DOI: 10.5061/dryad.gtht76hzh
title: Data from: Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

description: Advances in genome engineering have improved our ability to perturb
 microbial metabolic networks, yet bioproduction campaigns often struggle
 with parsing complex metabolic datasets to efficiently enhance product
 titers. We address this challenge by coupling laboratory automation with
 machine learning to systematically optimize the production of isoprenol, a
 sustainable aviation fuel (SAF) precursor, in Pseudomonas putida. The
 simultaneous downregulation through CRISPR interference of combinations of
 up to four gene targets, guided by machine learning (ML), permitted us to
 increase isoprenol titer 5-fold in six consecutive DBTL cycles. Moreover,
 ML enabled us to swiftly explore a vast experimental design space of
 800,000 possible combinations by strategically recommending approximately
 400 priority constructs. High-throughput proteomics allowed us to validate
 CRISPRi downregulation and identify biological mechanisms driving
 production increases. Our work demonstrates that ML-driven automated DBTL
 cycles can rapidly enhance titers without specific biological knowledge,
 suggesting that it can be applied to any host, product, or
 pathway. 
description: High-throughput proteomics data were generated to monitor the
 effects of CRISPRi-mediated gene knockdowns on protein expression levels
 across six DBTL cycles (DBTL0-DBTL6). The sample preparation protocol is
 detailed at Protocols.io dx.doi.org/10.17504/protocols.io.6qpvr6xjpvmk/v1. Protein was extracted from <em>P. putida</em> cell pellets using Qiagen P2 Lysis Buffer, precipitated with acetone, and digested with trypsin. Resulting tryptic peptides were analyzed using an Agilent 1290 UHPLC system coupled to a Thermo Scientific Orbitrap Exploris 480 mass spectrometer, employing data-independent acquisition (DIA) mode. The data processing protocol is detailed at Protocols.io dx.doi.org/10.17504/protocols.io.5qpvobk7xl4o/v2. DIA raw data were processed using DIA-NN software (library-free mode) against a database containing the <em>P. putida</em> KT2440 Uniprot proteome, heterologous proteins, and common contaminants. Protein quantification was performed using the Top3 method (Ahrne et al. 2013 DOI:10.1002/pmic.201300135), averaging the signal response of the three most intense tryptic peptides for each protein. Data were filtered to a global false discovery rate (FDR) ≤ 0.01 at both precursor and protein group levels. The generated mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD063733 (DBTL0), PXD063737 (DBTL1), PXD063738 (DBTL2), PXD063740 (DBTL3), PXD063743 (DBTL4), PXD063744 (DBTL5), and PXD063746 (DBTL6). DIA-NN is freely available for download from https://github.com/vdemichev/DiaNN.
description: # Data from: Automation and machine learning drive rapid optimization of
 isoprenol production in Pseudomonas putida Dataset DOI:
 [10.5061/dryad.gtht76hzh](10.5061/dryad.gtht76hzh) ## Description of the
 data and file structure This dataset contains proteomic data key to the
 characterization and analyses of our CRISPRi study. It includes liquid
 chromatographic and mass spectrometric analysis of the proteomic samples
 of strains engineered for isoprenol production. High-throughput proteomics
 allowed us to validate CRISPRi downregulation and identify biological
 mechanisms driving production increases. In DBTL0, we used proteomics to
 determine whether a sgRNA downregulated a target gene based on the
 following three constraints: 1) below 90% of the library mean for the
 target; 2) in the bottom quartile of target expression; and 3) isoprenol
 titer greater than 66.6 mg/L (approximately 40% of control isoprenol
 titer). In DBTL1-6, the proteomics abundance measurements were used to
 identify CRISPRi combinations in which all individual perturbations
 successfully interfered with their target and to confirm dCas9 production.
