Genomic Science Program
U.S. Department of Energy | Office of Science | Biological and Environmental Research Program

Automation of a CRISPRi Platform for Enhanced Isoprenol Production in Pseudomonas putida


David N. Carruthers1,2* (, Patrick Kinnunen1,2, Ian Yunus1,2, William Galliard1,3, Sophie Li3,4, Yan Chen1,2, Jennifer Gin1,2, Chris Petzold1,2, Jess Sustarich1,3, Paul D. Adams1,2, Aindrila Mukhopadhyay1,2, Jay Keasling1,2,4, Hector Garcia Martin1,2, Taek Soon Lee1,2


1Joint BioEnergy Institute; 2Lawrence Berkeley National Laboratory; 3Sandia National Laboratories; 4University of California–Berkeley



Establish the scientific knowledge and new technologies to transform the maximum amount of carbon available in bioenergy crops into biofuels and bioproducts.


Automation technologies expedite Design-Build-Test-Learn (DBTL) cycles while reducing time and resources. Here, the project developed an automated conversion pipeline that, coupled with machine learning, omics studies, and microfluidics, elevates the capacity to engineer microbes. Pseudomonas putida is a promising microbial host owing to its genetic tractability and capacity to grow on many carbon substrates (Wang et al. 2022). Recently, P. putida was engineered for isoprenoid production with subsequent work improving isoprenol titer. Isoprenol is a biological precursor to 1,4-dimethylcyclooctane (DMCO), a sustainable aviation fuel (Baral et al. 2021). Building upon that work, researchers applied CRISPR interference (CRISPRi), which uses a deactivated Cas9 enzyme (dCas9) and customizable gRNAs to selectively downregulate orthogonal metabolic pathways. The team developed an automated pipeline to rapidly screen CRISPRi targets and test strains for isoprenol production. The pipeline harnessed microfluidic liquid handlers (ECHO, Mantis, and Biomek) for nanoliter-scale dispensing of molecular cloning reagents then employed a customized high-throughput electroporation device for rapid transformation of 384 strains in parallel. Growth and production studies were completed in BioLector microfermentors with proteomics data collected to verify dCas9 expression and confirm gene of interest downregulation. Finally, researchers trained a machine learning model, the Automated Recommendation Tool (ART), with isoprenol titers and associated downregulated genes to iteratively generate recommendations for further downregulation (Radivojević et al. 2020). When coupled with automation, CRISPRi enabled researchers to screen 130 genes associated with isoprenoid precursors or utilization pathways in parallel to identify genes that improve isoprenol titer. Using ART, genes were then iteratively paired in 2- and 3-gRNA arrays for further investigation. CRISPRi successfully downregulated selected genes to increase metabolic flux towards isoprenol in P. putida and, by iteratively screening different target combinations in gRNA arrays, demonstrably improved titers. Following this successful application, the team plans to use ART to further explore and exploit gRNA combinations to maximize isoprenol titer. The pipeline demonstrates a successful application of machine learning tools to systematically and predictably improve isoprenol titers, rates, and yields.


Graphic showing the Design-Build-Test-Learn cycle.


Baral, N. R., et al. 2021. “Production Cost and Carbon Footprint of Biomass-Derived Dimethylcyclooctane as a High-Performance Jet Fuel Blendstock,” ACS Sustainable Chemistry & Engineering 9(35), 11872–82.

Radivojević, T., et al. 2020. “A Machine Learning Automated Recommendation Tool for Synthetic Biology,” Nature Communications 11(1), 4879.

Wang, X., et al. 2022. “Engineering Isoprenoids Production in Metabolically Versatile Microbial Host Pseudomonas putida,” Biotechnology for Biofuels and Bioproducts 15(1), 137.

Funding Information

This project is supported by the Office of Science, BER program, of the U.S. DOE under Contract No. DE-AC02-05CH11231.