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NSF-ANR MCB/PHY: Virus self-assembly, from test tube to cell cytoplasm

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Coming out of the most severe and destructive viral pandemic of the past 100 years, the importance of understanding how viruses “work” is clear. Most viruses – including polio, yellow fever, Dengue, and SARS, etc. – have RNA genomes that are quickly turned or “translated” into viral proteins in host cells that self-assembled into new virus particles called capsids. Elucidating how this process happens is a high priority for preventing and treating these infections. This project sets out to connect in vivo experiments carried out in live cells with in vitro experiments carried out in a test tube with purified viral capsid proteins and RNA genome. While test tube studies allow for full control of the types and numbers of components and solution conditions in which they are interacting, live cells studies, on the other hand, involve viral RNA and capsid proteins in the presence of many unknown components whose effects on RNA translation and self-assembly into capsids have not yet been determined. The fundamental understanding that results from this research will enhance the ability to develop anti-viral treatments. Graduate students will be trained in an inter-/cross-disciplinary range of physical, chemical, biological, and translational medicine concepts and methods. Active outreach efforts aim at enhancing interest and understanding of science amongst budding scientists and lay persons of all kinds will be conducted.    This project will be performed by an international collaboration between five different research groups in the US and France, each specializing in different experimental and theoretical techniques and each having extensive experience with one or the other of the plant (cowpea chlorotic mottle virus [CCMV]) and mammalian (hepatitis B [HepB]) viruses under study. These viruses were chosen because how significantly they differ in their host cell and capsid structure, so that general principles of viral self-assembly can be established. It is the goal of this project to elucidate the differences between in vitro and in cellulo viral processes by progressively adding to RNA and capsid protein a series of molecules that play key roles in the viral “life” cycle, mimicking the crowded interior of the cell. Using cell-free cytoplasmic (ribosome-rich) extract, viral RNA will be translated into protein products and the time course of capsid assembly will be investigated by a combination of experimental techniques, including magnetic resonance, X-ray scattering, and fluorescence and electron microscopies. Coarse-grained molecular dynamics computations and phenomenological theory will be used to analyze these kinetic data and to compare with what is learned using the same experimental techniques applied to corresponding virus assembly in test tubes, where all concentrations and solution conditions are controlled.     This collaborative US/France project is supported by the US National Science Foundation and the French Agence Nationale de la Recherche, where NSF funds the US investigator and ANR funds the partners in France.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Conference: Building Teams to Build Better Epidemiological Models: Balancing Participation from Mathematical and Social, Behavioral, and Economic Sciences

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This award will support two separate one-day virtual conferences entitled “Building Teams to Build Better Epidemiological Models: Balancing Participation from Mathematical and Social, Behavioral, and Economic Sciences” (https://sites.duke.edu/betterepidemiologicalmodelsconference/), to be held in January 2025. In a crisis such as the COVID-19 pandemic, mathematical models played their role in designing, developing, deploying, and evaluating public health strategies with different levels of success. Still, all were confronted with prioritizing public health or economic viability. To frame a sound pandemic response strategy, mathematical models are primary tools that must incorporate behavioral components and frameworks to be more efficient and useful for public health policy interventions and the evaluation of the economic impact of such measures. The COVID-19 pandemic highlights the need to develop mathematical methodologies, new techniques, and innovative approaches designed to incorporate the new paradigm of behavioral dynamics into the transmission dynamics of human diseases. Multidisciplinary teams are needed to innovate new mathematical methodologies which incorporate human behavioral and social dynamics. This award will be used to support a conference to bring together mathematical and social / behavioral / economic scientists to develop improved epidemiological models which can protect both public health and the economy. Applicants will be selected to balance these research areas, with attention given during the selection process to ensure that women and members of underrepresented groups are fully considered with an eye to broadening participation.    The standard framework for the mathematical modeling of infectious diseases is the basic Kermack-McKendrick model, a compartmental model framed in ordinary differential equations and their extensions to stochastic and hybrid models. Mixing is a random process in this framework, and this characteristic has pervaded in models for prediction and forecasting and is one, but not unique, of the most challenging and important topics in modeling infectious diseases: how to modify the basic assumption of the homogeneous population in the model to incorporate significant behavioral effects robustly and effectively. For example, there have been several efforts in literature to integrate behavior; one of them is the one that assumes that agents that interact during the transmission of the disease are rational, i.e., the individuals behave in a way consistent with a rational evaluation of risks. This model type is based on economic thinking in which costs and benefits are balanced, where there is a trade-off that rational agents resolve. The problem in epidemiology is that many of the actions of natural agents during an epidemic do not adapt to this hypothesis; therefore, applying this type of modeling requires the development of innovative ideas, alternative conceptual frameworks, and new mathematical techniques and methodologies. Scientific teams which can innovate and parameterize mathematical models which are tractable, represent an analogue of human behavior and transmission, work across a variety of domains and settings, and can be used to test interventions are needed.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Excellence in Research: a PEC-AbP Dual Signal Amplification Method and its Mechanistic Study of Signal Transduction for DNA Sensing

