Scientists at the Department of Energy’s Oak Ridge National Laboratory created a platform that pinpoints genetic triggers that turn microbes into efficient factories for new chemicals and materials. The method enables rapid, precise reprogramming of bacteria as biotechnology tools, supporting the domestic production of valuable products and the recovery of critical minerals and materials, enhancing the nation’s supply chains and global competitiveness.
Scientists leveraged synthetic biology expertise, artificial intelligence and statistical mapping techniques to rapidly assess bacterial traits and precisely identify candidate genes that trigger desired characteristics. They then validated findings with gene editing, as described in Nature Communications . The approach builds on previous work by ORNL scientists who adapted a technique called protoplast fusion to create the diverse microbial offspring needed for genetic mapping.
The resulting platform enables the discovery of specific genetic triggers for complex traits, supporting precise design of microbes with targeted capabilities for applications such as the breakdown and conversion of plant lignin into valuable products and the uptake of critical minerals.
“Unlike past approaches that study the effect of gaining or losing whole genes, the new approach lets us determine how small differences in the nucleotide sequence affect bacterial function,” said Josh Michener, co-lead for the project and Biological Systems Design group leader at ORNL. “Variations in strains at the nucleotide level have a huge impact on the resulting phenotype, especially when you’re engineering microbes with specific mutations. The method also lets us study natural mutations in parental strains that make them ideal biotechnology tools.”
Old-school technique makes fresh microbial mixes
To determine which genes control certain characteristics in organisms, scientists used a method called quantitative trait locus (QTL) mapping. QTL mapping involves analyzing the traits of lots of varied offspring from two distinct parents, and is a common approach in mapping the genes of other organisms such as plants. The method examines many genetic differences at once and precisely identifies candidate genes in a single workflow.
The problem with applying QTL mapping to bacteria, however, is that these microorganisms reproduce asexually with limited genetic variation.
ORNL researchers overcame this hurdle by applying a tool first developed in the 1970s: protoplast fusion. Using the fusion technique , researchers were able to cross Bacillus strains, producing a large population of genetically varied offspring , called recombinants. Bacillus are model bacteria that serve as workhorses for fermentation, enzyme production, and for plant growth and health.
The team also tested the method across several other bacterial groups, demonstrating alternative genome shuffling methods that expand the tool’s usability on different types of microbes used as biotechnology tools. These include:
Researchers measured properties of the bacteria and identified DNA variants that could explain the differences in those traits. They then validated their findings by using CRISPR gene-editing tools to swap gene sections and confirm the effects in bacteria.
“We have now, for the first time ever, put all these pieces together for a platform that gets results on complex genes-to-traits linkages much faster,” Michener said. “We built the genetically diverse bacteria population, identified DNA variants, and confirmed the work with gene editing.”
Automation results in 10x faster phenotyping
By creating such broad diversity in the bacterial offspring, scientists faced a challenge in the research: phenotyping all the progeny. They tackled it with automation and AI, setting up a robotic system to quickly and repeatedly place plates with precision so that high-resolution digital imaging could be accomplished at the same angle and lighting for comparable data between the recombinants. The phenotyping was accomplished 10 times faster with automation, the scientists noted.
Getting consistent data was crucial to the application of mathematical algorithms and the use of a computer vision model that processed the images and extracted traits, said co-lead Dan Jacobson, ORNL computational systems biologist.
“We built this project with a very multidisciplinary lineup,” Jacobson said, “The team did everything from building the robotics, conducting imaging and image processing, performing the statistical work, the mapping and assemblies, the genome shuffling work, growing these different isolates from the population and extracting DNA to send for sequencing, then growing them again for the phenotype assays. It’s an example of the kind of good collaboration that’s possible at a national lab, and how that research can enable whole new areas of inquiry across the nation’s science ecosystem.”
The microbial QTL mapping platform is available for licensing at ORNL .
Scientists continue to deploy the method to study and engineer microbes for better manufacturing processes as part of the DOE Center for Bioenergy Innovation (CBI) at ORNL. The platform is also being used to study plant-associated microbes as part of the DOE Secure Ecosystem Engineering and Design Science Focus Area (SEED SFA), as well as by a program at Colorado State University studying airborne microbes.
The project was supported primarily by the ORNL Laboratory-Directed Research and Development program and CBI. Additional funding was provided by the SEED SFA and Michener’s DOE Early Career Research Award (ECA). CBI, the SEED SFA and the ECA are in turn supported by the DOE Office of Science Biological and Environmental Research program. Genome sequencing was conducted by the Joint Genome Institute, a DOE Office of Science user facility.
UT-Battelle manages ORNL for DOE’s Office of Science. The Office of Science is the largest supporter of basic research in the physical sciences in the United States and is committed to addressing some of the most pressing challenges of our time. For more information, visit energy.gov/science . — Stephanie Seay
Nature Communications
Data/statistical analysis
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Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis
5-Aug-2026