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 appears 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 percent 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 Plant Cell 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.
... continue reading