Mark Bathe, Ph.D. - US grants

This Designing Materials to Revolutionize and Engineer our Future (DMREF) grant provides funding for the development of a computational tool to determine optimal design parameters for the synthesis of DNA-based materials. The developed tool will determine the optimal DNA sequences and environmental assembly conditions, including solvent and temperature, to realize pre-specified design criteria for two-dimensional and three-dimensional DNA-based nanoscale structures and materials. Physics-based computational models will be used to incorporate mechanical, electrostatic, and hybridization free energies into an overall structural-thermodynamic model of the target DNA-based assembly. This model will be used together with numerical optimization and highly parallel computation to optimize the design and synthesis process. Detailed experimentation will be used to test and validate the computational tool, including two-dimensional and three-dimensional characterization of target structural properties and assembly kinetics.

The results of this research will lead to a broadly accessible, automated design tool for the production of custom DNA-based nanostructures and materials. Optimal sequence design and assembly conditions for target DNA-based material properties will be generated using this tool, enabling its broad use in diverse nanotechnology applications. Target properties may include complex two- and three-dimensional nanoscale structural features, mechanical response, and operating temperature and solvent conditions. Determining computationally the sequence design parameters to achieve these target properties will reduce the financial cost and time required to manufacture DNA-based materials, as well as optimize the quality and uniformity of the final product. This computational tool will also enable the simulation and design of diverse functional aspects of custom DNA-based nanomaterials using complementary computational modeling approaches.

In this project the PI will investigate the role of contractile actin networks in driving shape changes and morphogenesis in Drosophila embryos. Contractile actin networks are increasingly being shown to play a central role in fundamental biological processes involving cellular transport including cell division and embryogenesis. A physics-based approach that integrates quantitative analysis of fluorescence microscopy data with mechanistic, physics based modeling is proposed to investigate these processes. A major challenge posed by this data-driven modeling approach is the unbiased evaluation of competing physical models. To meet this challenge, a Bayesian inference framework is explored that models noise in the data generation process to systematically test competing hypotheses of transport mechanisms. Specific contributions of this research project include a unified, physical understanding of how dynamic, contractile actin meshworks operate to transport organelles and entire cells during basic biological processes including cell division and embryogenesis. Further, a data-driven approach to bridging physics-based transport models with fluorescence microscopy data sets is explored that will be of broad utility to the biological physics community.

Contractile actin networks are protein networks present in eukaryotic cells that play an important role in cell division, cell movement, and tissue development. However, a mechanistic, physical understanding of how short time-scale, dynamic contractions of actin at the subcellular scale interact mechanically to coordinate cellular transport at larger length and time-scales does not exist. Resolving this mechanism requires a quantitative, physics-based approach that models contraction-driven transport at multiple scales ranging from single cells to collections thereof. The present project is at the forefront of this research area, which is of great importance to our scientific understanding the roles of contractile actin networks in development.

Educational initiatives being advanced by the PI include undergraduate and graduate curriculum enhancement, including the new graduate elective offered in the Department of Biological Engineering, Physical Biology, and a new Institute-wide seminar series in Biophysics at MIT. As a result of the present research project, the PI is establishing a webserver for objective, Bayesian analysis of transport measurements to infer physical transport models in a variety of scientific disciplines, updating a webserver for integration of physics-based modeling into molecular animations of proteins, protein assemblies, and cytoskeletal dynamics for educational purposes, and developing a virtual laboratory in molecular and cellular biophysics for students at City on a Hill, a local charter high school serving under-represented minorities in the Boston area. Educational and research activities of the PI will be further disseminated via continuous rotation of undergraduate students from the Biological Engineering Research Experience for Undergraduates program and MIT's Undergraduate Research Opportunities Program, as well as from developing foreign countries.

