security. The objective of this project is to assess the impacts of climate (temperature, precipitation) changes under two climate scenarios (baseline RCP4.5 and RCP8.5) on cotton production and estimate the performances of sustainable management practices under climate change via computational simulation. An existing long-term cotton field experiment in Jackson, TN will be used. This experiment was initiated in 1981 and has so far been continually conducted for 40 years. The state-of-the-art DSSAT computational model will be used. Through Extensional education, farmers will recognize the impacts of climate change on future cotton production, and will adopt relevant best management practices in cotton production under climate change.