X Marks the Spot

To understand John Chaput’s cutting-edge research in using synthetic genetics in pharmaceutical drug discovery and development, look for the X factor.

Chaput is a UCI professor of pharmaceutical sciences, and his lab is one of the few places in the world where students can study the evolution of artificial genetic polymers. These molecules are synthetic forms of genetic sequences that contain an alternative sugar in place of the natural deoxyribose sugar found in DNA. “Just as the D in DNA stands for deoxyribose, our polymers are commonly viewed as XNA, where X is a placeholder for your favorite sugar,” he explains.

Chaput’s X factor is the sugar threose, which he and his students use to synthesize TNA building blocks to develop the next generation of diagnostic and therapeutic agents at a much faster rate and lower cost than via traditional methods. “This has been a 20-year effort to get where we are,” he says, “but we now have the tools needed to create new TNA reagents.”

X factor (noun): a variable in a given situation that could have the most significant impact on the outcome

Considered a pioneer in synthetic genetics, Chaput mastered his protein evolution skills as a Howard Hughes Medical Institute Fellow in Harvard Medical School’s Department of Molecular Biology and Genetics, where he worked under Nobel laureate Jack Szostak. After a decade on the faculty at Arizona State University, Chaput joined UCI in 2015. In 2018, he was named an American Association for the Advancement of Science Fellow for his distinguished contributions to the field of chemical biology. He recently received the 2022-23 Distinguished Mid-Career Faculty Award for Research from UCI’s Academic Senate.

What advantage does XNA have over traditional candidates in drug formulation?

When you want to make a drug out of DNA or RNA, you ask two fundamental questions: How do I get it to work, and how do I get it to last inside a cell so that it will work he way it’s supposed to?

As soon as you introduce DNA- and RNA-based molecules into a patient, the body’s natural enzymes try to recycle them into their own building blocks. These enzymes, called nucleases, are kind of like Pac-Man; they just chew up the molecules. It’s a constant battle. XNAs have different properties that are not recognized by enzymes; they’re essentially invisible because nature has never seen them or evolved to react to them. In particular, TNA evokes a very low response, so in that regard it’s a very promising candidate. We can instead just focus on the first question: How can I get it to work?

What different approaches are you taking with XNA in your lab?

One approach is creating aptamers, which are XNA sequences that we evolve to recognize and bind to a particular target. If you can get the aptamer to bind to a protein that’s associated with a disease, you prevent that protein from binding with other receptors and spreading the disease.

We envision aptamers being a potentially better and cheaper solution than monoclonal antibodies, which are amazing drugs but extremely expensive to discover and produce. We’ve partnered with a major Silicon Valley tech firm to combine our aptamer evolution work in the lab with its machine learning capabilities. The hope is it’ll accelerate the drug discovery process because it will reduce iteration time and broaden the number of conditions you could survey for any given target.

We’re also working on a new class of therapeutics called DNA enzymes, or DNAzymes for short. They can silence the expression of disease-associated proteins caused by a single genetic mutation in one’s DNA or messenger RNA. The DNAzymes would cut the genetic sequence associated with the mutation and shut down the disease but preserve and drive the healthy strands at the same time. Our versions insert XNA into the enzyme’s design to provide such traits as increased biological stability or enhanced RNA binding affinity. DNAzymes are a precision medicine approach that could potentially be applied to a range of diseases, including cancer.

Our DNAzyme strategy is also being used to develop diagnostic sensors for disease detection. We created a DNAzyme-based COVID-19 test that identified with 100 percent accuracy the specific genetic mutations associated with the alpha, gamma, delta, epsilon and omicron SARS-CoV-2 variants in a sample of 34 patients. There’s the potential to expand it to other respiratory tract infections that share symptoms with COVID-19, like influenza.

“When you want to make a drug out of DNA or RNA, you ask two fundamental questions: How do I get it to work, and how do I get it to last inside a cell so that it will work the way it’s supposed to?”

XNA also holds tremendous promise in a vastly different area: archival data storage. How does that work?

Humanity is generating data at an exponential rate that outpaces our capacity for storing that information. Researchers have developed algorithms that break down the binary ones and zeroes used in computing code and convert them into the four-letter nucleotide code of DNA: A, T, C and G. You can use this encoding to transcribe anything to a strand of DNA: a movie, a book, a picture. You might be able to store the Library of Congress in DNA with enough advances.

The challenge is: What happens if an enzyme gets in the DNA and degrades it? Suddenly, poof, there goes the Library of Congress. As we know from our research, the XNA we’re working with, TNA, is totally invisible to those enzymes that degrade DNA, and TNA also comprises A, T, C and G. We tested this mechanism by transcribing the Declaration of Independence and the UCI seal to a solution of TNA. The TNA-encoded information was 100 percent intact and 100 percent recoverable. We showed that TNA could be a biologically safe, low-energy medium for storing information. Data storage is not the main focus of what we do, but it has been a fun side project.