DNA-Based Platform Detects Traces of Molecules from Broad Range of Sources

Researchers have harnessed synthetic DNA technology to develop a super-efficient nanomachine that detects trace amounts of substances that range from viruses and bacteria to cocaine and metals.

"It's a completely new platform that can be adapted to many kinds of uses," said co-author John Brennan, director of Biointerfaces Insitute, McMaster University (Hamilton, Ontario, Canada), "These DNA nano-architectures are adaptable, so that any target should be detectable." Besides being life’s genetic material, DNA is also a very programmable molecule that lends itself to engineering for synthetic applications. The new method shapes separately programmed pieces of DNA material into pairs of interlocking circles. The first remains inactive until it is released by the second, like a bicycle wheel in a lock. When the second circle, acting as the lock, is exposed to even a trace of the target substance, it opens, freeing the first circle of DNA, which replicates quickly and creates a signal, such as a color change.

"The key is that it's selectively triggered by whatever we want to detect," said Prof. Brennan, "We have essentially taken a piece of DNA and forced it to do something it was never designed to do. We can design the lock to be specific to a certain key. All the parts are made of DNA, and ultimately that key is defined by how we build it."

The idea came from nature itself, explained co-author Yingfu Li, who holds the Canada Research Chair in Nucleic Acids Research, "Biology uses all kinds of nanoscale molecular machines to achieve important functions in cells," said Prof. Li, "For the first time, we have designed a DNA-based nanomachine that is capable of achieving ultra-sensitive detection of a bacterial pathogen." The DNA-based nanomachine is being further developed into a user-friendly detection kit that will enable rapid testing of a variety of substances, and could move to clinical testing within a year.

The study, by Liu M et al, was published June 23, 2016, in the journal Nature Communications.

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