Alternative Watson-Crick Synthetic Genetic Systems

Abstract

In its "grand challenge" format in chemistry, "synthesis" as an activity sets out a goal that is substantially beyond current theoretical and technological capabilities. In pursuit of this goal, scientists are forced across uncharted territory, where they must answer unscripted questions and solve unscripted problems, creating new theories and new technologies in ways that would not be created by hypothesis-directed research. Thus, synthesis drives discovery and paradigm changes in ways that analysis cannot. Described here are the products that have arisen so far through the pursuit of one grand challenge in synthetic biology: Recreate the genetics, catalysis, evolution, and adaptation that we value in life, but using genetic and catalytic biopolymers different from those that have been delivered to us by natural history on Earth. The outcomes in technology include new diagnostic tools that have helped personalize the care of hundreds of thousands of patients worldwide. In science, the effort has generated a fundamentally different view of DNA, RNA, and how they work.

Figure 1.

The “grand challenge” target. Vitamin B₁₂ (left) was synthesized to drive chemical theory, not because it was a multistep tour de force or because the product itself was valuable. Tetrodotoxin (middle) is barely stable, but was nevertheless synthesized (Kishi et al. 1972). Palytoxin (right) is not a biopolymer, although its structure reveals a biosynthetic pathway that involves repetitive assembly of building blocks; chemists could synthesize this also (Kishi 1989).

Figure 2.

How genetics works. The cartoon that “explains everything”—paper cutouts that taught generations of schoolchildren that molecular genetics were simple chemistry.

Figure 3.

Some of the features of “noncartoon” natural DNA that make the Watson–Crick model less self-evident. (A) Unlike the “pennies in a roll” model for nucleobase stacking, the aromatic rings of benzene do not stack in a crystal (Cox et al. 1958). (B) Some synthetic nucleobase pairs that contribute to duplexes similar to the T:A pair, showing the flexibility of the backbone (Geyer et al. 2003; Gao et al. 2004; Heuberger and Switzer 2008; Winnacker and Kool 2013). (C) Adenine is missing a hydrogen-bonding group, creating many problems when using DNA to assemble nanostructures or to construct large DNA assemblies; diaminopurine forms a stronger pair. (D) Deamination of cytosine (shown), adenine, and guanine requires continuous repair. (E) G quartets are an example of a noncanonical structure that can dominate Watson–Crickery.

Figure 4.

Two rules of complementarity guide base pairing in DNA and RNA (collectively xeno nucleic acid [XNA]). The rules are (1) size complementarity (large purines pair with small pyrimidines), and (2) hydrogen-bonding complementarity (hydrogen bond donors, D, pair with hydrogen bond acceptors, A). Rearranging the nucleobase D and A groups gives artificially expanded genetic information systems (AEGIS). Chemical issues in the “first-generation” AEGIS (left pairs) are indicated in magenta. These motivated the synthesis of a second-generation AEGIS (right pairs). Electron density presented to the minor groove (green lobes) is recognized by polymerases (Benner et al. 1998).

Figure 5.

Different ring systems having different properties can implement the same Watson–Crick hydrogen-bonding pattern. (A) Zubay’s ring system formally implements a hydrogen bond donor–acceptor–donor pattern, but without the planar aromatic system that the Watson–Crick model implies (Zubay 1988); to get both, one must make a C-glycoside (Benner et al. 1987). (B) In nature, uridine and pseudouridine implement the same hydrogen-bonding pattern, the first as an N-glycoside, the second as a C-glycoside. (C) Obtaining a heterocycle to implement the puDDA hydrogen-bonding pattern was especially challenging, as various C-glycosides are easy to oxidize or easily epimerized. (D) Various different 5,6-ring systems implement the pu(DDA) hydrogen-bonding pattern, with different amounts of a minor tautomeric form, which implements a different pu(DAD) hydrogen-bonding pattern complementary to thymidine.

Figure 6.

Some second-order Watson–Crick pairing rules obtained via artificially expanded genetic information systems (AEGIS) development (Geyer et al. 2003). A negative charge in the nucleobase stack destabilizing (left). An uncompensated C–NH₂ unit is destabilizing (center). An uncompensated C=O unit is acceptable (right).

Figure 7.

Three crystal structures with Z:P artificially expanded genetic information systems (AEGIS) pairs. An isolated pair in a short, A-form DNA duplex crystallized with the aid of a selenium substitution (left) (Zhang et al. 2015). A 16-mer duplex with six consecutive Z:P pairs (center) (Georgiadis et al. 2015). A single Z:P pair in an RNA riboswitch in four views A, B, C, and D each rotated 90° (right) (Hernandez et al. 2015).

Figure 8.

