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Automation of a CRISPRi platform for enhanced isoprenol production in Pseudomonas putida [DBTL2]

ABSTRACT: Recent advances in genome engineering technologies have significantly enhanced the ability to perturb microbial metabolic networks. Despite this progress, many bioproduction efforts struggle to parse complex metabolic datasets (e.g., -omics) within the constraints of the traditional design-build-test-learn (DBTL) cycle to efficiently optimize titer, rate, and yield. Here, we consider bioproduction of isoprenol, a precursor to a sustainable aviation fuel blendstock, in Pseudomonas putida. Initially, a combination of heuristics and genome scale metabolic modeling was used to identify 120 genes associated with isoprenol production for CRISPR interference. We then developed a largely automated conversion pipeline to design multiplexed guide RNA arrays, construct strains harboring these arrays, and then assess proteomics-validated isoprenol production in P. putida, which reduced the DBT turnaround time from months using conventional methods to two weeks. By leveraging an active learning model, the Automated Recommendation Tool (ART), we systematically and rapidly identified new experimental designs that improved titer. ART successfully downselected from an initial design space of 2.1 x 108 possible combinations to approximately 300 combinations. After 6 successive DBTL cycles this ultimately yielded several different gRNA combinations with up to 5-fold titer improvement. Our conversion pipeline not only showcases the potential of integrating laboratory automation with machine learning, but also marks a significant advancement toward autonomous strain design.

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ORGANISM(S): Pseudomonas Putida Kt2440

SUBMITTER: Christopher Petzold

LAB HEAD: Christopher Petzold

PROVIDER: PXD063738 | Pride | 2025-09-19

REPOSITORIES: Pride

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Project description:Our group has evaluated the productivity of the alcohol isoprenol (3-methyl-3-buten-1-ol), platform commodity chemical with attractive properties specifically as a diesel fuel blendstock and precursor for the bio-jetfuel DMCO, across a number of microbial hosts. While there are strengths and drawbacks to producing isoprenol in any one microbe, Pseudomonas putida KT2440 has a naturally versatile catabolic profile competent of consuming isoprenol. The pathway for this catabolism was revealed using functional genomics data (RB-TnSeq - Thompson et al) and we speculated that this data could also provide a path to developing an isoprenol biosensor. Refactoring knowledge of the isoprenol catabolism signaling system could provide a readout for intracellular isoprenol, in turn enabling an alternative approach to strain engineering that did not rely on a mechanistic understanding of how a heterologous pathway modulates native metabolism. We define a biosensor as the system where a ligand is recognized by a transcriptional activator, which in turn binds to a cognate DNA sequence driving transcription of a downstream reporter gene. The resulting gene expression linearly increases in response to the initial ligand concentration. There are undoubtedly other biosensor modalities (such as directly converting the ligand into a colored molecule) but the advantage of a biosensor that activates a genetic circuit is that the response can also be integrated back into cellular physiology for high throughput selection with growth based assay. For example, strains can be devised where cell growth is concomitant with the increased production of the ligand. Prokaryotic systems are also advantaged over eukaryotes for biosensor development as the determinants of ribosome translation efficiency are dictated by simple RBS sequences adjacent to the start codon as opposed to complex transcriptional regulators, enabling fine protein level expression tuning while maintaining inducibility.

			Action	DRS
	Carruther_et_al_DBTL2_Proteomics.csv	Csv
	Control_P1-R1.raw	Raw
	Control_P1-R2.raw	Raw
	Control_P1-R3.raw	Raw
	Control_P2-R1.raw	Raw

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Automation of a CRISPRi platform for enhanced isoprenol production in Pseudomonas putida [DBTL2]

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