Data availability
Oligonucleotides, as well as plasmids and strains constructed and used in this work, are described in Supplementary Data 2–4 as well as Supplementary Table 2. Plasmids and strains are available in the public domain of the JBEI Registry (https://public-registry.jbei.org). Source data are provided in the Source Data file. The generated mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD063733 (DBTL0) [https://www.ebi.ac.uk/pride/archive/projects/PXD063733], PXD063737 (DBTL1) [https://www.ebi.ac.uk/pride/archive/projects/PXD063737], PXD063738 (DBTL2) [https://www.ebi.ac.uk/pride/archive/projects/PXD063738], PXD063740 (DBTL3) [https://www.ebi.ac.uk/pride/archive/projects/PXD063740], PXD063743 (DBTL4) [https://www.ebi.ac.uk/pride/archive/projects/PXD063743], PXD063744 (DBTL5) [https://www.ebi.ac.uk/pride/archive/projects/PXD063744], and PXD063746 (DBTL6) [https://www.ebi.ac.uk/pride/archive/projects/PXD063746]65. DIA-NN is freely available for download from https://github.com/vdemichev/DiaNN. All proteomics and GC-FID data were also deposited into a publicly available DRYAD database (https://doi.org/10.5061/dryad.gtht76hzh). Source data are provided with this paper.
Code availability
ART is freely available for non-commercial use by academic institutions and for a small licensing fee for commercial use; the license can be accessed at https://github.com/JBEI/ART. All code required to reproduce this study, including ART, automation workflows, and figure generation, is provided as Jupyter Notebooks and can be accessed at https://github.com/JBEI/Isoprenol_CRISPRi or https://doi.org/10.5281/zenodo.1717868466.
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Acknowledgements
We would like to thank Dr. Thomas Eng, who supplied protocols for gene deletion as well as several plasmids and oligonucleotides used for gene deletions in previous P. putida work. We also appreciate the expertise shared by Dr. Joonhoon Kim, who introduced us to Biocluster in R and provided insights into exploring off-target effects. This work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, through contract DE-AC0205CH11231 between Lawrence Berkeley National Laboratory and the U.S. Department of Energy. All authors were supported on this contract. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The views and opinions expressed by the authors do not necessarily reflect those of the United States Government or any of its agencies. The United States Government and its employees make no warranty, expressed or implied, regarding the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor do they represent that the use of such information would not infringe privately owned rights.
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A.K.S. and J.S. are founders of XLSI Bio. H.G.M. and W.R.G. are scientific advisors to XLSI Bio. The remaining authors declare no competing interests.
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Carruthers, D.N., Kinnunen, P.C., Li, Y. et al. Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida. Nat Commun (2025). https://doi.org/10.1038/s41467-025-66304-8
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DOI: https://doi.org/10.1038/s41467-025-66304-8