? DESCRIPTION (provided by applicant): Significant work is ongoing to reveal how different cell types in the brain contribute to behavior and pathology, and how they change in plasticity and disease, empowered by new genetic, optical, and physiological tools. However, the functional activity and dysregulation of neuronal circuits relies critically on the in situ molecular composition of neuronal synapses. Although it is clear that the properties of a given synapse are determined by, amongst other things, the specific types of cells that are thus connected, far less is known about the diversity of synapse types in the brain than cell types, perhaps because this is an intrinsically proteomic problem: a given neuron might make many different kinds of synapse with different targets, and thus transcriptomics (which is prevailing as a method for cell type analysis) may not suffice for synapse typing. High- throughput in situ proteomic tools are needed to characterize synapse molecular composition at the single-cell level in the context of whole brains or brain regions, and thus to connect the currently distant topics of neuronal activity and genetic aberrations associated with disease pathology. Here, we propose a high-risk, high-payoff, and as far as we know entirely novel agenda: to develop tools capable of resolving the molecular proteomic composition of synapse types, testing them in cultured neurons and intact brain tissues. To achieve this transformative goal of establishing a broadly useful tool for in situ synapse proteomics, we will build on our recent breakthrough in developing the DNA-based highly multiplexed, quantitative super-resolution imaging method DNA- PAINT (Points Accumulation for Imaging in Nanoscale Topography). DNA-PAINT exploits the transient binding of short fluorescently labeled DNA-probes for simple and easy-to-implement quantitative, highly multiplexed, super-resolution imaging with sub-10 nm resolution. In this application, we plan to develop and apply DNA-PAINT to enable quantitative, ultra-multiplexed, in situ characterization of neuronal synapse proteins for understanding synaptic types and studying cell type-specific synaptic functions. The outcome of our work will be a broadly useful in situ proteomic tool for quantification of neuronal synapse composition that can be used by diverse neurobiology laboratories to study single cell-level synapse properties in fixed tissues from whole brains or cell culture assays.

3D printing has revolutionized the ability to fabricate complex solid objects at the macroscopic scale using simple Computer-Aided Design (CAD) files as input. In this process, the user specifies the solid object using simple geometric primitives or surface-based meshes. Recent applications of this revolutionary technology include printing limb prosthetics and implants and tissue engineering scaffolds, as well as rapid prototyping of products in industries ranging from apparel and eyeware to automotive, aerospace, and art. A similar transformation in automated fabrication began in the 1970s using CAD for the design of complex electronics using very large scale integration (VLSI) to design circuits consisting of thousands of transistors. This CAD revolution also dramatically increased and broadened the participation of designers without detailed technical know-how needed to design and synthesize custom electrical circuits for diverse applications in industries ranging from mobile devices to biomedical implants. At the nanometer-scale, programmed self-assembly of synthetic DNA offers a similar ability to "print" complex 3D nanometer-scale objects with precisely defined 3D structural features. While the field of structural DNA nanotechnology is considerably younger than the preceding examples, recent technological and scientific advances have enabled the low-cost and reproducible synthesis of diverse structured DNA nano-objects, enabling numerous technological innovations including casting metallic nanoparticles for photonics and light-harvesting devices, fabricating therapeutic vectors that mimic viruses for drug and gene delivery, and developing nanoscale sensors for biomarker detection in disease diagnosis.

Structural DNA nanotechnology currently faces a similar bottleneck in the broad participation of designers due to the need for automated CAD-based design software for these nano-objects. Here, development of a next-generation CAD framework is proposed to enable the fully automated design of structured DNA assemblies at the nanometer scale. As a starting point, the development of a CAD program is proposed here for the synthesis of a unique class of DNA-based objects called DNA nanocages. DNA nanocages can be programmed to adopt nearly arbitrary symmetries and sizes on this scale. Further, these DNA-based particles may be functionalized chemically with proteins, RNAs, chromophores, and other small molecules for diverse applications in biomolecular science and technology. In addition, these nanoscale materials can be transformed into structured inorganic materials including metals and silicon dioxide. To realize the aim of transforming the ability to design and fabricate DNA-based nanomaterials, an open-source software package will be developed to prescribe geometrically from the top-down nanocage size and symmetry using a simple high-level language and CAD environment that is distributed worldwide through the world-wide web. Synthetic DNA sequences that self-assemble to form these CAD-specified structures will be automatically generated for nanocage fabrication. Validation of nanocage synthesis will be performed experimentally using high-resolution structural and folding assays. This work forms the starting point for a new high-level programming language to print 3D objects at the nanometer-scale using synthetic DNA that will broadly enable the use and application of these assemblies across diverse research and industrial applications. Future work may extend this framework to arbitrary 2D and 3D DNA-based assemblies, as well as molecularly functionalized DNA-assemblies that mimic, as well as extend far beyond, nature's evolutionary designs.