 The dataset presents diverse proteomic profiles and is ideal for
 comparative studies and bioinformatics analysis. The raw mass spectrometry
 proteomics data have been deposited to the ProteomeXchange Consortium via
 the PRIDE partner repository with the dataset identifiers PXD063733
 (DBTL0), PXD063737 (DBTL1), PXD063738 (DBTL2), PXD063740 (DBTL3),
 PXD063743 (DBTL4), PXD063744 (DBTL5), and PXD063746 (DBTL6). DIA-NN is
 freely available for download from
 [https://github.com/vdemichev/DiaNN](https://github.com/vdemichev/DiaNN).
 ### Files and variables
 **CRISPRi_automation_Pputida_proteomic_Top3_peptide_quantification_method_data.csv** This file contains proteomic data from *Pseudomonas putida* strains engineered to produce isoprenol with The protein accession ID corresponds to proteins identified by the DIA-NN peptide search program, and the quantitative values correspond to the Top3 peptide absolute protein quantification method as detailed in Ahrne et al. 2013 (DOI:10.1002/pmic.201300135), consisting of the average signal response of the three most intense tryptic peptides for each protein. The file also contains the isoprenol titer measured in mg/L for each line. *Description of the columns:* DBTL_Cycle: (int) The Design-Build-Test-Learn cycle number Line_name: (str) The individual line name with replicate ID  Line: (str) The line name with the replicate ID removed Replicate: (str) The replicate ID for the line Isoprenol_titer: (float) The amount of isoprenol produced by the line as measured by GC-FID. Isoprenol_titer_units: (str) Units of the Isoprenol_titer measurements. All other column headers: Uniprot accession IDs  All other table values: (float): Percentage of the proteome for the specific protein as calculated by the Top3 peptide absolute protein quantification method as detailed in Ahrne et al. 2013 (DOI:10.1002/pmic.201300135), consisting of the average signal response of the three most intense tryptic peptides for each protein. When the protein is not detected or is detected with fewer than three peptides the field is left blank (nan). **CRISPRi_automation_Pputida_proteomic_metadata.csv** This file contains the metadata for the proteomic experiments detailed in the CRISPRi_automation_Pputida_proteomic_metadata.csv file. *Description of the columns:* DBTL_Cycle: (int) The cycle number within a Design-Build-Test-Learn cycle framework. Line_name: (str) A descriptive name for a specific experimental line or condition with the replicate ID Line: (str) A numerical or coded identifier for an experimental line or condition. Replicate_ID: (str) The replicate number for a given experimental condition.  Indicates repeated measurements under identical conditions. Culture_volume: (float) The volume of the culture used in the experiment. Culture_format: (str) The format of the culture vessel. Growth_temperature_Celsius: (float) The temperature at which the culture was grown, in degrees Celsius. Shaking_speed_rpm: (int) The speed of shaking (revolutions per minute) used in the culture incubation. Media: (str) The type of growth media used for the culture. Carbon_source: (str) The primary carbon source used in the growth media. Carbon_source_concentration: (float) The concentration of the carbon source in the media. Carbon_source_concentration_units: (str) The units of concentration for the carbon source. Inducer: (str) The type of inducer used to trigger gene expression (if applicable). Inducer_concentration: (float) The concentration of the inducer used. Inducer_concentration_units: (str) The units of concentration for the inducer. Induction_time_point: (float) The time point at which induction was initiated. Induction_time_point_units: (str) The units of time for the induction time point. Assay_type: (str) The type of assay performed on the culture. Assay_time_point: (float) The time point at which the assay was performed. Assay_time_point_units: (str) The units of time for the assay time point. Organism: (str) The organism used in the experiment. Strain_ID: (str) A unique identifier for the specific strain of the organism used. Genes_targeted_for_CRISPRi:(str)A list of genes targeted for CRISPR interference (CRISPRi) for each line in the experiment. ## Access information Other publicly accessible locations of the data: * The raw mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD063733 (DBTL0), PXD063737 (DBTL1), PXD063738 (DBTL2), PXD063740 (DBTL3), PXD063743 (DBTL4), PXD063744 (DBTL5), and PXD063746 (DBTL6).