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New York University scientists are using artificial intelligence to determine which genes collectively govern nitrogen use efficiency in plants such as corn, with the goal of helping farmers improve their crop yields and minimize the cost of nitrogen fertilizers.

"By identifying genes-of-importance to nitrogen utilization, we can select for or even modify certain genes to enhance nitrogen use efficiency in major US crops like corn," said Gloria Coruzzi, the Carroll & Milton Petrie Professor in NYU's Department of Biology and Center for Genomics and Systems Biology and the senior author of the study, which in the journal The Plant Cell.

In the last 50 years, farmers have been able to grow larger crop yields thanks to major improvements in plant breeding and fertilizers, including how efficiently crops uptake and use nitrogen, the key component of fertilizers.

Still, most crops only use roughly 55% of the nitrogen in fertilizer that farmers apply to their fields, while the remainder ends up in the surrounding soil. When nitrogen seeps into groundwater, it can contaminate drinking water and cause harmful algae blooms in lakes, rivers, reservoirs, and warm ocean waters. Furthermore, the unused nitrogen that remains in the soil is converted by bacteria into nitrous oxide, a potent greenhouse gas that is 265 times more effective at trapping heat over a 100-year period than is carbon dioxide.

The United States is the world's leading producer of corn. This major cash crop requires large amounts of nitrogen to grow, but much of the fertilizer fed to corn is not taken up or used. Corn's low nitrogen use efficiency presents a financial challenge for farmers, given the increasing costs of fertilizer—the majority of which is imported—and also risks harming the soil, water, air, and climate.

To address this challenge in corn and other crops, NYU researchers have developed a novel process to improve nitrogen use efficiency that integrates plant genetics with machine learning, a type of artificial intelligence that detects patterns in data—in this case, to associate genes with a trait (nitrogen use efficiency).

Using a model-to-crop approach, NYU researchers tracked the evolutionary history of corn genes that are shared with Arabidopsis, a small flowering weed often used as a model organism in plant biology due to the ease of studying it in the lab using the power of molecular genetic approaches. In a previous study published in Nature Communications, Coruzzi's team identified genes whose responsiveness to nitrogen was conserved between corn and Arabidopsis and validated their role in plants.

In the current study, their most recent on this topic, the NYU researchers built upon their work in corn and Arabidopsis to identify how nitrogen use efficiency is governed by groups of genes—also known as "regulons"—that are activated or repressed by the same transcription factor (a regulatory protein).

"Traits like nitrogen use efficiency or photosynthesis are never controlled by one single gene. The beauty of the machine learning process is it learns sets of genes that are collectively responsible for a trait, and can also identify the transcription factor or factors that control these sets of genes," said Coruzzi.

The researchers first used RNA sequencing to measure how genes in corn and Arabidopsis respond to nitrogen treatment. Using these data, they trained machine-learning models to identify nitrogen-responsive genes conserved across corn and Arabidopsis varieties, as well as the transcription factors that regulate the genes-of-importance to nitrogen use efficiency (NUE).

For each "NUE Regulon"—the transcription factor and corresponding set of regulated NUE genes—the researchers calculated a collective machine learning score and then ranked the top performers based on how well the combined expression levels could accurately predict how efficiently nitrogen is used in field-grown varieties of corn.

For the top-ranked NUE Regulons, the researchers used cell-based studies in both corn and Arabidopsis to validate the machine-learning predictions for the set of genes in the genome that are regulated by each transcription factor. These experiments confirmed NUE regulons for two corn transcription factors (ZmMYB34/R3) that regulate 24 genes controlling nitrogen use, as well as for a closely related transcription factor in Arabidopsis (AtDIV1), which regulates 23 target genes sharing a genetic history with corn that also control nitrogen use.

When fed back into the machine learning models, these model-to-crop conserved NUE regulons significantly enhanced the ability of AI to predict nitrogen use efficiency across field-grown corn varieties.

Identifying NUE Regulons of collective genes and related transcription factors that govern nitrogen use will enable crop scientists to breed or engineer corn that needs less fertilizer.

"By looking at corn hybrids at the seedling stage to see if expression of the identified genes-of-importance to nitrogen use efficiency is high, rather than planting them in the field and measuring their nitrogen use, we can use molecular markers to select the hybrids at the seedling stage that are most efficient in nitrogen use, and then plant those varieties," said Coruzzi.

"This will not only result in a cost savings for farmers, but also reduce the harmful effects of nitrogen pollution of groundwater and nitrous oxide greenhouse gas emissions."

NYU has filed a patent application covering the research and findings described in this paper; the provisional patent also describes the use of CRISPR gene editing technology to engineer NUE regulons in crops for improved nitrogen use efficiency.