The past decade has witnessed dramatic growth in ability to "print" complex nanometer-scale structures and patterns using self-assembling nucleic acids. These structures can be used as templates to synthesize inorganic materials on the 1-100 nanometer-scale, or employed directly in applications such as DNA-based memory storage, therapeutic delivery, single-molecule structure-determination, and nanoscale excitonic materials. While various computational strategies are available to forward design these complex 3D structures manually from underlying DNA or RNA sequence and topology, the inverse problem of autonomously generating linear nucleic acid sequences from target geometry alone remains an unsolved computational challenge. In this project, fully automatic, top-down computer-aided design (CAD) algorithms are explored to generate topological sequence designs for broad classes of programmed DNA and RNA assemblies in an autonomous manner using target geometry alone. These assemblies can be "printed" via self-assembly in vitro or in vivo to form target nanoscale geometries using either synthetic or transcribed nucleic acids. The approach will offer a broadly accessible, high-level programming language to realize sequence-based programming of arbitrary 1D/2D/3D nanoscale structured materials based on nucleic acids with diverse applications in basic science and nanotechnology.


The proposed computational algorithms will be distributed freely online as open source software as well as integrated into a variety of software packages to broadly enable the top-down design of DNA and RNA assemblies. These algorithms and software will provide the broader scientific and industrial communities with easy-to-use, high-level design strategies that will accelerate the broad participation of groups in the use of nucleic acid nanotechnology for diverse applications in biomolecular and materials science and technology. The tools will open up opportunities for high school students and undergraduates to gain hands-on experience in nucleic acid nanostructure design. Curriculum developments at ASU and MIT will employ the use of this sequence design software for participation by undergraduate and graduate students in its use and application to basic questions in computer science and nanotechnology research.


Foundational aspects of the design of nanoscale structured materials using DNA and RNA will be explored. Algorithmic approaches to rendering diverse CAD-based geometric primitives using DNA and RNA will be investigated, including wireframe lattices in 2D and 3D, single-layer surfaces that may contain arbitrary curvatures, as well as 3D solid objects. Meshing algorithms will be used to discretize geometric objects in 1D, 2D, and 3D, and topological routing and sequence design will be applied to position nucleic acid strands within CAD objects. Continuous and discontinuous single stranded nucleic acids will be routed through duplexes using anti-parallel and parallel crossover configurations to exploit distinct modes of programmed self-assembly. Sequence design and routing will be validated experimentally to explore principles for obtaining optimal folding, self-assembly, and positioning of specific base pairs in 3D space. Self-assembly of nanostructures from RNA will additionally be explored, utilizing staple-free designs from single long continuous scaffold strands. Close interaction between experiment and computation will help to distill fundamental yet practical approaches to programming structured nucleic acid assemblies.

Active transport mechanisms of messenger RNAs (mRNAs) are core to neuronal network development and function. Fluorescence imaging is a powerful approach to resolving the physical basis of mRNA transport using direct reporters of mRNA location and copy number in live cells. However, resolving these mechanisms requires quantitative, physics-based approaches that model ribosome-mRNA associations, copy numbers, and recruitment to synaptic and cytoskeletal sites where local translation is needed. The present project integrates live-cell imaging with physics-based modeling and inference of stochastic molecular transport and copy number variations to characterize the molecular basis of neuronal synapse development that is core to brain development and function in living systems. Educational initiatives advanced by the PI include undergraduate and graduate curriculum enhancements including a discussion based seminar course on the physics of living systems that is taught jointly between the Departments of Biological Engineering, Physics, and Biology at MIT. The PI is additionally active in developing and maintaining free web servers that distribute worldwide physics-based inference procedures developed by his group. The PI participates in outreach to under-represented minorities through teaching in an annual workshop organized by the Department of Biology over the inter-activity period in January. Educational and research activities of the PI are translated to undergraduate students through MIT's Undergraduate Research Opportunities Program, as well as through host visitations of international students from foreign countries.