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North Carolina Agricultural & Technical State University

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DNA sensing techniques have been widely applied in daily life such as medical diagnosis, biowarfare defense, forensic science, and environmental monitoring, and were significantly promoted during the past pandemic, e.g., reverse transcription polymerase chain reaction (RT-PCR) test for COVID-19. Rapid DNA detection with high sensitivity, specificity, and accuracy is in high demand, however limited by signal readout. This project is aimed at developing an innovative dual signal amplification method by integrating two different signal amplification methods, i.e., materials science- and optical-based. The research goals are to strengthen signal readouts and build field-friendly DNA sensors that are amenable to point-of-need applications with ultrasensitivity. The discovery of fundamental science and transformative technology will potentially enable a reliable multiplexed high-throughput DNA analysis platform that may greatly benefit health care in society and facilitate research and applications in biomedical and life science. The scientific learning of this interdisciplinary research performed at the HBCU (NC A&T) and MSI (UNC Greensboro) will advance sensing mechanism understanding, instruct and train students especially underrepresented students, in research and education, and engage K-12 STEM educators and students in science.    Genetic information with or without variation coded within nucleic acids, indicating an illness or health outcome, is termed a nucleic acid biomarker, thus plays a crucial role in precision medicine. Sensitive and selective detection of nucleic acid biomarkers with rapid signal amplification is the key for early screening and diagnosis of human diseases. This project is aimed at developing an innovative dual signal amplification method and understanding the signal transduction mechanism for enhanced DNA sensing. The work is built on the seamless integration between amplification-by-polymerization (AbP) in DNA sensing for optical clarity change on surface based on effective mass growth upon DNA recognition and in-planar metallic film nanoarrays for plasmon-exciton coupling (PEC) optical enhancement. The research will be conducted in three stages to (1) fully explore the potential of the AbP-PEC dual signal amplification platform, (2) investigate the fundamental mechanism of the amplified signal transduction pertaining to the AbP-produced film thickness and plasmonic nanoslit structure, and (3) optimize the AbP-PEC platform for a portable DNA sensor in point-of-care diagnostics. The outcome may be transformative towards a multiplexed, rapid, highly sensitive, visible (by naked eyes) analysis of DNA in biofluids.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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I-Corps: Translation Potential of Safe Biomedical Perfusion Technologies

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Technology, Innovation and Partnerships (TIP)