The branched DNA assay (Bushnell et al. 1999). When artificially expanded genetic information systems (AEGIS) nucleotides (in this case, first-generation S and B) are placed in the amplifier nanostructure, the noise is dramatically decreased (Elbeik et al. 2004a,b).

Figure 9.

Schematic of colony self-replication (CSR) (Ghadessy et al. 2001) for the selection of polymerases that amplify AEGIS pairs, here in a nested polymerase chain reaction (PCR) architecture. A library of genes encoding active (blue) and inactive (red) polymerases is cloned into bacterial cells, which are dispersed (one cell per droplet) into an emulsion where the extracellular buffer contains PCR primers and triphosphates. The initial heat cycle places the polymerases in contact with the PCR primers and triphosphates. The droplets keep the polymerase variant associated with its own encoding gene; if the gene is to be amplified, it must be amplified by its encoded polymerase. After the target-specific primers are consumed, the nested PCR is “carried” by external primers containing AEGIS components. The genes encoding polymerases that are able to copy AEGIS nucleotides (the blue arcs) are enriched in the product pool. The products are then recloned, and the selection is repeated.

Figure 10.

The pattern of amino acid replacement in natural history. Represented here by a phylogenetic tree for an individual site, the pattern can be used to guide protein engineering and directed evolution experiments. (A) Amino acid replacement at sites with low-level of replacements typically leads to inactivation of the polymerase. (B) Homoplasy in the form of parallel evolution indicating purifying selection removes variation that moves beyond a small set of amino acids; sites displaying this variation may be replaced in a controlled way. (C) Substantial variation indicates a site experiencing little purifying selective pressure; replacement at this site is unlikely to have little interesting impact on polymerase behavior. (D) Heterotachy, in which different branches have different rates of amino acid replacement, indicates changing functional constraints at a site; these sites are the foci of the most successfully designed protein engineering libraries (Chen et al. 2010).

Figure 11.

Although synthesized in a “grand challenge” to ask “what if?” and “why not?” questions, artificially expanded genetic information systems (AEGIS) components turned out to have practical value. Here, AEGIS orthogonality creates very clean multiplexed polymerase chain reaction (PCR) (right) in a nested PCR architecture (left). With AEGIS nucleotides in the external primers, PCR is clean, even in complex biological mixtures compared with the same PCR but with external primers having only standard nucleotides (Yang et al. 2010).

Figure 12.

Artificially expanded genetic information systems (AEGIS) conversion supports an assay that allows 22 mosquito-borne viruses to be sought in a single mosquito carcass (for details, see Glushakova et al. 2015).

Figure 13.

Assembly of large DNA constructs using artificially expanded genetic information systems (AEGIS) pairs to assemble the strands without their forming hairpins or other undesired structures. Here, the added information density of a six-letter polymer diminishes assembly ambiguity. After the assembly is complete, AEGIS nucleotides are converted to standard nucleotides, giving an entirely natural construct (Merritt et al. 2014). This allows shorter fragments to be used, which, in turn, recognizes that the longer the synthetic fragment, the greater the chance of error.

Figure 14.

Schematic of the cell-targeted artificially expanded genetic information systems–laboratory in vitro evolution (AEGIS-LIVE) reported in Zhang et al. (2015). A GACTZP DNA library with a randomized region flanked by primer binding sites was incubated with the target cells. Unbound sequences were then washed away. Bound sequences were eluted from the cells, and the supernatant enriched in AEGIS DNA molecules (“survivors”) having affinity for the cells was collected. Counterselections were performed against untransformed liver cells. The survivors were polymerase chain reaction (PCR)-amplified using a fluorescein isothiocyanate (FITC)-labeled primer and a biotinylated primer. Single-stranded DNA was made from the PCR products using the biotin handle and entered into the next round of selection. In each round, survivor pools were monitored for the appearance of “bulk” binding. After this was seen, survivors were sequenced and resynthesized for study.

Figure 15.

DNA aptamers from artificially expanded genetic information systems–laboratory in vitro evolution (AEGIS-LIVE) (Zhang et al. 2015). (A) Sequences (only randomized region), dissociation constants (K_d), and their percentage in the pool of binders after 12 rounds of positive selection and four rounds of negative selection (Z and P in red). Sequences are arranged in order of increasing K_d. (B) Binding and specificity of AEGIS aptamers, arranged from the weakest binding (top) to the tightest (bottom). Binding is measured by incubating cells with fluorescently tagged binding species in cell sorter to give fluorescence intensity per cell (x-axis, log scale; the y-axis indicates the number of cells having the indicated intensity). (Left) Binding to transformed cancer cells, the cells that served as the target in the aptamer selection process. (Right) Binding to untransformed liver cells, which were the counterselection cells. Red distribution at bottom of each panel is the binding displayed by the DNA library before selection.