The objective of this project is to understand the translational dynamics of messenger RNAs that are regulated by active transport mechanisms in neuronal cells. Neurons consist of highly elongated axonal and dendritic processes that extend hundreds to thousands of cell bodies away from the nucleus, where transcription occurs. Consequently, neuronal plasticity in development and learning requires synaptic and cytoskeletal proteins to be synthesized locally within these extended processes that cannot be reached by passive mRNA transport mechanisms alone. To achieve this function, mRNAs are actively trafficked by molecular motors to synaptic sites to enable local protein production. In this project the PI will apply single-molecule live-cell imaging together with physics-based modeling of mRNA and ribosomal transport to understand the molecular basis of neuronal mRNA transport. The PI will develop stochastic modeling and inference procedures to infer the association dynamics of mRNAs and ribosomes, as well as their physical association with filamentous actin networks, microtubules, and synaptic proteins to identify cellular landmarks that regulate mRNA translation. Fluorescence fluctuation analysis is performed to infer copy numbers of mRNAs and ribosomes in ribonucleoprotein complexes, which are cross-validated using multiplexed super-resolution fluorescence imaging in fixed neuronal samples with exchangeable DNA probes. This work will help resolve the physical basis of mRNA recruitment and trafficking in the formation and turnover of synapses that are central to neuronal development and plasticity.

This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Cellular Dynamics and Function Cluster in the Division of Molecular and Cellular Biosciences.

PROJECT SUMMARY The development, functional activity, and plasticity of neuronal circuits rely critically on the spatially controlled expression and regulation of synaptic proteins and their messenger RNAs (mRNAs). Genome-wide association studies have revealed extensive polygenic variation in synaptic proteins in association with diseases including autism, schizophrenia, and Alzheimer's. A molecular understanding of how these complex genetic variations impact neuronal synapse development, plasticity, and homeostasis is crucial for the development of new therapies to treat these diseases. Fluorescence imaging offers the potential to characterize neuronal synapse protein and mRNA levels and localizations in situ; however, current imaging approaches can simultaneously interrogate no more than four of the several dozen molecules of interest in any given neuronal sample. To overcome this obstacle, we propose to develop a transformative fluorescence imaging assay that enables simultaneous, highly multiplexed, high-throughput molecular characterization of protein and mRNA expression levels and localizations in intact neurons. To this end, we will develop an innovative labeling strategy that exploits transiently binding fluorescent nucleic acids to enable multiple rounds of imaging of intact specimens, using both standard and super-resolution microscopy. In conjunction, we will develop ultra-bright fluorescent probes based on hybridization chain reaction and structured nucleic acids for visualization of single mRNA molecules. We will apply both standard confocal and super-resolution imaging to characterize spatial distributions and molecular interactions of synaptic proteins and regulatory mRNA-binding proteins, including Fragile-X Mental Retardation Protein (FMRP) in both mouse and human induced pluripotent stem cell models. Using this approach, we will characterize the impact of gene deletions associated with autism on the levels and localizations of more than 10 synaptic and cytoskeletal proteins, as well as examine the interactions of FMRP with dozens of mRNAs in intact dendritic arbors, spines, and synapses. We intend our technique to become broadly useful as a platform technology for the study of the molecular impacts of genetic variations in psychiatric diseases, including autism and schizophrenia. Consequently, we will develop our imaging platform in close collaboration with the Stanley Center at the Broad Institute of MIT and Harvard. The high-throughput nature of our imaging approach ensures that it will be useful for development of novel methods of treating psychiatric diseases using small-molecule and gene-editing approaches.