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The broader impact of this I-Corps project is the development of a safety control system for biomedical perfusion applications. Perfusion involves the circulation of an external fluid through a body compartment, often using surgically inserted catheters. Currently, multiple safety risks exist during perfusion procedures, including the risk of blood flow occlusion during high-pressure perfusion as well as the risk of tissue damage resulting from high perfusion and/or drainage flowrates. Many existing and emerging biomedical applications involve the use of perfusion, especially through the abdominal cavity. These applications include peritoneal dialysis, hyperthermic intraperitoneal chemotherapy (HIPEC), and peritoneal oxygenation.  This technology is designed to address the safety risks, which may have the potential to improve the safety of biomedical perfusion and potentially improve patients’ outcomes.     This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a control algorithm for the safe perfusion of oxygenated perfluorocarbons through the peritoneal cavity. Perfusion plays a critical role in both existing life-saving biomedical interventions (such as peritoneal dialysis, hyperthermic intra-peritoneal chemotherapy, and extra-corporeal membrane oxygenation) as well as emerging interventions (such as peritoneal oxygenation). This technology utilizes perfusion flowrate, pressure, and volume sensors to estimate and manage multiple perfusion safety hazards simultaneously. These technologies were developed as an emerging medical intervention where oxygen-rich liquids are perfused through patients’ abdomens to enable oxygen transport into the bloodstream via diffusion.  This technology has the potential to allow the abdomen to serve as a “third lung”, particularly for patients with respiratory failure due to ailments such as COVID-19. The solution will improve the safety during peritoneal oxygenation and more broadly during any biomedical perfusion application.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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I-Corps: Translation Potential of an Enhanced Fluorescence-based Diagnostic Technology for the Detection of Lyme Disease

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Technology, Innovation and Partnerships (TIP)

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The broader impact of this I-Corps project is the development of an enhanced fluorescence-based diagnostic technology for the detection of   antibodies and other biomarkers for disease diagnostics. The base technology has been demonstrated for diagnosing high profile diseases including COVID-19 and Lyme disease. For this I-Corps effort, Lyme disease has been chosen as the beachhead market due to the current diagnostic challenges, and the growing market for fast and accurate Lyme disease diagnostic technologies. The accepted standard for Lyme disease, known as standard two-tiered testing (STTT) is time consuming, requires specialists to run, and can be unreliable, especially for early stages of the disease. This technology has proven to alleviate these pain points, providing rapid and accurate Lyme disease diagnosis, especially for early Lyme disease patients. The platform has also been utilized for detecting RNA-protein and DNA-protein interactions, which potentially broadens its utility for a large number of different disease diagnostic applications, biomarker discovery, and biological / pharmaceutical research applications. The technology may have impact in several clinical and biological research fields.    This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a photonics-based Lyme disease diagnostic platform. Lyme disease is the most common vector-borne disease in the United States and, despite advances, remains a considerable diagnostic challenge. Confirmatory diagnosis requires a second test, performed in series, often in batches, and at a centralized laboratory. This delay can lead to considerable morbidity in disseminated Lyme disease. This technology is a low-cost, highly sensitive, fluorescence-based platform, which provides a rapid, easy-to-use, and highly accurate Lyme test that could be used outside traditional clinical laboratories or for more rapid and accurate diagnosis within clinical laboratories. The proof-of-principle research positions the technology as a rapid (<40 minutes total test time) and reliable alternative to traditional Lyme tests, while retaining the full sophistication of a two-tiered testing system.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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I-Corps: Translation Potential of an Online Healthcare Information (OHI) Trust Badge

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Technology, Innovation and Partnerships (TIP)

