Developing microbial factories for sustainable chemicals

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By Sarah Wong

Newswise – The science is clear: fossil fuels are harmful to the environment. Why do we find it so difficult not to use them anymore? Economic reasons are at least part of the answer. From our energy network to the manufacture of certain textiles and other products, many parts of our society are based on fossil fuels. The transition will come with some costs.

But what if we could make an economically attractive substitute for fossil fuels? New research from Pacific Northwest National Laboratory (PNNL) suggests a way to do just that. Biologists invented it a way to develop yeast to produce itaconic acid—A valuable staple chemical – with data integration and supercomputing power as a guide.

Create microbial factories using metabolic models

Itaconic acid has enormous potential as a renewable chemical building block. It could replace some fossil fuel derived products. In 2004 it became one of the “Chemicals with the highest added value from biomass“In a report from the Department of Energy (DOE). Given the potential of itaconic acid as a petrochemical replacement, Data scientist Neeraj Kumar wanted to make it inexpensively with microbes.

Kumar and colleagues had previously developed a method to calculate how manipulated changes in microbes could affect their metabolism. Building on this idea, Kumar wanted to see if he could use these metabolic predictions to develop yeast to produce large amounts of itaconic acid.

“We had to find out which genes in the itaconic acid production path we could modify so that the yeast could produce larger quantities of the chemical,” said Kumar. “The challenge was to find the balance between cellular health and bioproduction.”

Design-build-test-learn

Itaconic acid is naturally produced by a few mushrooms. PNNL scientist Ziyu Dai to give genes borrowed from other mushrooms Yarrowia lipolytica the ability to produce the chemical. Biologist Erin Bredeweg had been working on this modified yeast, which contained several different combinations of genes, when Kumar asked them to collaborate. Bredeweg and her colleagues had created a metabolic and proteomic profile of the modified yeast and passed the data on to Kumar.

Notes on the Design-build-test-learn Strategy, Kumar and his research fellow Andrew McNaughton used machine learning to study this profile to see which non-essential genes could be removed from yeast or which helpful ones could be added to increase itaconic acid production.

After choosing the genes to “shape” the organism, it was time to build it. Bredeweg created different versions of the yeast with added or removed genes based on the computer predictions of Kumar and McNaughton. She then tested the various yeasts to see if the carbon flow towards the itaconic acid production pathways was impaired. Machine learning analysis of the data from RNA sequencing showed that the computer predictions were consistent with the experimental result and further detailed gene predictions for future analysis.

“Although this research is still in its infancy, it is exciting to see its potential,” said Bredeweg. “Machine learning and causal inference can uncover new ways of thinking about how a complex cell system like yeast might respond to individual gene changes, beyond what is possible through metabolic modeling alone.”

Machine learning and multiomics datasets expand the potential of metabolic modeling

Yeasts and other microbes are widely used to make useful chemicals. While it is easy to get them to make some chemicals in high yields, such as ethanol, other chemicals can be more challenging. Kumar hopes this system, which combines machine learning with metabolic modeling and multiomics datasets, will help overcome these manufacturing challenges.

“While we need more testing on this model, there is amazing potential to extend this computational bioengineering to other systems,” said Kumar. “This strategy could usher in a new era in biosystem design for the production of environmentally friendly chemicals.”

James Manzer, Jeremy Zucker, Meagan Burnet, Ernesto S. Nakayasu, and William Chrisler of PNNLs Life Sciences Department, Kyle R. Pomraning from PNNL’s Energy Processes and Materials Department, Eric D. Merkley of PNNL’s Signature Science and Technology Department, Nathalie Munoz and Scott E. Baker of the Laboratory for Molecular Environmental Sciences (EMSL) at PNNL, and Peter St. John of the National Renewable Energy Laboratory also contributed to this work. This research was supported by the Laboratory Directed Research and Development program of the PNNL. Researchers from the National Renewable Energy Laboratory contributed to this study. Computational resources as well as metabolomics and proteomics experiments were supported by EMSL’s intramural program.


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