Biological organisms utilize DNA to encode the synthesis of a remarkably diverse variety of materials ranging from photosynthetic plants to pearly substances from mollusks to silk from spiders and worms. In recent years, it has become possible to utilize DNA itself as a construction material and to turn it into a wide range of materials not readily produced from naturally evolved organisms. To this end, this research project focuses on identifying computational design rules that will accelerate the discovery of DNA-based, structured materials possessing unusual chemical, mechanical, and optical properties. The synthetic DNA materials are being designed with open pore configurations suitable for further modification with proteins, enzymes, chromophores, metallic particles, and other organic and inorganic materials. These combinations offer a next-generation platform for the creation of complex multi-scale DNA-based materials for diverse chemical, biological, mechanical, optical, and sensing applications. The predictive software tools developed in the course of the project are being made broadly accessible and open-sourced to facilitate engagement worldwide of researchers and practitioners in the custom design and synthesis of complex DNA-based materials.

This research project aims to develop a generalized computational framework for the rational design of structured DNA-based materials of nearly arbitrary nanoscale geometric composition. Computational models of DNA-based self-assembly and structure are being validated experimentally in a highly iterative and integrative manner. Similar to unstructured and non-porous colloidal particles that have previously been organized rationally using DNA, structured DNA nanoparticles and single-stranded DNA tiles are being used to realize complex structured and porous 3-dimenional materials in infinite, extended lattices or finite, discrete nanoscale clusters. Utilization of purely synthetic, single-stranded tile oligos facilitates the realization of up to gigadalton-scale DNA-based materials with unique nanoscale addressability using sequence specification amenable to downstream functionalization with metallic nanoparticles, enzymes, or other functional moieties. Web-based dissemination of computational models is being done to make the results of this research project available worldwide. Software developed during the course of the project also is being made freely available, under an open source license, for further development and integration into other software packages by researchers worldwide. This broad dissemination of computational results facilitates the broader materials and nanotechnology communities to study this novel class of hierarchical DNA-based materials. For example, computational models could be used to perform in silico screens for target structural and functional properties, reducing significantly the time needed to deploy functional DNA-based materials for industrial applications.

Moore's Law, the well-known observation that the number of transistors in silicon-based integrated circuits and personal computing devices doubles approximately every year, has ended. A major challenge for the next decade is to identify viable alternatives to traditional, silicon-based computing. Such alternatives are needed to meet the ever-increasing worldwide computational demands in almost all areas of life. Quantum information processors may be one viable alternative to meet this demand. By taking advantage of quantum mechanical phenomena, the computational power of quantum computers may exceed by orders of magnitude that of conventional, silicon-based integrated circuits. However, to date quantum information processing is required to operate at ultra-cold temperatures in highly isolated and protected environments. No viable quantum information processing platform exists that functions at room temperature or in wet condition. If they did exist, such quantum systems could be used for tasks such as quantum-enhanced sensing. In this project, structured DNA circuits are used to construct quantum-based processing and sensing devices that operate at room temperature and in the liquid state. Foundational principles of DNA-based quantum sensing and signal processing devices are being established and applied to practical quantum information processing challenges to demonstrate viability of this revolutionary approach to address diverse societal computing needs. An interdisciplinary team of investigators from chemistry, biological engineering and materials science, and electrical engineering will pursue this transformative approach towards room-temperature information processors, and train the next-generation of interdisciplinary quantum computer scientists and engineers



Classical silicon-based computing has reached its limit for solving increasingly complex, resource-intensive computational challenges in diverse application areas. In contrast, quantum-based systems enable a myriad of possibilities for transformative technological advances in simulation, sensing, computation, and metrology by harnessing quantum mechanical phenomena. Through the unique physics of quantum mechanics, quantum systems process or exchange information that can surpass classical computing architectures and measure environmental variables with unprecedented sensitivity. However, deployment of quantum systems to diverse application arenas has been encumbered by the fragility of quantum states to the ever-present noisy thermal bath, thereby limiting the operation of current quantum devices to cryogenic temperatures. For this reason, fundamental investigations into new quantum information processing and sensing architectures are needed, particularly for the deployment of devices for room-temperature and biomedical applications. Here, novel quantum circuits are developed based on controlled excitonic states of biomolecular materials. The structural control and single-molecule addressability of highly programmable DNA nanostructures are leveraged together with synthetic dyes as an engineering platform for quantum-coherent excitonic systems. Controlling the positions and energies of distinct excitonic states using DNA-based scaffolds enables the integration of qubits into higher-order circuits to create multi-qubit systems and devices. Closely coupled theory and experiment are used to design and evaluate emergent qubit function. Novel, high-impact room-temperature quantum sensing and information processing devices are fabricated and characterized, with potential for integration into prototypical next-generation quantum technologies.