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The broader impact of this I-Corps project is the development of an online knowledge recommender tool and trust badge for consumers. Health misinformation remains a serious societal threat. Since the emergence of the COVID-19 pandemic, reports show on average that 8 out of 10 Americans search for online healthcare information (OHI), and 4 out of 10 Americans cannot correctly identify false healthcare claims. The goal of the new technology is to help alleviate confusion amongst consumers caused by the overwhelming amount of OHI, and to help OHI providers boost their reputation as a trustworthy source. The tool is designed to combat misinformation by proactively serving a wide spectrum of stakeholders who regularly deal with OHI content. The I-Corps project will focus on the specific issues and public challenges of endorsements in addition to fact checking of OHI content and contributing to a better understanding of the needs of people who use and/or provide OHI content. This solution serves as a foundation for a consultancy service providing platform offering advice plus training to OHI consumers and OHI providers.    This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a software tool that will serve online healthcare information (OHI) users by providing machine learning-based classification and certification of OHI content trustworthiness. Research has shown that machine learning-based classifiers can process OHI claims and classify them as fact or fake, but such solutions have not been directly integrated into web browsers and have been trained with primarily textual cues from mostly unimodal datasets. This technology addresses these limitations and is designed as a machine learning driven online knowledge recommender tool, prototyped as a web extension utility, which can be directly embedded into web browsers to seamlessly report trustworthiness of any OHI content. the solution is designed as a trust badge model for easy certification of web content and can function both as an online content classifier. This capability may allow both OHI consumers and OHI providers to validate and tag OHI websites' trustworthiness. Additionally, the solution is trained with multimodal data, that includes both textual and visual cues (e.g., image elements, graphic contents, and infographics), unlike existing solutions that do not include visual cues or image artifacts.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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SBE-UKRI: Resource Rational Contractualism: A foundation for moral judgment and decision making

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Often people need to make moral decisions in cases where no clear rules apply. For instance, during the COVID pandemic people often faced difficult tradeoffs and choices in situations where no clear rules existed. Philosophers have suggested that one promising approach is for a person to act in the way that they believe everybody would agree to, if there were time for everybody to talk things over.     This research project asks whether ordinary people imagine a kind of bargain between interested parties and regard the imagined outcome of the bargain as the most morally appropriate solution. The project develops precise computational models of how people do this, and then tests the models by conducting experiments. These experiments ask people to reason about everyday situations, and also puts people in structured economic exchanges with each other to explore whether their choices reflect basic principles of bargaining. Finally, the project embeds bargaining principles within current artificial intelligence (AI)    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Collaborative Research: RESEARCH-PGR: Extracellular RNA Produced By Plants: What, Where, How, Who, and Why?

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This project investigates the role of secreted RNA in the immune system of plants.  The Innes and Meyers laboratories recently discovered that the leaves of plants accumulate RNA in the spaces between cells and on their surfaces.  Although we usually think of RNA as a molecule that can direct cells to synthesize specific proteins (e.g., the mRNA in COVID vaccines directs our cells to make SARS-CoV2 spike protein), some RNAs serve other functions.  Analysis of the base sequences of plant extracellular RNAs revealed that these RNAs are diverse in sequence, but do not appear to encode proteins.  The discovery of extracellular non-coding RNA in plants raises two fundamental questions that this project will address:  1) how do plants secrete RNA? and 2) what is the function of this RNA?  It takes a large amount of energy for cells to secrete RNA, thus this secreted RNA must benefit the plant in some way.  This project will test the hypothesis that secreted RNA functions to protect plants from infection by fungi and bacteria.  If this hypothesis is correct, the proposed research will enable generation of crop plants with improved immune systems that are more resistant to disease.  Such crops are needed to feed a growing global population in a sustainable manner, while reducing the environmental impacts of agriculture.    The Innes and Meyers laboratories recently discovered that the apoplast of Arabidopsis leaves contains abundant long non-coding RNAs, including circular RNAs, as well as small RNAs. These RNAs are bound to protein particles, which protects them against degradation. Notably, this extracellular RNA (exRNA) is highly enriched in the post-transcriptional modification N6-methlyadenine (m6A). These discoveries raise fundamental questions about plant biology: Are there specific exRNAs that are broadly conserved across plant species? How are exRNAs secreted, and are post-transcriptional modifications central to this process? And why do plants produce exRNAs? Do they play a fundamental role in plant-microbe interactions? To answer these questions, exRNA will be purified from the apoplast and leaf surfaces of seven diverse species: Arabidopsis, soybean, tomato, lettuce, pineapple, rice, and maize, which were chosen based on their phylogenetic diversity, genomic resources, importance as crops, and diversity in physiology. These exRNAs will be analyzed using both RNA-seq and sRNA-seq, which will allow identification of RNAs that are conserved between species. To assess whether m6A or other modifications are required for secretion, transgenic plants that express exRNAs that lack modification sites will be tested for their secretion efficiency. To investigate additional requirements for exRNA secretion, the exRNA content in Arabidopsis and rice plants with mutations in known RNA binding proteins and secretory pathway genes will be analyzed. Lastly, to assess whether exRNAs contribute to immunity, mutants compromised in exRNA secretion will be tested for resistance to fungal and bacterial pathogens.    This award was co-funded by the Plant Genome Research Program and the Plant Biotic Interactions Program in the Division of Integrative Organismal Systems.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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PIPP Phase I: Develop and Evaluate Computational Frameworks to Predict and Prevent Future Coronavirus Pandemics

