Researchers Develop New Tools to Turn Grain Crops into Biosensors

A collaborative team of researchers from the Donald Danforth Plant Science Center, the University of Florida, Gainesville and University of Iowa have developed groundbreaking tools that allow grasses—including major grain crops like corn—to act as living biosensors capable of detecting minute amounts of chemicals in the field.

Principal Investigators Dmitri Nusinow, PhD, and Malia Gehan, PhD, led the effort to engineer grasses that produce a visible purple pigment, anthocyanin, in response to specific chemical cues. When paired with advanced imaging and analytical systems, these plants can report extremely low levels of chemical exposure, pollution, or other adverse conditions that may impact crop and human health.

Their findings, Remote Sensing of Endogenous Pigmentation by Inducible Synthetic Circuits in Grasses, were recently published in the Plant Biotechnology Journal.

Turning Plants Into “Sentinels”

“What if plants could alert farmers to adverse conditions or unwanted chemicals?” posited the research team. Although researchers have begun exploring plant-based biosensors, most tools have been developed in dicot species such as Arabidopsis thaliana. Grass species—monocots—have lagged behind despite being the foundation of global grain production. Plant pigments, such as carotenoids, betalains and anthocyanins are being adapted as non-invasive visual reporters to monitor gene expression in plants.

Nusinow and Gehan successfully adapted a ligand-inducible genetic circuit that activates the plant’s own anthocyanin pathway in the C4 model grass Setaria viridis. These new tools could be used to trigger grasses like corn to make a purple pigment, anthocyanin, when exposed to specific chemicals.

Key advances include:

• Identification of two transcription factors that can be co-expressed from a single transcript to trigger anthocyanin production.
• Demonstration of both constitutive and ligand-inducible pigment production in protoplasts and whole plants.
• Development of hyperspectral imaging and discriminative analysis techniques that non-destructively detect pigmentation changes from a near-remote distance.