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Swett, A.

Publications and source records attributed to Swett, A..

3 recordsLinked to original sources

Computational Design Strategies for Nanoscale 3D Auxetic Metastructures from DNA

Auxetic metamaterials exhibit negative Poissons ratio behaviors due to their architecture of periodically arranged unit cells. Although mechanical metamaterials are well established at the macroscale, programmable auxetic units remain scarce at the nanoscale. DNA origami offers a promising platform to bridge this gap, but design principles for dynamically deformable 3D auxetic nanostructures remain largely unexplored. Here, we develop design strategies for such 3D auxetic metastructures built from wireframe DNA origami. As a model system, we use a 3D re-entrant triangular unit composed of double-stranded DNA (dsDNA) bundle edges connected by single-stranded DNA (ssDNA) joints. Using coarse-grained molecular dynamics (MD) and umbrella-sampling free-energy simulations, we examine how edge design and joint-connection scheme govern auxetic responses and the energetics of the structural transformation. Our results show that auxetic performance and deformation energetics emerge from the coupled effects of DNA bundle rigidity and connector mechanics at the joints. This study provides mechanistic insights and design guidelines for programmable auxetic motion and energetics in 3D DNA origami metamaterials, advancing the development of stimuli-responsive nanomechanical devices.

biophysics↗

A Nanoscale Jitterbug Transformer from DNA

Many viruses have intricate polyhedral shells capable of symmetric transformations in response to external stimuli to initiate payload release. Such deployable auxetic nanostructures are not available in the synthetic realm. Here we present a nanoscale Jitterbug transformer using DNA origami that can reconfigure its structure upon chemical and optical signals while maintaining a Poissons ratio of -1. By leveraging molecular dynamics simulations, we design the Jitterbug DNA to form a compact octahedron by storing elastic energy and spontaneously transition into an expanded cuboctahedron by releasing it. DNA transformers are explored like viruses that can create nanopores on lipid membranes and regulate payload release into vesicles. This work integrates programmable DNA self-assembly with free-energy-guided mechanical design, providing a pathway toward adaptive nanomaterials with potential in synthetic organelles and stimuli-responsive nanodevices.

biophysics↗

Genotype-to-phenotype mapping of somatic clonal mosaicism via single-cell co-capture of DNA mutations and mRNA transcripts

Somatic mosaicism is a hallmark of malignancy that is also pervasively observed in human physiological aging, with clonal expansions of cells harboring mutations in recurrently mutated driver genes. Bulk sequencing of tissue microdissection captures mutation frequencies, but cannot distinguish which mutations co-occur in the same clones to reconstruct clonal architectures, nor phenotypically profile clonal populations to delineate how driver mutations impact cellular behavior. To address these challenges, we developed single-cell Genotype-to-Phenotype sequencing (scG2P) for high-throughput, highly-multiplexed, single-cell joint capture of recurrently mutated genomic regions and mRNA phenotypic markers in cells or nuclei isolated from solid tissues. We applied scG2P to aged esophagus samples from five individuals with high alcohol and tobacco exposure and observed a clonal landscape dominated by a large number of clones with a single driver event, but only rare clones with two driver mutations. NOTCH1 mutants dominate the clonal landscape and are linked to stunted epithelial differentiation, while TP53 mutants and double-driver mutants promote clonal expansion through both differentiation biases and increased cell cycling. Thus, joint single-cell highly multiplexed capture of somatic mutations and mRNA transcripts enables high resolution reconstruction of clonal architecture and associated phenotypes in solid tissue somatic mosaicism.

genomics↗