Person:

Myhrvold, Cameron

Microbial population growth is typically measured when cells can be directly observed, or when death is rare. However, neither of these conditions hold for the mammalian gut microbiota, and, therefore, standard approaches cannot accurately measure the growth dynamics of this community. Here we introduce a new method (distributed cell division counting, DCDC) that uses the accurate segregation at cell division of genetically encoded fluorescent particles to measure microbial growth rates. Using DCDC, we can measure the growth rate of Escherichia coli for >10 consecutive generations. We demonstrate experimentally and theoretically that DCDC is robust to error across a wide range of temperatures and conditions, including in the mammalian gut. Furthermore, our experimental observations inform a mathematical model of the population dynamics of the gut microbiota. DCDC can enable the study of microbial growth during infection, gut dysbiosis, antibiotic therapy or other situations relevant to human health.

Organized complexity is a hallmark of biology in general, and eukaryotes in particular. This phenomenon abounds across many size scales ranging from tissues to organelles to protein complexes. Scaffold molecules, which facilitate the assembly of protein complexes, serve as guides for organization. These scaffolds encompass a wide variety of materials, including DNA, RNA, and proteins, and are used for engineering metabolic reactions and signaling pathways. However, the structures which have been produced to date are fairly simple in geometry, and in many cases are not compatible with in vivo assembly. Here, I aim to recapitulate biological organization synthetically using nucleic acids and lipids as scaffolds for proteins used as markers and as reaction catalysts, both in vitro and in vivo. In Chapter 1, I discuss a new method for assaying the assembly of DNA nanostructures using next-generation DNA sequencing. In Chapter 2, I explore a new set of DNA nanostructures capable of self-assembly across a wide range of temperatures and conditions. In Chapter 3, I develop a method called distributed cell division counting (DCDC) for counting bacterial cell divisions that utilizes the segregation of self-assembling fluorescent particles, and apply DCDC to measure the growth rate of a native gut microbe in the mammalian gut. In Chapter 4, I discuss a new set of lipid-based scaffolds that can co-localize enzymes in vivo and apply this technique to enhance indigo biosynthesis. Together, these results indicate that self-assembly can be designed to occur under a wide range of conditions, and demonstrate several practical applications of self-assembled nanostructures.

Collections of DNA sequences can be rationally designed to self-assemble into predictable three-dimensional structures. The geometric and functional diversity of DNA nanostructures created to date has been enhanced by improvements in DNA synthesis and computational design. However, existing methods for structure characterization typically image the final product or laboriously determine the presence of individual, labelled strands using gel electrophoresis. Here we introduce a new method of structure characterization that uses barcode extension and next-generation DNA sequencing to quantitatively measure the incorporation of every strand into a DNA nanostructure. By quantifying the relative abundances of distinct DNA species in product and monomer bands, we can study the influence of geometry and sequence on assembly. We have tested our method using 2D and 3D DNA brick and DNA origami structures. Our method is general and should be extensible to a wide variety of DNA nanostructures.

Nucleic acids (DNA and RNA) are widely used to construct nanoscale structures with ever increasing complexity1–14 for possible applications in fields as diverse as structural biology, biophysics, synthetic biology and photonics. The nanostructures are formed through one-pot self-assembly, with early examples typically containing on the order of 10 unique DNA strands. The introduction of DNA origami4, which uses many staple strands to fold one long scaffold strand into a desired structure, gave access to kilo- to mega-dalton nanostructures containing about 102 unique DNA strands6,7,10,13 . Aiming for even larger DNA origami structures is in principle possible15,16, but faces the challenge of having to manufacture and route an increasingly long scaffold strand. An alternative and in principle more readily scalable approach uses DNA brick assembly8,9, which doesn’t need a scaffold and instead uses hundreds of short DNA brick strands that self-assemble according to specific inter-brick interactions. First-generation bricks used to create 3D structures are 32-nt long with four 8-nt binding domains that directed 102 distinct bricks into well-formed assemblies, but attempts to create larger structures encountered practical challenges and had limited success.9 Here we show that a new generation of DNA bricks with longer binding domains makes it possible to self-assemble 0.1 – 1 giga-dalton three-dimensional nanostructures from 104 unique components, including a 0.5 giga-dalton cuboid containing 30,000 unique bricks and a 1 giga-dalton rotationally symmetric tetramer. We also assemble a cuboid containing 10,000 bricks and 20,000 uniquely addressable ‘nano-voxels’ that serves as a molecular canvas for three-dimensional sculpting, with introduction of sophisticated user-prescribed 3D cavities yielding structures such as letters, a complex helicoid and a teddy bear. We anticipate that, with further optimization, even larger assemblies might be accessible and prove useful as scaffolds or for positioning functional components.

Self-folding of an information-carrying polymer into a defined structure is foundational to biology and offers attractive potential as a synthetic strategy. Although multicomponent self-assembly has produced complex synthetic nanostructures, unimolecular folding has seen limited progress. We describe a framework to design and synthesize a single DNA or RNA strand to self-fold into a complex yet unknotted structure that approximates an arbitrary user-prescribed shape. We experimentally construct diverse multikilobase single-stranded structures, including a ~10,000-nucleotide (nt) DNA structure and a ~6000-nt RNA structure. We demonstrate facile replication of the strand in vitro and in living cells. The work here thus establishes unimolecular folding as a general strategy for constructing complex and replicable nucleic acid nanostructures, and expands the design space and material scalability for bottom-up nanotechnology.

Molecular biology provides an inspiring proof-of-principle that chemical systems can store and process information to direct molecular activities such as the fabrication of complex structures from molecular components. A first step towards mimicking this capability is understanding how molecular interactions encode and execute algorithms, with self-assembly of relatively simple units into complex products1 particularly well-suited for such investigations. Theory has indeed shown that full-fledged algorithmic behavior can be embedded within molecular self-assembly processes2,3, and this has been experimentally demonstrated by using DNA nanotechnology4 and up to 22 tile types5,6,7,8,9,10,11. But many information technologies exhibit a complexity threshold – such as the minimum transistor count needed for a general-purpose computer – beyond which the power of a reprogrammable system increases qualitatively, and it remains unclear whether the biophysics of DNA self-assembly allows that threshold to be exceeded. Here we report the design and experimental validation of a DNA tile set that contains 355 single-stranded tiles and can, through simple tile selection, be reprogrammed to implement a wide variety of 6-bit algorithms. We use this set to implement 21 circuits that include copying, sorting, recognizing palindromes and multiples of 3, random walking, obtaining an unbiased choice from a biased random source, electing a leader, simulating cellular automata, generating deterministic and randomised patterns, and serving as a period 63 counter, and find an average per-tile error rate less than 1 in 3000. These findings suggest that molecular self-assembly may serve as a reliable algorithmic component within future programmable chemical systems that could serve as creative spaces where high-level molecular programmers can flourish.