Category: Notable Grants

Bacteria can evolve and become resistant to antibiotics that are used to treat infections. Antibiotic resistance is a serious global health challenge and is a factor in millions of deaths each year. The environment plays an important role in how resistant bacteria survive and spread. Scientists have found antibiotic-resistant bacteria can spread in aerosols in air. This CAREER project focuses on how the formation of aerosols influences the transport and spread of antibiotic-resistant microbes. It uses a flow-through chamber to simulate bacterial transport in air and to measure the response of bacteria, including the development of antibiotic resistance, to environmental conditions. The experiments are complemented with field studies and computational models to predict transport, exposure and risk in high-risk areas. The project also provides hands-on research opportunities and international training programs that connect engineering, environmental science, and public health. Project outcomes provide data needed to develop better strategies to slow the spread of antibiotic resistance around the world. The proposed project introduces both technical and conceptual innovations that advance the understanding of airborne antibiotic resistance beyond detection and transport. While aerosols have been increasingly recognized as a pathway for antibiotic resistance dissemination, their role as dynamic environments that shape bacterial adaptation remains largely unexplored. The project deploys a modular, bench-scale aerosol chamber capable of systematically manipulating environmental variables such as humidity, particulate load, and source characteristics, to isolate mechanisms driving resistance dynamics of microbial communities during aerosolization, transport, and deposition. The research distinguishes between physical transport, stress-induced adaptation, and horizontal gene transfer as contributors to resistance spread using culture-based assays, digital PCR for absolute quantification of resistance genes, and metagenomic and transcriptomic sequencing to resolve community composition and functional responses. Iterative coupling of laboratory experiments with field sampling campaigns in built and natural environments enables validation of mechanistic findings under environmentally relevant conditions. Data generated through the project informs ecological and transport-based conceptual models that treat aerosols not only as vectors but also as selective environments capable of shaping bacterial evolution. The resulting framework advances fundamental understanding of aeromicrobiology while establishing a scalable biotechnology platform that integrates engineered aerosol systems with advanced molecular tools to investigate antimicrobial resistance and a broad range of environmentally transmitted pathogens.

Funder: NSF 

Amount: $698,554 

PI: Olivia Ginn, College of Engineering 

Wolbachia are bacteria that are widespread endosymbionts found in more than half of all arthropod species, like all endosymbionts they potentially affect many aspects of their host biology. In particular, many Wolbachia ensure their own transmission to the next generation by selfishly manipulating the reproduction of their host as Wolbachia are primarily transmitted from the host mother to her offspring. This research investigates both short and long-term interactions between hosts and Wolbachia. Specifically, it aims to understand how Wolbachia manipulate host reproduction and how hosts co-evolve to resist these manipulations. The results will be of broad significance to both basic and applied questions in biology. First, given their abundance and effects on their arthropod hosts, endosymbionts including Wolbachia likely contribute to species diversification and broader patterns of biodiversity. The findings will help understand how endosymbionts contribute to the evolutionary and ecological processes that generate and maintain biodiversity. Second, Wolbachia are currently used for biocontrol of insect vectors that transmit human pathogens as well as of agricultural pests that cause harm to plants. The evolutionary dynamics that occur in natural host-endosymbiont interactions likely also occur in engineered biocontrol systems. The findings of this study will inform the potential consequences of wide-scale release of Wolbachia-infected insects, including both how the host evolves to modulate Wolbachia’s effects, as well as how Wolbachia co-evolves to result in strain replacements. Critical to completing the scientific aims is training the next generation of the STEM workforce. This research will provide mentored research opportunities for trainees of all levels, who will receive hands-on training in scientific research methods and communication. This project specifically investigates the genetic interactions and co-evolutionary dynamics between two sister Drosophila fly species and two Wolbachia strains. In the wild, D. recens is infected with two closely related strains of Wolbachia, both of which cause moderate cytoplasmic incompatibility, or the death of offspring of uninfected females and infected males, in this host. When transferred to the host D. subquinaria, one of these Wolbachia strains causes strong cytoplasmic incompatibility, while the other causes strong male killing, where the sons of infected females die. This project will result in key insights into i) the mechanisms by which endosymbionts cause reproductive manipulations, ii) the mechanisms by which hosts counteract these reproductive manipulations, and iii) the dynamics of how endosymbionts invade and spread through host populations. To complete the aims, the research will integrate field assays, molecular genetics, genetic mapping, population genetics, and laboratory assays.

