The U.S. National Science Foundation has awarded over $12.7 million across nine research teams to understand better the untapped capabilities of ribonucleic acid (RNA) for potentially far-reaching biotechnology applications, from disease prevention in crops to cancer-fighting therapies. The nine teams will each receive $1 million to $1.65 million from NSF through the Molecular Foundations for Biotechnology (MFB) program, a joint effort of NSF in partnership with the National Institutes of Health’s National Human Genome Research Institute (NHGRI). NHGRI plans to invest in additional projects to be announced later in 2024, which will focus on the development of novel technologies to investigate RNA biology.
“Innovative new modes of inquiry into the molecular-level structure, dynamics and function of RNA is expected to lead to significant biotech breakthroughs at the intersection of chemistry and biology,” says NSF’s Chemistry Division Director David Berkowitz. “By advancing this fundamental science, we open the door to new avenues of use-inspired research and applications that can benefit society and improve our quality of life.”
“We are excited to partner with NSF to support research into the structures, interactions, functions and applications of RNA,” says Carolyn Hutter, Director of the Division of Genome Sciences at NHGRI. “New tools and technologies that harness RNA research have the potential to transform the biomedical field and improve human health.”
RNA is a complex organic molecule that performs essential tasks within the biological and chemical machinery of all living cells. Although RNA was first identified nearly a century ago, many of its functional aspects are not fully understood or predictable. The nine research teams will explore RNA’s roles and actions with the goal of creating new RNA-based methods for treating cancerous cells, making crops more resistant to blight and disease, fighting viral infections like the common cold and more. The teams include experts in a range of fields from chemistry, biology and physics to mathematical modeling and machine learning. Their projects are expected to provide opportunities to partner with industry to translate knowledge gained in the lab into marketable new biotechnologies.
In addition to supporting the research, NSF’s investment will provide hands-on training for students and early-career researchers through mentorship, workshops and internships for high school and undergraduate students and other activities to expand and broaden participation in the U.S. STEM workforce.
The nine projects and teams are:
Next-generation Proximity Labeling Technologies to Map Subcellular Transcriptomes and RNA Interactomes in Living Cells with Nanometer Resolution (Stanford University) aims to create new technologies to enable scientists to quickly visualize where RNAs localize within living cells and identify other genetic materials nearby that could interact with the RNAs; these technologies could be useful in studying in-cell interactions in diseases such as cancer.
Stabilizing Hairpin Inserts in RNA Virus Induced Gene Silencing Vectors (University of Maryland, College Park and Silvec Biologics) will work to create stable RNA genomes that can be used as delivery devices to disable the bacteria that cause diseases in plants, such as citrus greening; a serious plant disease that impacts economically and agriculturally important citrus trees internationally and for which there is no cure.
Cracking the Codes: Understanding the Rules of mRNA Localization and Translation (University of Colorado Denver) aims to use recent developments in RNA sequencing technology to create a model capable of predicting protein output from a messenger RNA, which could have a broad impact on what we understand about how genes encode and transmit information.
Better Homologous Folding using Computational Linguistics and Deep Learning (Oregon State University and the University of Rochester) seeks to use artificial intelligence to develop faster and better algorithms and software tools to model RNA secondary structures, which has the potential for advancing therapeutic and diagnostic design.
Characterization of the Biogenesis, Uptake, and Cellular Response to the Ribonucleoprotein Cargoes of Extracellular Vesicles using EV-CLASP (Vanderbilt University) will work to increase our understanding of extracellular vesicle-derived RNAs, which could enhance our ability to understand RNA dynamics during cellular communication, which would help identify novel gene regulatory elements and develop ways to deliver RNA treatments into cells.
RNA Modifications of Frameshifting Stimulators: Cellular Platforms to Engineer Gene Expression by Computational Mutation Predictions and Functional Experiments (New York University and the University of North Carolina at Chapel Hill) aims to predict and model how two proteins can be generated from the same messenger RNA with the goal of applying that knowledge to limit how RNA viruses can use this mutation to infect humans or to develop new forms of drug delivery
Evaluating and Advancing Cryo-EM for RNA Conformational Ensembles (Stanford University) will test whether cryogenic electron microscopy and computational methods can accurately visualize functionally critical features of RNA machines to create a validated toolkit that could help researchers develop models of a variety of RNA-based machines of biological or biotechnological interest.
Massively Parallel Identification of Translation Regulatory Sequences in Human and Viral mRNAs (Yale University) will take a systems-level approach to understand the various factors that impact the amount of protein synthesized from messenger RNAs, which could aid in designing new classes of therapeutic messenger RNAs.
Continuous Evolution of RNAs with Novel Functions in Mammalian Cells (Weill Cornell Medicine and Massachusetts Institute of Technology) aims to overcome the challenges in deploying functional RNA into live cells, which could transform biotechnology, biomedicine and biology broadly by allowing scientists to develop and deliver RNAs that can bind to target proteins in living cells.
The MFB program is a cross-disciplinary initiative led by NSF’s Directorates for Mathematical and Physical Sciences and Biological Sciences, with additional support from the Directorate for Computer and Information Science and Engineering and the Directorate for Engineering.
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