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.

PROJECT SUMMARY Targeted delivery of therapeutic gene editing CRISPR ribonucleoprotein (RNP) complexes to cells offers major opportunities for next-generation therapies, however, realization of this goal requires a vehicle to protect and direct CRISPR RNPs to their targeted cells and tissues. Currently, there are no vehicles that can incorporate CRISP RNPs for cellular delivery in vivo. Here, we propose to use structured DNA nanoparticles (DNA-NPs) as versatile carriers for the targeted delivery of RNPs, taking advantage of base-pairing between the RNP and the DNA-NP to facilitate RNP incorporation and controlled release. DNA-NPs will be designed with stoichiometric control over the number of RNP payloads they carry internally, and functionalized externally with antibodies and lipid binding moieties to control cellular targeting and intracellular trafficking. We will minimize macrophage engulfment and enhance blood compatibility through surface functionalization with polyethylene glycol (PEG) and other biocompatible passivation techniques, using standard assays for nanoparticle physiological and immunological interactions. Binding and release of CRISPR RNPs from DNA-NPs will be assayed in vitro, and DNA-RNP base pairing will ensure stable binding and facile release of cargo from vehicle. Genome editing will be assayed through both next generation sequencing and targeted PCR, which will esure that the enzymatic activity of the cargo remains intact during delivery. Targeting CRISPR RNPs to sub-populations of cultured cells expressing cell specific surface receptors will be evaluated, with particular attention paid targeted gene editing in cells of interest. The generality of our RNP delivery platform offers a unique and transformative approach to treating a range of deadly and currently untreatable genetic diseases and cancers by targeted delivery of CRISPR.

The recent explosion in worldwide data together with the end of Moore's Law and the near-term limits of silicon-based data storage being reached are driving an urgent need for alternative forms of computing and data storage/retrieval platforms. In particular, exabyte-scale datasets are increasingly being generated by the biological sciences and engineering disciplines including genomics, transcriptomics, proteomics, metabolomics, and high-resolution imaging, as well as disparate other scientific fields including climate science, ecology, astronomy, oceanography, sociology, and meteorology, amongst others. In this data revolution, the continuously increasing size of these datasets requires a concomitant increase in available computational power to store, process, and harness them, which is driving a need for revolutionary new, alternative substrates for, and forms of, computing and data storage. Unlike traditional data storage and computing materials such as silicon, the human brain offers a remarkable ability to sense, store, retrieve, and compute information in a manner that is unrivaled by any human-made material. In this research project, analogous modes of information sensing, data storage, retrieval, and computation will be explored in non-traditional computing molecular systems and materials. The over-arching goal of the research is to discover revolutionary new modes of data storage/retrieval, sensing, and computation that rival conventional silicon-based technology, for deployment to benefit society broadly across all domains of data science. Graduate students and postdocs across five institutions will be trained and mentored in a highly interdisciplinary manner to attain this goal and prepare the next-generation of data scientists, chemists, physicists, and engineers to harness the ongoing data revolution. The research will be disseminated to a broad community through news outlets and integration of high school student internships in participating research laboratories.

Large-scale datasets from spatial-temporal calcium imaging of the mouse brain will be recorded into DNA-based, nanoparticle-based, and phononic 2D and 3D soft and hard materials. Continuous spatial-temporal data will first be transformed into discrete data for mapping onto DNA-conjugated fluorophore networks, dynamic barcoded nanoparticle networks, and phononic 2D and 3D materials. Sensing, computation, and data storage/retrieval will be demonstrated as proofs-of-principle in exploiting the chemical properties of molecular networks and materials to recover the encoded neuronal datasets and their sensing and computing processes. Success with any of these three prototypical materials would revolutionize the ability to encode arbitrarily complex, large-scale datasets into complex molecular systems, with the potential to scale across diverse data domains and materials frameworks. The investigators' Autonomous Computing Materials framework will thereby enable the encoding of arbitrary "big data" sets into diverse materials for data storage, sensing, and computing. This project maximizes opportunities for disruptive new computing and data science concepts to emerge from a multi-disciplinary, collaborative team spanning data science, neuroscience, materials science, chemistry, physics, and biological engineering.