Funder: NSF 

Amount: $1,229,568 

PI: Kelly Dyer, Franklin College of Arts and Sciences, Department of Genetics 

Myoblast fusion is essential for muscle development and regeneration. The long-term goal of our research is to identify key molecular regulators of this process, focusing on the role of CHAMP1 gene in CHAMP1 syndrome—a severe developmental disorder characterized by muscle weakness, hypotonia, and motor impairments, often requiring lifelong care. Our CRISPR-based screening, developed with R21 funding, identified CHAMP1 as a novel regulator of human myoblast fusion. We discovered a noncanonical function of CHAMP1 as a MyoD co-factor that promotes fusion by inducing the expression of muscle fusogen Myomaker. Deletion of CHAMP1 in patient cells and mouse models leads to impaired myoblast fusion, reduced Myomaker expression, and phenotypes resembling Myomaker-null conditions. To build on these findings, we aim to thoroughly investigate CHAMP1’s role in muscle development and function in vivo, utilizing tissue- and stage-specific mouse models that target both embryonic muscle precursors and adult myofibers. This involves detailed molecular and phenotypic analyses, assessing muscle morphology and functional performance. We will also leverage an unique transplantation model and employ the newest spatial transcriptomics tools to unbiasedly assess the consequence of patient mutations on human myoblast fusion. By this project, we will also elucidate the biochemical mechanisms by which CHAMP1 regulates Myomaker expression, focusing on its zinc finger motifs and interaction with the MyoD:TCF12:P300 complex. Our approach integrates RNA sequencing, CUT&Tag assays, protein interaction studies, and AlphaFold3-based structural modeling to map protein interfaces at the atomic level. Upon completing this study, we will not only gain critical insights into how CHAMP1 mutations cause fusion myopathy but also establish new animal models and molecular targets for studying and treating muscle symptoms in CHAMP1 syndrome. This project will benefit CHAMP1 patients in three distinct venues: 1) rationalizing the development of humanized Champ1 mutant mice for gene therapy testing to correct patient mutations, 2) restoring the expression of key target genes, such as Myomaker, that are affected in patient muscle cells, and 3) gaining structural insights into MyoD regulation by CHAMP1, enabling drug development to counteract MyoD inhibition caused by CHAMP1 mutations. This study will benefit from close interactions with the CHAMP1 Foundation, a patient advocacy group, to access new patient mutations, engage in the annual Family and Science Engagement Conference, and identify new collaborations when opportunities arise.

Funder: NIH 

Amount: $2,733,911 

PI: Pengpeng Bi, Franklin College of Arts and Sciences, Department of Genetics 

While personalized neoantigen vaccines have shown therapeutic promise, their efficacy is often impaired by immunoediting and immune suppression in established cancers. Preventive vaccination prior to cancer onset offers a compelling alternative, as shown by the success of HPV vaccines in preventing HPV-related cancers. However, effective prevention vaccines for most non-viral cancers remain elusive, largely due to the challenges of identifying antigens capable of eliciting broad protection across diverse cancer types. Public neoantigens are derived from driver hotspot mutations, presented by common major histocompatibility complex (MHC) alleles, and capable of eliciting dynamic T-cell response, making them promising targets for scalable, off-the-shelf vaccines. However, their subtle differences from self-peptides make them less immunogenic. Thus, developing a novel platform to induce strong, durable cell mediate immunity, and testing the platform in a relevant model system before human trials are essential. Commonly used rodent models for cancer prevention do not fully capture the heterogeneity, complexity, spontaneity, and often prolonged course of tumorigenesis in humans, complicating translational efforts. We propose to leverage pet dogs, particularly Golden Retriever (GR) pet dogs, as a novel alternative model system (NAMS) for cancer prevention, given their high spontaneous cancer incidence, shared driver hotspot mutations, and compatible MHC alleles with humans. We have identified 30 public neoantigen candidates, 48% of which are already validated and collectively have a potential to impact 0.41 billion people, and cloned them into an innovative, highly immunogenic parainfluenza virus 5-based self-amplifying virus-like particle (PIV5- saVLP) vaccine platform. Our central hypothesis is that vaccination with this novel platform in GRs will induce durable, protective immunity, serving as a critical translational step toward human cancer prevention. Specific aims include (1) conducting a vaccination trial in healthy GRs to assess safety, immunogenicity, and preliminary efficacy while characterizing immune correlates, and (2) identifying dog-human antigen-presentation equivalent MHC alleles and functionally equivalent, neoantigen-specific T cells to facilitate future clinical translation. This project will provide key insights into public neoantigen immunogenicity and cancer-preventive immunity in a relevant large-animal model, while establishing a blueprint for successful future human translation.