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Old Dominion University Research Foundation

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Once a novel coronavirus or a new variant is detected, how likely would the novel coronavirus or new variant transmit from person to person, and how sick could patients become? What kind of new coronaviruses could cause future pandemics? Knowing the answers to these questions can help nations make proper strategic decisions. The dilemma is how to predict the behavior and pathogenic severity of new viruses as early as possible. A team of researchers thinks they have found ways to answer these questions by developing new artificial intelligence software tools to predict the virus’s behaviors based on its genome sequence. This team of researchers recognizes the potential bias in machine learning applications and the need to increase diversity in the future artificial intelligence workforce. Leveraging their expertise in genomics, data science, artificial intelligence, genetics, infectious disease, chemical engineering, public health, and communication, this team of researchers will organize training workshops and activities providing culturally responsive teaching of artificial intelligence,  data science training to teachers,  and context-relevant coding experiences to high school students. The team will promote public trust in science and discernment of misinformation through community outreach.     This research team will prototype a deep learning model based on biological knowledge and hypotheses that can predict viral pathogenic fitness from genomic sequences to test the potential rules for viral pathogenicity. The team will explore several methods to correct the sampling bias in viral genomic surveillance in order to accurately estimate the fitness of a viral strain. The team will investigate the mutation and recombination profiles in all available bat coronavirus genomes from the Southeastern Asia and build a prototype geospatial model to predict the recombination probability for all available bat coronaviruses. Leveraging their expertise in genetics and macromolecular structure modeling, the team will test a few candidate genes in SARS-CoV-2 for potential pathogenic rules. Based on the outcomes of these pilot projects, the team will be able to estimate the pathogenic fitness of an emerging SARS-CoV-2 variant or another novel coronavirus.     This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE).    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Preparing for the Future of the STEM Teacher Workforce in the 21st Century: Leveraging Multi-contextual Evidence

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This project aims to identify trends in the demographics and turnover behavior of the STEM teacher workforce. It focuses on specific remedies and investments needed to retain prospective teachers in high-need schools, especially rural communities, and to improve outcomes for their students. The project includes two complementary studies, one using the National Teacher and Principal Survey (an update from the earlier Schools and Staffing Survey) and a second, using longitudinal administrative data from the states of Kansas and Missouri. The project will explore a range of questions, including: how STEM teacher demographics, turnover intentions, and actual turnover may have changed nationally due to the COVID-19 pandemic; and how the Great Recession of 2007-08 and the COVID-19 pandemic may have influenced teacher outcomes. This body of research will produce insights to inform actionable recruitment and retention practices for high-need school districts and future research focused on teacher labor markets.    The conceptual framework and proposed analyses build upon a model of teacher turnover that suggests three main categories of factors that drive teacher turnover: teacher factors, school factors, and external factors and events. Leveraging this framework, the investigators group the research questions and analyses into three broad themes: STEM teacher characteristics, the school and student characteristics in which STEM teachers are employed, and contemporary secular trends that impact STEM teachers. They also consider the interplay between the STEM teacher characteristics and the school context in which STEM teachers work. The investigating team will employ descriptive and regression analyses to answer the research questions. Across interconnected lines of inquiry, the researchers will balance national generalizability with comprehensive state-specific application to inform current and future practice, policy, and research.    This project is supported by the EHR Core Research(ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent issues in STEM education.    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.