This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Chemistry within the NSF Directorate of Mathematical and Physical Sciences.

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.

PROJECT SUMMARY/ABSTRACT COVID-19 has emerged from SARS-CoV-2 within the course of several months to spread worldwide as a deadly pandemic, with the number of deaths approaching one-half million worldwide. While over one hundred vaccines are currently in development, and several already in human clinical trials, most of these early candidates consist of messenger RNA or DNA formulations used to transiently express SARS-CoV-2 subunit proteins, which may not elicit sufficiently neutralizing, long-term antibody response. Strategies to enhance antigenicity, antibody affinity maturation, and memory induction in response to subunit vaccines are of broad relevance for the design of effective vaccines against infectious diseases such as COVID-19, and may be particularly important to neutralize the SARS-CoV-2 pathogen. One approach to enhance the efficacy of subunit vaccines is to formulate antigens in a multivalent, nanoparticulate form, which promotes several aspects of humoral immunity, most notably crosslinking of B cell receptors (BCRs). This approach has been exploited both in licensed vaccines (e.g., the HPV and HBV vaccines), and in a great variety of vaccines in preclinical and clinical development. In this project, we use the unique technology of scaffolded DNA origami to engineer virus-like nanoparticles on the 10?100 nanometer scale that offer the ability to conjugate controlled copy numbers of SARS-CoV-2 antigens at controlled inter-antigen spacings. We test the relative importance of copy number, spacing, and virus-like nanoparticle size on B cell activation in vitro. Optimal constructs identified using B cell activation assays in vitro will subsequently be used to characterize T-cell and B-cell response in vivo using mouse models. Successful vaccine constructs identified from in vivo studies will be shared with commercial partners to facilitate follow-on toxicity, safety, and efficacy studies in higher animal models including non-human primates. Our results will offer a novel subunit vaccine formulation that may be generalized to other SARS-CoV variants including SARS-CoV- 1 through heterovalent protein antigen presentation, as a generalized vaccine platform to avoid future coronavirus-induced pandemics.

PROJECT SUMMARY Strategies to enhance antigenicity, antibody affinity maturation, and memory induction in response to subunit vaccines are of broad relevance for the design of effective vaccines against infectious diseases, and may be especially important for difficult-to-neutralize pathogens such as HIV. One approach to enhance the efficacy of subunit vaccines is to formulate antigens in a multivalent, nanoparticulate form, which promotes several aspects of humoral immunity, and most notably enhances crosslinking of B cell receptors (BCRs). This approach has been exploited both in licensed vaccines (e.g., the HPV and HBV vaccines), and in a great variety of vaccines in preclinical and clinical development. However, to date it remains unclear what are the ideal characteristics of nanoparticle antigen display. In this project, we use the unique technology of scaffolded DNA origami to engineer nanoparticles on the 10?100 nanometer scale that offer the ability to investigate the impact of scaffold size, antigen copy number up to more than 100, antigen-BCR affinity, as well as the nanoscale spatial organization and dimensionality of antigen presentation on BCR activation. Specifically, we test the relative importance of these parameters on B-cell activation, which are of central importance to the development of a successful subunit vaccines, using the germline targeting engineered outer domain of HIV-1 gp120, termed eOD-GT8, and its variants with different affinities, as a testbed. In vitro evaluation of early B-cell signaling and pathway activation will be characterized, and contrasted with the benchmark strongly activating 60-mer control organized on a protein scaffold. Single-cell fluorescence imaging is used to investigate the detailed mechanism of BCR-binding and B-cell activation based on the optimal immunogen presentation found. These constructs are then used to test the impact of these optimal HIV DNA-NP constructs on T-cell and B-cell response in vivo using mouse models. Taken together, our results will offer the elucidation of the optimal immunogen presentation parameters for effective immune cell response in the development of more effective subunit vaccines, with major translational potential for HIV and other infectious diseases.