Funder: NIH 

Amount: $3,204,941 

PI: Shaying Zhao, Franklin College of Arts and Sciences, Department of Biochemistry and Molecular Biology 

Influenza vaccines remain the most cost-effective tool for reducing infection and disease burden. However, despite decades of research and development, the protection they provide is often suboptimal. A key challenge is the wide variability in vaccine induced immune responses among individuals, which is poorly understood. Addressing this knowledge gap is crucial for improving vaccine efficacy and vaccination strategies. Predictive modeling, with its demonstrated success in advancing medical research and public health, holds significant promise in this regard. By harnessing predictive models, we can tailor vaccination strategies to individuals, optimizing protection and improving outcomes. Current predictive models, however, suffer from critical limitations. Most rely solely on static, pre- vaccination data and focus narrowly on antibody responses to a single vaccine component (in current vaccines, these are H3N2, H1N1 and one or two of the B lineages). Existing models do not account for the dynamic nature of the immune system or the role of heterologous, non-vaccine-specific antibody responses, reducing their accuracy and practical utility. These shortcomings hinder the development of more comprehensive, adaptable models for predicting vaccine efficacy across diverse populations and viral strains. This project aims to overcome these limitations by developing and validating advanced predictive models for influenza vaccine responses. Our approach integrates systematically collected, longitudinal data with state-of-the-art statistical and machine-learning methods. Specifically, we will: 1. Develop predictive models that incorporate long-term antibody response trajectories and heterolo- gous strain data to improve predictions for vaccine strain responses. 2. Expand the scope of these models to predict heterologous breadth and overall antibody responses, offering a more complete understanding of vaccine-elicited immunity. 3. Generate and analyze high-throughput antibody landscape data to further refine and enhance our predictive models. By combining these innovations, we aim to establish a new framework for individualized vaccine re- sponse prediction. This framework will significantly improve upon existing models, enabling tailored vac- cination strategies designed to optimize protection for each individual. Ultimately, this work will provide a robust scientific foundation for guiding future influenza vaccination efforts, contributing to better public health outcomes worldwide.

Funder: NIH 

Amount: $3,886,738 

PI: Andreas Handel, College of Public Health 

This CAREER project will develop artificial intelligence (AI) tools to better understand and monitor neurological diseases. The research will combine brain images, genetic information, and clinical information. These are complex data sets with uncertainties, which makes it difficult to analyze them separately. The AI tools developed in this project will integrate and interpret the combined data. The resulting analysis will identify patient specific disease mechanisms and predict how diseases progress. The outcomes of the project will advance personalized healthcare, disease monitoring, and pattern detection. The project will also support education and hands-on training in biomedical AI, statistical inference, and modeling. Outreach activities include summer coding camps and data science workshops for high school students, and tutorials at national conferences. Software and models will be released as open-source tools to promote reproducibility and wide adoption. These activities will increase participation in biomedical engineering, train a skilled workforce, and improve public understanding of AI-enabled healthcare technologies. A unified, AI-driven framework for multimodal feature engineering will be used to model complex neurological diseases. The framework will combine machine learning (ML) with statistical causal inference to unify multimodal data across biological domains and timescales, producing interpretable representations that identify patient-specific mechanisms, predict individualized disease trajectories, and support precise diagnosis, treatment stratification, and real-time disease monitoring. The project will introduce three core system-level innovations: (1) domain-aware causal inference that integrates probabilistic and generative modeling to uncover latent features linking imaging, genetic, and clinical data; (2) customized transformer-based architectures that fuse structured and unstructured biomedical data, such as imaging embeddings, genetic variants, and clinical narratives, using cross-modal attention and contrastive learning strategies; and (3) domain-specialized large language models (LLMs) trained on multimodal features and biomedical text to translate complex outputs into interpretable clinical summaries. Reusable, open-source tools and models will be developed for clinicians and researchers, enabling analysis of large, heterogeneous biomedical datasets. Education and training activities will include interdisciplinary coursework in AI and causal inference for biomedical systems, hands-on research experiences, and outreach programs for high school learners.

Funder: NSF

Amount: $546,167

PI: Rongjie Liu, Franklin College of Arts and Sciences, Department of Statistics

The project is innovative in its approach to developing outreach education materials to empower high school students with essential skills in artificial intelligence (AI) and emerging food technologies. Through innovative educational modules, hands-on workshops, and an annual entrepreneurship contest, students will gain foundational knowledge and practical experience in AI, data science, and sustainability. Partnering with 4-H and FFA, the project targets a diverse group of students across urban and rural communities, with a focus on underrepresented populations. Educators from 4-H and FFA will receive professional development training to ensure effective program delivery and long-term sustainability. The project seeks to prepare the next generation of STEM professionals for careers in AI-driven agriculture and food systems, ultimately contributing to a more sustainable and innovative future.

Funder: USDA NIFA

Amount: $750,000

PI: Jason Peake, College of Agricultural and Environmental Sciences

The biological carbon pump plays an important role in the ocean’s carbon cycle. It transports carbon from the surface waters into the deep ocean where it supports ecosystems and sequesters carbon from the atmosphere. This carbon is transported by different pathways that are controlled by a complex mix of biological, physical, ecological, and biogeochemical processes operating on a range of spatial and temporal scales. The dominant pathway is the gravitational pump which is comprised of sinking particles, animal fecal pellets and large aggregates of smaller marine particles. As they sink, these particles are transformed and consumed by microbes and zooplankton, which can also fragment large pieces. These transformations attenuate the flux of carbon with depth, and the magnitude of this attenuation affects the timescale that carbon can be stored in the deep ocean. Estimating the carbon transported by the biological carbon pump globally requires a mixture of field data, remote sensing data, and computer models. The models use assumptions and simplifications to represent the complexity of processes affecting carbon in the oceans, and this leads to global estimates of the carbon export differing by more than a factor of two. In this project, we propose to use a combination of multivariate data analysis, modeling, remote sensing data, and field data to assess uncertainties in model predictions of the carbon flux by sinking particles. The models explicitly represent size-dependent processes that result in changes to the particle size distribution and particle flux. No new field data will be collected as part of the project; instead, we will use existing, publicly available field data and generated remote sensing data. The outcomes of this work will be an improved understanding and quantification of the uncertainties in modeling carbon fluxes within the biological pump. In particular, by systematically replacing in situ data with remote sensing data we will be able to identify and quantify how model predictions change when remote sensing data are used, and which in situ variables are key for accurate modeling of the biological pump. Implementing our mechanistic particle flux model in a global 3-D model will provide us with improved representations of crucial particle processes for use in other global models.

Funder: NASA

Amount: $945,355

PI: Adrian Burd, Franklin College of Arts and Sciences, Department of Marine Sciences

Vertebrate animals determine whether the gonads develop into male testes or female ovaries through a variety of ways, yet scientists still do not fully understand how these processes evolved or how they operate across different species. Reptiles, in particular, display an extraordinary range of gonad-determining systems, making them an important group for uncovering fundamental principles of biology. This project focuses on Anolis lizards, which have an ancient genetic system of gonad determination that is distinct from the genetic systems used by mammals and birds. By investigating how testis versus ovary formation is determined in these lizards, the research will provide new insights into the development of reproductive systems across vertebrates. This work will advance fundamental knowledge in genetics and developmental biology, while also supporting the training of undergraduate and graduate students, fostering hands-on research experiences, and expanding participation in science through collaborative training opportunities. These efforts contribute to the national interest by promoting scientific progress, strengthening the research workforce in gene editing biotechnology, and enhancing understanding of biological systems relevant to health and biodiversity.

Funder: NSF

Amount: $980,133

PI: Douglas Menke, Franklin College of Arts and Sciences, Department of Genetics

Immune checkpoint blockade has become a cornerstone treatment for many solid tumors. However, not all patients respond effectively to this therapy. To enhance its efficacy, researchers are exploring the combination of radiation and immunotherapy, a strategy known as radio-immunotherapy. Despite its promise, radio- immunotherapy has not consistently demonstrated clinical benefit, in part due to a lack of activated dendritic cells (DCs) within tumors. Among these immune cells, a specific subset known as cDC1s plays a crucial role in antigen cross-presentation to T cells in the tumor-draining lymph nodes (TDLNs) to initiate anti-tumor immune responses. Emerging studies suggest that cDC1s in the TDLNs are also critical targets for immune checkpoint inhibitors. Tumors resistant to immunotherapy or radio-immunotherapy often exhibit low cDC1 levels, and their migration to TDLNs can be impeded by the immunosuppressive tumor microenvironment. This project aims to develop a calcium nanoparticle-based combinatory treatment to enhance radio- immunotherapy by improving both tumor infiltration and TDLN migration of cDC1s. We hypothesize that surface- engineered calcium nanoparticles, termed αCaNPs, can selectively deliver calcium, a key second messenger for immune cell migration, into circulating cDC1s, thereby enhancing their tumor infiltration in response to chemotactic signals. In addition, we anticipate that αCaNPs, by elevating cytosolic calcium levels, will promote cDC1 maturation inside tumors and afferent migration to TDLNs. Under immune checkpoint blockade with PD- L1 antibody, we expect that cDC1s will prime T cells in the TDLNs, leading to clonal expansion and robust anti- tumor immunity. To evaluate the efficacy of this approach, we will use murine models of head and neck squamous cell carcinoma (HNSCC). If successful, our technology could be extended to other cancers currently under intensive clinical investigation for radio-immunotherapy

Funder: NIH

Amount: $3,016,765

PI: Jin Xie, College of Engineering and Franklin College of Arts and Sciences, Department of Chemistry